sysuse auto.dta, clear(1978 automobile data)03 - Stata Essentials
econ 490
stata
describe
summarize
loops
help
This notebook dives into a few essentials commands in Stata, including describe, summarize, loops, and help.
Prerequisites
- Understand how to effectively use Stata do-files and know how to generate log files.
Learning Outcomes
- View the characteristics of any dataset using the command
describe. - Use
helpto learn best how to run commands. - Understand the Stata command syntax using the command
summarize. - Create loops using the commands
for,while,forvaluesandforeach.
3.1 Describing Your Data
Let’s start by opening a dataset that was provided when we installed Stata onto our computers. We will soon move on to importing our own data, but this Stata data set will help get us started. This is a data set on automobiles and their characteristics. We can install this dataset by running the command in the cell below:
We can begin by checking the characteristics of the data set we have just downloaded. The command describe allows us to see the number of observations, the number of variables, a list of variable names and descriptions, and the variable types and labels of that data set.
describe
Contains data from D:\Stata/ado\base/a/auto.dta
Observations: 74 1978 automobile data
Variables: 12 13 Apr 2024 17:45
(_dta has notes)
-------------------------------------------------------------------------------
Variable Storage Display Value
name type format label Variable label
-------------------------------------------------------------------------------
make str18 %-18s Make and model
price int %8.0gc Price
mpg int %8.0g Mileage (mpg)
rep78 int %8.0g Repair record 1978
headroom float %6.1f Headroom (in.)
trunk int %8.0g Trunk space (cu. ft.)
weight int %8.0gc Weight (lbs.)
length int %8.0g Length (in.)
turn int %8.0g Turn circle (ft.)
displacement int %8.0g Displacement (cu. in.)
gear_ratio float %6.2f Gear ratio
foreign byte %8.0g origin Car origin
-------------------------------------------------------------------------------
Sorted by: foreignNotice that this data set consists of 12 variables and 74 observations. We can see that the first variable is named make, which indicates the make and model of the vehicle. We can also see that the variable make is a string variable (made up of text). Other variables in this data set are numeric. For example, the variable mpg indicates the vehicle’s mileage (miles per gallon) as an integer. The variable foreign is also numeric, and it only takes the values 0 or 1, indicating whether the car is foreign or domestically made; this is a dummy variable.
3.2 Introduction to Stata Command Syntax
3.2.1 Using HELP to understand commands
Stata has a help manual installed in the program which provides documentation for all Stata published commands. This information can be reached by typing the command help and then the name of the command we need extra information about.
Let’s try to see what extra information Stata provides by using the help command with the summarize command. summarize gives us the basic statistics from any variable(s) in the data set, such as the variables we have discussed above, but what else can it do? To see the extra information that is available by using summarize, let’s run the command below:
help summarize
[R] summarize -- Summary statistics
(View complete PDF manual entry)
Syntax
------
summarize [varlist] [if] [in] [weight] [, options]
options Description
-------------------------------------------------------------------------
Main
detail display additional statistics
meanonly suppress the display; calculate only the mean;
programmer's option
format use variable's display format
separator(#) draw separator line after every # variables; default is
separator(5)
display_options control spacing, line width, and base and empty cells
-------------------------------------------------------------------------
varlist may contain factor variables; see fvvarlist.
varlist may contain time-series operators; see tsvarlist.
by, collect, rolling, and statsby are allowed; see prefix.
aweights, fweights, and iweights are allowed. However, iweights may not
be used with the detail option; see weight.
Menu
----
Statistics > Summaries, tables, and tests > Summary and descriptive
statistics > Summary statistics
Description
-----------
summarize calculates and displays a variety of univariate summary
statistics. If no varlist is specified, summary statistics are
calculated for all the variables in the dataset.
Links to PDF documentation
--------------------------
Quick start
Remarks and examples
Methods and formulas
The above sections are not included in this help file.
Options
-------
+------+
----+ Main +-------------------------------------------------------------
detail produces additional statistics, including skewness, kurtosis, the
four smallest and four largest values, and various percentiles.
meanonly, which is allowed only when detail is not specified, suppresses
the display of results and calculation of the variance. Ado-file
writers will find this useful for fast calls.
format requests that the summary statistics be displayed using the
display formats associated with the variables rather than the default
g display format; see [D] format.
separator(#) specifies how often to insert separation lines into the
output. The default is separator(5), meaning that a line is drawn
after every five variables. separator(10) would draw a line after
every 10 variables. separator(0) suppresses the separation line.
display_options: vsquish, noemptycells, baselevels, allbaselevels,
nofvlabel, fvwrap(#), and fvwrapon(style); see [R] Estimation
options.
Examples
--------
. sysuse auto
. summarize
. summarize mpg weight
. summarize mpg weight if foreign
. summarize mpg weight if foreign, detail
. summarize i.rep78
Video example
-------------
Descriptive statistics in Stata
Stored results
--------------
summarize stores the following in r():
Scalars
r(N) number of observations
r(mean) mean
r(skewness) skewness (detail only)
r(min) minimum
r(max) maximum
r(sum_w) sum of the weights
r(p1) 1st percentile (detail only)
r(p5) 5th percentile (detail only)
r(p10) 10th percentile (detail only)
r(p25) 25th percentile (detail only)
r(p50) 50th percentile (detail only)
r(p75) 75th percentile (detail only)
r(p90) 90th percentile (detail only)
r(p95) 95th percentile (detail only)
r(p99) 99th percentile (detail only)
r(Var) variance
r(kurtosis) kurtosis (detail only)
r(sum) sum of variable
r(sd) standard deviationWe need to run this command directly into the Stata console on our computer in order to able to see all of the information provided by help. Running this command now will allow us to see that output directly.
When we do, we can see that the first 1-2 letters of the command are often underlined. This underlining indicates the shortest permitted abbreviation for a command (or option).
For example, if we type help rename, we can see that rename can be abbreviated as ren, rena, or renam, or it can be spelled out in its entirety.
Other examples are, generate, append, rotate, run.
If there is no underline, then no abbreviation is allowed. For example, the command replace cannot be abbreviated. The reason for this is that Stata doesn’t want us to accidentally make changes to our data by replacing the information in the variable.
We can write the summarize command with its shortest abbreviation su or a longer abbreviation such as sum.
Also, in the Stata help output we can see that some words are written in blue and are encased within square brackets. We will talk more about these options below, but in Stata we can directly click on those links for more information from help.
Finally, help provides a list of the available options for a command. In the case of summarize, these options allow us to display extra information for a variable. We will learn more about this below in section 3.2.4.
3.2.2 Imposing IF conditions
When the syntax of the command allows for [if], we can run the command on a subset of the data that satisfies any condition we choose. Here is the list of conditional operators available to us:
- Equal: ==
- Greater than and less than: > and <
- Greater than or equal and less than or equal: >= and <=
- Not Equal: !=
We can also compound different conditions using the list of logical operators:
- And: &
- Or: |
- Not: ! or ~
Let’s look at an example which applies this new knowledge: summarizing the variable price when the make of the car is domestic (i.e. not foreign):
su price if foreign == 0
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 52 6072.423 3097.104 3291 15906Let’s do this again, but now we will impose the additional condition that the mileage must be less than 25.
su price if foreign == 0 & mpg < 25
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 44 6354.568 3273.345 3291 15906Maybe we want to restrict to a particular list of values. Here we can write out all of the conditions using the “or” operator, or we can simply make use of the option inlist():
su price if mpg == 10 | mpg == 15 | mpg == 25 | mpg == 40
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 7 6507.857 1838.25 4482 9735This works exactly the same way as this command:
su price if inlist(mpg,10,15,25,40)
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 7 6507.857 1838.25 4482 9735Maybe we want to restrict to values in a particular range. Here we can use the conditional operators, or we can make use of the option inrange():
su price if mpg >= 5 & mpg <= 25
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 60 6577.083 3117.013 3291 15906Notice the output returned by the code below is equal to the previous cell:
su price if inrange(mpg,5,25)
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 60 6577.083 3117.013 3291 15906There might be variables for which there is no information recorded for some observations. For example, when we summarize our automobile data we will see that there are 74 observations for most variables, but that the variable rep78 has only 69 observations - for five observations there is no repair record indicated in the data set.
su price rep78
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 74 6165.257 2949.496 3291 15906
rep78 | 69 3.405797 .9899323 1 5If, for some reason, we only want to consider observations without missing values, we can use the option !missing() which combines the command missing() with the negative conditional operator “!”. For example, the command below says to summarize the variable price for all observations for which rep78 is NOT missing.
su price if !missing(rep78)
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 69 6146.043 2912.44 3291 15906This command can also be written using the conditional operator since missing numeric variables are indicated by a “.”. This is shown below:
su price if rep78 != .
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 69 6146.043 2912.44 3291 15906Notice that in both cases there are only 69 observations.
If we wanted to do this with missing string variables, we could indicate those with ““.
3.2.3 Imposing IN conditions
We can also subset the data by using the observation number. The example below summarizes the data in observations 1 through 10.
su price in 1/10
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 10 5517.4 2063.518 3799 10372But be careful! This type of condition is generally not recommended because it depends on how the data is ordered.
To see this, let’s sort the observations in ascending order by running the command sort:
sort price
su price in 1/10
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 10 3726.5 245.9007 3291 3984We can see that the result changes because the observations 1 through 10 in the data are now different.
Always avoid using in whenever you can. Try to use if instead!
3.2.4 Command options
When we used the help command, we saw that we can introduce some optional arguments after a comma. In the case of the summarize command, we were shown the following options: detail, meanonly, format and separator(#).
If we want additional statistics apart from the mean, standard deviation, min, and max values, we can use the option detail or just d for short.
su price, d
Price
-------------------------------------------------------------
Percentiles Smallest
1% 3291 3291
5% 3748 3299
10% 3895 3667 Obs 74
25% 4195 3748 Sum of wgt. 74
50% 5006.5 Mean 6165.257
Largest Std. dev. 2949.496
75% 6342 13466
90% 11385 13594 Variance 8699526
95% 13466 14500 Skewness 1.653434
99% 15906 15906 Kurtosis 4.8191883.3 Using Loops
Much like any other programming language, there are for and while loops that we can use to iterate through many times. In particular, the for loops are also sub-divided into forvalues (which iterate across a range of numbers) and foreach (which iterate across a list of names).
It is very common that these loops create a local scope (i.e. the iteration labels only exist within a loop). A local in Stata is a special variable that we create ourselves that temporarily stores information. We’ll discuss locals in the next module, but consider this simple example in which the letter “i” is used as a place holder for the number 95 – it is a local.
For a better understanding of locals and globals, please visit Module 4.
local i = 95
display `i'95We can also create locals that are strings rather than numeric in type. Consider this example:
local course = "ECON 490"
display "`course'"ECON 490We can store anything inside a local. When we want to use that information, we include the local encased in a backtick (`) and apostrophe (’).
local course = "ECON 490"
display "I am enrolled in `course' and hope my grade will be `i'%!"I am enrolled in ECON 490 and hope my grade will be 95%!3.3.1 Creating loops Using forvalues
Whenever we want to iterate across a range of values defined as forvalues = local_var_name = min_value(steps)max_value, we can write the command below. Here we are iterating from 1 to 10 in increments of 1.
forvalues counter=1(1)10{
*Notice that now counter is a local variable
display `counter'
}1
2
3
4
5
6
7
8
9
10Notice that the open brace { needs to be on the same line as the for command, with no comments after it. Similarly, the closing brace } needs to be on its own line.
Experiment below with the command above by changing the increments and min or max values. See what your code outputs.
/*
forvalues counter=???(???)???{
display `counter'
}
*/ 3.3.2 Creating loops using foreach
Whenever we want to iterate across a list of names, we can use the foreach command below. This asks Stata to summarize for a list of variables (in this example, mpg and price).
The syntax for foreach is similar to that of forvalues: foreach local_var_name in "list of variables". Here, we are asking Stata to perform the summarize command on two variables (mpg and price):
foreach name in "mpg" "price"{
summarize `name'
}
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
mpg | 74 21.2973 5.785503 12 41
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 74 6165.257 2949.496 3291 15906We can have a list stored in a local variable as well. Here, we are storing a list, which includes two variable names (mpg and price) in a local called namelist. Then, using foreach, we summarize name which runs through the list we created above, called namelist.
local namelist "mpg price"
foreach name in `namelist'{
summarize `name'
}
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
mpg | 74 21.2973 5.785503 12 41
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
price | 74 6165.257 2949.496 3291 159063.3.3 Writing loops with conitions using while
Whenever we want to iterate until a condition is met, we can write the command below. The condition here is simply “while counter is less than 5”.
local counter = 1
while `counter'<5{
display `counter'
local counter = `counter'+1
}1
2
3
43.4 Errors
A common occurrence while working with Stata is encountering various errors. Whenever an error occurs, the program will stop executing and an error message will pop-up. Most commonly occuring errors can be attributed to syntax issues, so we should always verify our code before execution. Below we have provided 3 common errors that may pop up.
capture noisily summarize hellovariable hello not foundWe must always verify that the variable you use for a command exists and that you are using its correct spelling. Stata alerts you when you try to execute a command with a non-existing variable.
capture noisily su price if 5 =< mpg =< 25<mpg invalid nameIn this example, the error is due to the use of invalid conditional operators. To make use of the greater than or equal to operator, you must use the symbol (mpg >= ) and to use the less than or equal to operator, you use the symbol (mpg <= ).
local word = 95
display "I am enrolled in `course' and hope my grade will be 'word'%!" // this is incorrect
display "I am enrolled in `course' and hope my grade will be `word'%!" // this is correctI am enrolled in ECON 490 and hope my grade will be 'word'%!
I am enrolled in ECON 490 and hope my grade will be 95%!The number 95 does not display in the string due to the wrong punctuation marks being used to enclose the local. We make the error of using two apostraphes instead of a backtick (`) and an apostrophe (’).
3.5 Wrap Up
In this module, we looked at the way Stata commands function and how their syntax works. In general, many Stata commands will follow the folllowing structure:
name_of_command [varlist] [if] [in] [weight] [, options]At this point, you should feel more comfortable reading a documentation file for a Stata command. The question that remains is how to find new commands!
You are encouraged to search for commands using the command search. For example, if you are interested in running a regression you can write:
search regress
Search of official help files, FAQs, Examples, and Stata Journals
-----------------------------------------------------------------
[U] Chapter 26 . . . . Working with categorical data and factor variables
(help generate, fvvarlist)
[U] Chapter 27 . . . . . . . . . . Overview of Stata estimation commands
(help u_estimation)
[R] regress . . . . . . . . . . . . . . . . . . . . . . Linear regression
(help regress)
[R] regress postestimation . . . . . . . Postestimation tools for regress
(help regress postestimation)
[R] regress postestimation plots . . . . Postestimation plots for regress
(help regress postestimation plots)
[R] regress postestimation time series Postest. regress with time series
(help regress postestimationts)
[R] logistic . . . . . . . . . Logistic regression, reporting odds ratios
(help logistic)
[R] logistic postestimation . . . . . . Postestimation tools for logistic
(help logistic postestimation)
[R] probit . . . . . . . . . . . . . . . . . . . . . . Probit regression
(help probit)
[R] poisson . . . . . . . . . . . . . . . . . . . . . . Poisson regression
(help poisson)
[R] poisson postestimation . . . . . . . Postestimation tools for poisson
(help poisson postestimation)
[R] anova . . . . . . . . . . . . . . Analysis of variance and covariance
(help anova)
[R] areg . . . . . . . . Linear regression with many indicator variables
(help areg)
[R] betareg . . . . . . . . . . . . . . . . . . . . . . . Beta regression
(help betareg)
[R] betareg postestimation . . . . . . . Postestimation tools for betareg
(help betareg postestimation)
[R] binreg . Generalized linear models: Extensions to the binomial family
(help binreg)
[R] biprobit . . . . . . . . . . . . . . . . Bivariate probit regression
(help biprobit)
[R] biprobit postestimation . . . . . . Postestimation tools for biprobit
(help biprobit postestimation)
[R] boxcox . . . . . . . . . . . . . . . . . . Box-Cox regression models
(help boxcox)
[R] cfprobit . . . . . . . . . . . . . Control-function probit regression
(help cfprobit)
[R] cfprobit postestimation . . . . . . Postestimation tools for cfprobit
(help cfprobit postestimation)
[R] cfregress . . . . . . . . . . . . . Control-function linear regression
(help cfregress)
[R] cfregress postestimation . . . . . Postestimation tools for cfregress
(help cfregress postestimation)
[R] churdle . . . . . . . . . . . . . . . . . . . Cragg hurdle regression
(help churdle)
[R] churdle postestimation . . . . . . . Postestimation tools for churdle
(help churdle postestimation)
[R] clogit . . . . . . . Conditional (fixed-effects) logistic regression
(help clogit)
[R] cloglog . . . . . . . . . . . . . . . Complementary log-log regression
(help cloglog)
[R] cnsreg . . . . . . . . . . . . . . . . Constrained linear regression
(help cnsreg)
[R] cnsreg postestimation . . . . . . . . Postestimation tools for cnsreg
(help cnsreg postestimation)
[R] cpoisson . . . . . . . . . . . . . . . . Censored Poisson regression
(help cpoisson)
[R] cpoisson postestimation . . . . . . Postestimation tools for cpoisson
(help cpoisson postestimation)
[R] eivreg . . . . . . . . . . . . . . . . Errors-in-variables regression
(help eivreg)
[R] eivreg postestimation . . . . . . . . Postestimation tools for eivreg
(help eivreg postestimation)
[R] estat classification . . . . . . Classification statistics and table
(help r estat classification)
[R] estat gof . . . . . . Pearson or Hosmer-Lemeshow goodness-of-fit test
(help logistic estat gof)
[R] exlogistic . . . . . . . . . . . . . . . . Exact logistic regression
(help exlogistic)
[R] exlogistic postestimation . . . . Postestimation tools for exlogistic
(help exlogistic postestimation)
[R] expoisson . . . . . . . . . . . . . . . . . . Exact Poisson regression
(help expoisson)
[R] expoisson postestimation . . . . . Postestimation tools for expoisson
(help expoisson postestimation)
[R] fp . . . . . . . . . . . . . . . . . Fractional polynomial regression
(help fp)
[R] fp postestimation . . . . . . . . . . . . Postestimation tools for fp
(help fp postestimation)
[R] fracreg . . . . . . . . . . . . . . . . Fractional response regression
(help fracreg)
[R] fracreg postestimation . . . . . . . Postestimation tools for fracreg
(help fracreg postestimation)
[R] frontier . . . . . . . . . . . . . . . . . Stochastic frontier models
(help frontier)
[R] glm . . . . . . . . . . . . . . . . . . . . Generalized linear models
(help glm)
[R] gmm . . . . . . . . . . . . . Generalized method of moments estimation
(help gmm)
[R] heckman . . . . . . . . . . . . . . . . . . . Heckman selection model
(help heckman)
[R] heckoprobit . . . . . . . . Ordered probit model with sample selection
(help heckoprobit)
[R] heckpoisson . . . Poisson regression with endogenous sample selection
(help heckpoisson)
[R] heckpoisson postestimation . . . Postestimation tools for heckpoisson
(help heckpoisson postestimation)
[R] hetprobit . . . . . . . . . . . . . . . . Heteroskedastic probit model
(help hetprobit)
[R] hetregress . . . . . . . . . . . . Heteroskedastic linear regression
(help hetregress)
[R] hetregress postestimation . . . . Postestimation tools for hetregress
(help hetregress postestimation)
[R] intreg . . . . . . . . . . . . . . . . . . . . . Interval regression
(help intreg)
[R] intreg postestimation . . . . . . . . Postestimation tools for intreg
(help intreg postestimation)
[R] ivfprobit postestimation . . . . . Postestimation tools for ivfprobit
(help ivfprobit postestimation)
[R] ivpoisson . . . . Poisson model with continuous endogenous covariates
(help ivpoisson)
[R] ivpoisson postestimation . . . . . Postestimation tools for ivpoisson
(help ivpoisson postestimation)
[R] ivprobit . . . . . Probit model with continuous endogenous covariates
(help ivprobit)
[R] ivprobit postestimation . . . . . . Postestimation tools for ivprobit
(help ivprobit postestimation)
[R] ivqregress . . . . . . . . Instrumental-variables quantile regression
(help ivqregress)
[R] ivqregress postestimation . . . . Postestimation tools for ivqregress
(help ivqregress postestimation)
[R] ivregress . . . . . Single-equation instrumental-variables regression
(help ivregress)
[R] ivregress postestimation . . . . . Postestimation tools for ivregress
(help ivregress postestimation)
[R] ivtobit . . . . . . Tobit model with continuous endogenous covariates
(help ivtobit)
[R] ivtobit postestimation . . . . . . . Postestimation tools for ivtobit
(help ivtobit postestimation)
[R] linktest . . . . . Specification link test for single-equation models
(help linktest)
[R] logit . . . . . . . . . . Logistic regression, reporting coefficients
(help logit)
[R] logit postestimation . . . . . . . . . Postestimation tools for logit
(help logit postestimation)
[R] lpoly . . . . . . . . . . . Kernel-weighted local polynomial smoothing
(help lpoly)
[R] lroc . . . . . . . . Compute area under ROC curve and graph the curve
(help lroc)
[R] lsens . . Graph sensitivity and specificity versus probability cutoff
(help lsens)
[R] mfp . . . . . . . . . . . . Multivariable fractional polynomial models
(help mfp)
[R] mlogit . . . . . . . . . Multinomial (polytomous) logistic regression
(help mlogit)
[R] mlogit postestimation . . . . . . . . Postestimation tools for mlogit
(help mlogit postestimation)
[R] mprobit . . . . . . . . . . . . . . . . Multinomial probit regression
(help mprobit)
[R] mprobit postestimation . . . . . . . Postestimation tools for mprobit
(help mprobit postestimation)
[R] nbreg . . . . . . . . . . . . . . . . . . Negative binomial regression
(help nbreg, gnbreg)
[R] nbreg postestimation . . . Postestimation tools for nbreg and gnbreg
(help nbreg postestimation)
[R] nestreg . . . . . . . . . . . . . . . . . . . Nested model statistics
(help nestreg)
[R] nl . . . . . . . . . . . . . . . . Nonlinear least-squares estimation
(help nl)
[R] nlsur . . . . . . . . . . Estimation of nonlinear system of equations
(help nlsur)
[R] npregress kernel . . . . . . . . . . Nonparametric kernel regression
(help npregress kernel)
[R] npregress kernel postestimation . . Postestimation tools for npregress
(help npregress kernel postestimation)
[R] npregress series . . . . . . . . . . Nonparametric series regression
(help npregress series)
[R] npregress series postestimation . . Postestimation tools for npregress
(help npregress series postestimation)
[R] ologit . . . . . . . . . . . . . . . . . Ordered logistic regression
(help ologit)
[R] ologit postestimation . . . . . . . . Postestimation tools for ologit
(help ologit postestimation)
[R] oprobit . . . . . . . . . . . . . . . . . . Ordered probit regression
(help oprobit)
[R] oprobit postestimation . . . . . . . Postestimation tools for oprobit
(help oprobit postestimation)
[R] orthog . . Orthogonalize variables and compute orthogonal polynomials
(help orthog, orthpoly)
[R] qreg . . . . . . . . . . . . . . . . . . . . . . Quantile regression
(help qreg)
[R] qreg postestimation . Postest. tools for qreg, iqreg, sqreg, & bsqreg
(help qreg postestimation)
[R] reg3 . . Three-stage estimation for systems of simultaneous equations
(help reg3)
[R] roc . . . . . . . . . Receiver operating characteristic (ROC) analysis
(help roc)
[R] rocreg . . . . . . . . . Parametric and nonparametric ROC regression
(help rocreg)
[R] rocreg postestimation . . . . . . . . Postestimation tools for rocreg
(help rocreg postestimation)
[R] rreg . . . . . . . . . . . . . . . . . . . . . . . Robust regression
(help rreg)
[R] rreg postestimation . . . . . . . . . . Postestimation tools for rreg
(help rreg postestimation)
[R] scobit . . . . . . . . . . . . . . . . . . Skewed logistic regression
(help scobit)
[R] scobit postestimation . . . . . . . . Postestimation tools for scobit
(help scobit postestimation)
[R] slogit . . . . . . . . . . . . . . . . Stereotype logistic regression
(help slogit)
[R] stepwise . . . . . . . . . . . . . . . . . . . . Stepwise estimation
(help stepwise)
[R] sureg . . . . . . . . . . . . Zellner's seemingly unrelated regression
(help sureg)
[R] sureg postestimation . . . . . . . . . Postestimation tools for sureg
(help sureg postestimation)
[R] table regression . . . . . . . . . . . . Table of regression results
(help table regression)
[R] table . . . . . . Table of frequencies, summaries, and command results
(help table)
[R] tnbreg . . . . . . . . . . . . Truncated negative binomial regression
(help tnbreg)
[R] tnbreg postestimation . . . . . . . . Postestimation tools for tnbreg
(help tnbreg postestimation)
[R] tobit . . . . . . . . . . . . . . . . . . . . . . . . Tobit regression
(help tobit)
[R] tobit postestimation . . . . . . . . . Postestimation tools for tobit
(help tobit postestimation)
[R] tpoisson . . . . . . . . . . . . . . . . Truncated Poisson regression
(help tpoisson)
[R] tpoisson postestimation . . . . . . Postestimation tools for tpoisson
(help tpoisson postestimation)
[R] truncreg . . . . . . . . . . . . . . . . . . . . Truncated regression
(help truncreg)
[R] truncreg postestimation . . . . . . Postestimation tools for truncreg
(help truncreg postestimation)
[R] vwls . . . . . . . . . . . . . . . . Variance-weighted least squares
(help vwls)
[R] xi . . . . . . . . . . . . . . . . . . . . . . Interaction expansion
(help xi)
[R] zinb . . . . . . . . . . . Zero-inflated negative binomial regression
(help zinb)
[R] zinb postestimation . . . . . . . . . . Postestimation tools for zinb
(help zinb postestimation)
[R] ziologit . . . . . . . . . . . Zero-inflated ordered logit regression
(help ziologit)
[R] ziologit postestimation . . . . . . Postestimation tools for ziologit
(help ziologit postestimation)
[R] zioprobit . . . . . . . . . . Zero-inflated ordered probit regression
(help zioprobit)
[R] zioprobit postestimation . . . . . Postestimation tools for zioprobit
(help zioprobit postestimation)
[R] zip . . . . . . . . . . . . . . . . . Zero-inflated Poisson regression
(help zip)
[R] zip postestimation . . . . . . . . . . . Postestimation tools for zip
(help zip postestimation)
[D] statsby . . . . . . Collect statistics for a command across a by list
(help statsby)
[G-2] graph twoway . . . . . . . . . . . . . . . . . . . . . Two-way graphs
(help graph twoway)
[G-2] graph twoway fpfit . . Two-way fractional-polynomial prediction plots
(help twoway fpfit)
[G-2] graph twoway fpfitci . Two-way fract.-polynomial pred. plots with CIs
(help twoway fpfitci)
[G-2] graph twoway lfit . . . . . . . . . . Two-way linear prediction plots
(help twoway lfit)
[G-2] graph twoway lfitci . . . . . Two-way linear prediction plots with CIs
(help twoway lfitci)
[G-2] graph twoway qfit . . . . . . . . . Two-way quadratic prediction plots
(help twoway qfit)
[G-2] graph twoway qfitci . . . Two-way quadratic prediction plots with CIs
(help twoway qfitci)
[TABLES] Example 5 Table of regression coefficients and confidence intervals
(help tables intro)
[TABLES] Example 6 . . . . . . . . . . . . Table comparing regression results
(help tables intro)
[TABLES] Example 7 . . . . . . Table of regression results using survey data
(help tables intro)
[BAYES] bayesmh . . . Bayesian regression using Metropolis-Hastings algorithm
(help bayesmh)
[BAYES] bayesmh evaluators . . . . . . . User-defined evaluators with bayesmh
(help bayesmh evaluators)
[BAYES] bayesselect . . . . Bayesian variable selection for linear regression
(help bayesselect)
[BAYES] bayes: regress . . . . . . . . . . . . . . Bayesian linear regression
(help bayes: regress)
[BAYES] bayes: logistic . Bayesian logistic regression, reporting odds ratios
(help bayes: logistic)
[BAYES] bayes: probit . . . . . . . . . . . . . . . Bayesian probit regression
(help bayes: probit)
[BAYES] bayes: poisson . . . . . . . . . . . . . Bayesian Poisson regression
(help bayes: poisson)
[BAYES] bayes: betareg . . . . . . . . . . . . . . . Bayesian beta regression
(help bayes: betareg)
[BAYES] bayes: binreg . . . . Bayesian glms: Extensions to the binomial family
(help bayes: binreg)
[BAYES] bayes: biprobit . . . . . . . . . Bayesian bivariate probit regression
(help bayes: biprobit)
[BAYES] bayes: clogit . . . . . . . . Bayesian conditional logistic regression
(help bayes: clogit)
[BAYES] bayes: cloglog . . . . . . Bayesian complementary log-log regression
(help bayes: cloglog)
[BAYES] bayes: fracreg . . . . . . . Bayesian fractional response regression
(help bayes: fracreg)
[BAYES] bayes: glm . . . . . . . . . . . . Bayesian generalized linear models
(help bayes: glm)
[BAYES] bayes: gnbreg . . . Bayesian generalized negative binomial regression
(help bayes: gnbreg)
[BAYES] bayes: heckman . . . . . . . . . . . Bayesian Heckman selection model
(help bayes: heckman)
[BAYES] bayes: heckoprobit . . Bayesian ordered probit model with sample sel.
(help bayes: heckoprobit)
[BAYES] bayes: hetoprobit . Bayesian heteroskedastic ordered probit regression
(help bayes: hetoprobit)
[BAYES] bayes: hetprobit . . . . . Bayesian heteroskedastic probit regression
(help bayes: hetprobit)
[BAYES] bayes: hetregress . . . . . Bayesian heteroskedastic linear regression
(help bayes: hetregress)
[BAYES] bayes: intreg . . . . . . . . . . . . . . Bayesian interval regression
(help bayes: intreg)
[BAYES] bayes: logit . . Bayesian logistic regression, reporting coefficients
(help bayes: logit)
[BAYES] bayes: mecloglog . . . Bayesian multilevel complementary log-log reg.
(help bayes: mecloglog)
[BAYES] bayes: meglm . . . . . . Bayesian multilevel generalized linear model
(help bayes: meglm)
[BAYES] bayes: meintreg . . . . . . . Bayesian multilevel interval regression
(help bayes: meintreg)
[BAYES] bayes: melogit . . . . . . . Bayesian multilevel logistic regression
(help bayes: melogit)
[BAYES] bayes: menbreg . . . Bayesian multilevel negative binomial regression
(help bayes: menbreg)
[BAYES] bayes: meologit . . . Bayesian multilevel ordered logistic regression
(help bayes: meologit)
[BAYES] bayes: meoprobit . . . Bayesian multilevel ordered probit regression
(help bayes: meoprobit)
[BAYES] bayes: mepoisson . . . . . . . Bayesian multilevel Poisson regression
(help bayes: mepoisson)
[BAYES] bayes: meprobit . . . . . . . . Bayesian multilevel probit regression
(help bayes: meprobit)
[BAYES] bayes: metobit . . . . . . . . . Bayesian multilevel tobit regression
(help bayes: metobit)
[BAYES] bayes: mixed . . . . . . . . . Bayesian multilevel linear regression
(help bayes: mixed)
[BAYES] bayes: mlogit . . . . . . . . Bayesian multinomial logistic regression
(help bayes: mlogit)
[BAYES] bayes: mprobit . . . . . . . . Bayesian multinomial probit regression
(help bayes: mprobit)
[BAYES] bayes: mvreg . . . . . . . . . . . . Bayesian multivariate regression
(help bayes: mvreg)
[BAYES] bayes: nbreg . . . . . . . . . Bayesian negative binomial regression
(help bayes: nbreg)
[BAYES] bayes: ologit . . . . . . . . . . Bayesian ordered logistic regression
(help bayes: ologit)
[BAYES] bayes: oprobit . . . . . . . . . . Bayesian ordered probit regression
(help bayes: oprobit)
[BAYES] bayes: qreg . . . . . . . . . . . . . . . Bayesian quantile regression
(help bayes: qreg)
[BAYES] bayes: streg . . . . . . . . . . Bayesian parametric survival models
(help bayes: streg)
[BAYES] bayes: tnbreg . . . . Bayesian truncated negative binomial regression
(help bayes: tnbreg)
[BAYES] bayes: tobit . . . . . . . . . . . . . . . Bayesian tobit regression
(help bayes: tobit)
[BAYES] bayes: tpoisson . . . . . . . . Bayesian truncated Poisson regression
(help bayes: tpoisson)
[BAYES] bayes: truncreg . . . . . . . . . . . . Bayesian truncated regression
(help bayes: truncreg)
[BAYES] bayes: zinb . . . Bayesian zero-inflated negative binomial regression
(help bayes: zinb)
[BAYES] bayes: ziologit . . . Bayesian zero-inflated ordered logit regression
(help bayes: ziologit)
[BAYES] bayes: zioprobit . . Bayesian zero-inflated ordered probit regression
(help bayes: zioprobit)
[BAYES] bayes: zip . . . . . . . . Bayesian zero-inflated Poisson regression
(help bayes: zip)
[BMA] bmaregress . . . . . . Bayesian model averaging for linear regression
(help bmaregress)
[BMA] bmacoefsample . . . . . . Posterior samples of regression coefficients
(help bmacoefsample)
[BMA] bmagraph . Graphical summary for models and predictors after BMA reg.
(help bmagraph)
[BMA] bmagraph coefdensity . Regression coeff. density plots after BMA reg.
(help bmagraph coefdensity)
[BMA] bmagraph msize . . Model-size distribution plots after BMA regression
(help bmagraph msize)
[BMA] bmagraph pmp . . . . . . Model-probability plots after BMA regression
(help bmagraph pmp)
[BMA] bmagraph varmap . . . . . Variable-inclusion map after BMA regression
(help bmagraph varmap)
[BMA] bmapredict . . . . . . . . . . . . . Predictions after BMA regression
(help bmapredict)
[BMA] bmastats . . . Summary for models and predictors after BMA regression
(help bmastats)
[BMA] bmastats jointness . Jointness measures for predictors after BMA reg.
(help bmastats jointness)
[BMA] bmastats lps . . . . . . . Log predictive-score after BMA regression
(help bmastats lps)
[BMA] bmastats models . Model & variable-inclusion summaries after BMA reg.
(help bmastats models)
[BMA] bmastats msize . . . . . . . Model-size summary after BMA regression
(help bmastats msize)
[BMA] bmastats pip . Posterior inclusion probabilities after BMA regression
(help bmastats pip)
[CAUSAL] cate postestimation . . . . . . . . . Postestimation tools for cate
(help cate postestimation)
[CAUSAL] eteffects . . . . . . . . . Endogenous treatment-effects estimation
(help eteffects)
[CAUSAL] etpoisson . . . Poisson regression with endogenous treatment effects
(help etpoisson)
[CAUSAL] etpoisson postestimation . . . . . Postestimation tools for etpoisson
(help etpoisson postestimation)
[CAUSAL] etregress . . . Linear regression with endogenous treatment effects
(help etregress)
[CAUSAL] etregress postestimation . . . . . Postestimation tools for etregress
(help etregress postestimation)
[CAUSAL] stteffects intro . Intro. treatment effects for obs. surv.-time data
(help stteffects intro)
[CAUSAL] stteffects ipwra . Survival-time inverse-prob.-weighted reg. adjust.
(help stteffects ipwra)
[CAUSAL] stteffects ra . . . . . . . . . Survival-time regression adjustment
(help stteffects ra)
[CAUSAL] stteffects wra . . . . . Survival-time weighted regression adjustment
(help stteffects wra)
[CAUSAL] teffects intro . . Intro. to treatment effects for observational data
(help teffects intro)
[CAUSAL] teffects intro advanced . Advanced introduction to treatment effects
(help teffects)
[CAUSAL] teffects ipwra . . Inverse-probability-weighted regression adjustment
(help teffects ipwra)
[CAUSAL] teffects ra . . . . . . . . . . . . . . . . . Regression adjustment
(help teffects ra)
[CM] Intro 5 . . . . . . . . . . . . . . . . . Models for discrete choices
[CM] Intro 6 . . . . . . . . . . . . . Models for rank-ordered alternatives
[CM] cmclogit . . . . . . . . Conditional logit (McFadden's) choice model
(help cmclogit)
[CM] cmmixlogit . . . . . . . . . . . . . . . . . Mixed logit choice model
(help cmmixlogit)
[CM] cmmixlogit postestimation . . . . Postestimation tools for cmmixlogit
(help cmmixlogit postestimation)
[CM] cmmprobit . . . . . . . . . . . . . . Multinomial probit choice model
(help cmmprobit)
[CM] cmrologit . . . . . . . . . . . . . . Rank-ordered logit choice model
(help cmrologit)
[CM] cmrologit postestimation . . . . . Postestimation tools for cmrologit
(help cmrologit postestimation)
[CM] cmroprobit . . . . . . . . . . . . . Rank-ordered probit choice model
(help cmroprobit)
[CM] cmxtmixlogit . . . . . . . . . . Panel-data mixed logit choice model
(help cmxtmixlogit)
[CM] nlogit . . . . . . . . . . . . . . . . . . . Nested logit regression
(help nlogit)
[CM] nlogit postestimation . . . . . . . . Postestimation tools for nlogit
(help nlogit postestimation)
[ERM] Intro . . . . . . . Introduction to extended regression models manual
(help erm intro)
[ERM] Intro 1 . . . . . . . . . . . . . An introduction to the ERM commands
(help erm intro)
[ERM] Intro 2 . . . . . . . . . . . . . . . . . . . The models that ERMs fit
(help erm intro)
[ERM] Intro 3 . . . . . . . . . . . . . . . . Endogenous covariates features
(help erm intro)
[ERM] Intro 4 . . . . . . . . . . . . . Endogenous sample-selection features
(help erm intro)
[ERM] Intro 5 . . . . . . . . . . . . . . . . Treatment assignment features
(help erm intro)
[ERM] Intro 6 . . . . . . . . . . Panel data and grouped data model features
(help erm intro)
[ERM] Intro 7 . . . . . . . . . . . . . . . . . . . . . Model interpretation
(help erm intro)
[ERM] Intro 8 . . . . . . . A Rosetta stone for extended regression commands
(help erm intro)
[ERM] Intro 9 . . . . . . . . . . Conceptual introduction via worked example
(help erm intro)
[ERM] eintreg . . . . . . . . . . . . . . . . . Extended interval regression
(help eintreg)
[ERM] eintreg postestimation . . . Postest. tools for eintreg and xteintreg
(help eintreg postestimation)
[ERM] eintreg predict . . . . . . . . . predict after eintreg and xteintreg
(help eintreg predict)
[ERM] eoprobit . . . . . . . . . . . . . Extended ordered probit regression
(help eoprobit)
[ERM] eoprobit postestimation . . Postest. tools for eoprobit and xteoprobit
(help eoprobit postestimation)
[ERM] eoprobit predict . . . . . . . predict after eoprobit and xteoprobit
(help eoprobit predict)
[ERM] eprobit . . . . . . . . . . . . . . . . . . Extended probit regression
(help eprobit)
[ERM] eprobit postestimation Postestimation tools for eprobit and xteprobit
(help eprobit postestimation)
[ERM] eprobit predict . . . . . . . . . predict after eprobit and xteprobit
(help eprobit predict)
[ERM] eregress . . . . . . . . . . . . . . . . . Extended linear regression
(help eregress)
[ERM] eregress postestimation . . Postest. tools for eregress and xteregress
(help eregress postestimation)
[ERM] eregress predict . . . . . . . predict after eregress and xteregress
(help eregress predict)
[ERM] ERM options . . . . . . . . . . . . Extended regression model options
(help erm options)
[ERM] estat teffects Average treat. effects for extended regression models
(help erm estat teffects)
[ERM] Example 1a . . Linear regression with continuous endogenous covariate
[ERM] Example 1b . Interval regression with continuous endogenous covariate
[ERM] Example 1c . Interval reg. with endog. covariate and sample selection
[ERM] Example 2a . . . . Linear regression with binary endogenous covariate
[ERM] Example 2b . . . . . . . . Linear regression with exogenous treatment
[ERM] Example 2c . . . . . . . Linear regression with endogenous treatment
[ERM] Example 3a . . Probit regression with continuous endogenous covariate
[ERM] Example 3b Probit regression with endogenous covariate and treatment
[ERM] Example 4a . . . . Probit regression with endogenous sample selection
[ERM] Example 4b . . Probit reg. with endog. treatment and sample selection
[ERM] Example 5 . . . . Probit regression with endogenous ordinal treatment
[ERM] Example 6a . . . Ordered probit regression with endogenous treatment
[ERM] Example 6b Ordered probit reg. with endog. treat. & sample selection
[ERM] Example 7 . . Random-effects reg. with continuous endogenous covariate
[ERM] Example 8a . Random effects in one equation and endogenous covariate
[ERM] Example 8b . Random effects, endog. covariate, and endog. sample sel.
[ERM] Example 9 . . Ordered probit reg. with endog. treat. & random effects
[ERM] predict advanced . . . . . . . . . . . . predict's advanced features
(help erm predict advanced)
[ERM] predict treatment . . . . . . . . . . predict for treatment statistics
(help erm predict treatment)
[ERM] Triangularize . . . . . . . How to triangularize a system of equations
[ERM] Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . Glossary
(help erm Glossary)
[FMM] fmm: betareg . . . . . . . Finite mixtures of beta regression models
(help fmm: betareg)
[FMM] fmm: cloglog . . Finite mixtures of complementary log-log reg. models
(help fmm: cloglog)
[FMM] fmm: glm . . Finite mixtures of generalized linear regression models
(help fmm: glm)
[FMM] fmm: intreg . . . . . . Finite mixtures of interval regression models
(help fmm: intreg)
[FMM] fmm: ivregress Finite mixtures of linear reg. models with endog. cov.
(help fmm: ivregress)
[FMM] fmm: logit . . . . . . Finite mixtures of logistic regression models
(help fmm: logit)
[FMM] fmm: mlogit . Finite mix. of multinomial (poly.) logistic reg. models
(help fmm: mlogit)
[FMM] fmm: nbreg . . Finite mixtures of negative binomial regression models
(help fmm: nbreg)
[FMM] fmm: ologit . . Finite mixtures of ordered logistic regression models
(help fmm: ologit)
[FMM] fmm: oprobit . . Finite mixtures of ordered probit regression models
(help fmm: oprobit)
[FMM] fmm: poisson . . . . . . Finite mixtures of Poisson regression models
(help fmm: poisson)
[FMM] fmm: probit . . . . . . . Finite mixtures of probit regression models
(help fmm: probit)
[FMM] fmm: regress . . . . . . Finite mixtures of linear regression models
(help fmm: regress)
[FMM] fmm: streg . . . . . . Finite mixtures of parametric survival models
(help fmm: streg)
[FMM] fmm: tobit . . . . . . . . Finite mixtures of tobit regression models
(help fmm: tobit)
[FMM] fmm: tpoisson . Finite mixtures of truncated Poisson regression models
(help fmm: tpoisson)
[FMM] fmm: truncreg . Finite mixtures of truncated linear regression models
(help fmm: truncreg)
[FMM] Example 1a . . . . . . . . . . . Mixture of linear regression models
(help fmm examples)
[FMM] Example 1b . . . . . . . . . . . . . Covariates for class membership
(help fmm examples)
[FMM] Example 1c . . . . . . . . . Testing coefficients across class models
(help fmm examples)
[FMM] Example 1d . . . . . . . . . . . . . . Component-specific covariates
(help fmm examples)
[FMM] Example 2 . . . . . . . . . . . . Mixture of Poisson regression models
(help fmm examples)
[H2OML] h2oml gbm . Gradient boosting machine for regression & classification
(help h2oml gbm)
[H2OML] h2oml gbregress . . . . . . . . . . . . . Gradient boosting regression
(help h2oml gbregress)
[H2OML] h2oml rf . . . . . . Random forest for regression and classification
(help h2oml rf)
[H2OML] h2oml rfregress . . . . . . . . . . . . . . . Random forest regression
(help h2oml rfregress)
[H2OML] h2omlgraph shapsummary . . . . . . . . . . Produce SHAP beeswarm plot
(help h2omlgraph shapsummary)
[H2OML] h2omlgraph shapvalues . . . . Produce SHAP values plot for indiv. obs.
(help h2omlgraph shapvalues)
[H2OML] metric_option . . . . . . . . . Classification and regression metrics
(help metric_option)
[LASSO] dslogit . . . . . . . . . . Double-selection lasso logistic regression
(help dslogit)
[LASSO] dspoisson . . . . . . . . . Double-selection lasso Poisson regression
(help dspoisson)
[LASSO] dsregress . . . . . . . . . . Double-selection lasso linear regression
(help dsregress)
[LASSO] poivregress . . Partialing-out lasso instrumental-variables regression
(help poivregress)
[LASSO] pologit . . . . . . . . . . . Partialing-out lasso logistic regression
(help pologit)
[LASSO] popoisson . . . . . . . . . . Partialing-out lasso Poisson regression
(help popoisson)
[LASSO] poregress . . . . . . . . . . . Partialing-out lasso linear regression
(help poregress)
[LASSO] xpoivregress . . Cross-fit partialing-out lasso inst.-variables reg.
(help xpoivregress)
[LASSO] xpologit . . . . . Cross-fit partialing-out lasso logistic regression
(help xpologit)
[LASSO] xpopoisson . . . . Cross-fit partialing-out lasso Poisson regression
(help xpopoisson)
[LASSO] xporegress . . . . . Cross-fit partialing-out lasso linear regression
(help xporegress)
[ME] mecloglog . Multilevel mixed-effects complementary log-log regression
(help mecloglog)
[ME] mecloglog postestimation . . . . . Postestimation tools for mecloglog
(help mecloglog postestimation)
[ME] meglm . . . . . . . Multilevel mixed-effects generalized linear models
(help meglm)
[ME] meintreg . . . . . . . . Multilevel mixed-effects interval regression
(help meintreg)
[ME] meintreg postestimation . . . . . . Postestimation tools for meintreg
(help meintreg postestimation)
[ME] melogit . . . . . . . . . Multilevel mixed-effects logistic regression
(help melogit)
[ME] melogit postestimation . . . . . . . Postestimation tools for melogit
(help melogit postestimation)
[ME] menbreg . . . . Multilevel mixed-effects negative binomial regression
(help menbreg)
[ME] menbreg postestimation . . . . . . . Postestimation tools for menbreg
(help menbreg postestimation)
[ME] menl . . . . . . . . . . . . . . . Nonlinear mixed-effects regression
(help menl)
[ME] menl postestimation . . . . . . . . . . Postestimation tools for menl
(help menl postestimation)
[ME] meologit . . . . Multilevel mixed-effects ordered logistic regression
(help meologit)
[ME] meologit postestimation . . . . . . Postestimation tools for meologit
(help meologit postestimation)
[ME] meoprobit . . . . . Multilevel mixed-effects ordered probit regression
(help meoprobit)
[ME] meoprobit postestimation . . . . . Postestimation tools for meoprobit
(help meoprobit postestimation)
[ME] mepoisson . . . . . . . . Multilevel mixed-effects Poisson regression
(help mepoisson)
[ME] mepoisson postestimation . . . . . Postestimation tools for mepoisson
(help mepoisson postestimation)
[ME] meprobit . . . . . . . . . Multilevel mixed-effects probit regression
(help meprobit)
[ME] meprobit postestimation . . . . . . Postestimation tools for meprobit
(help meprobit postestimation)
[ME] metobit . . . . . . . . . . Multilevel mixed-effects tobit regression
(help metobit)
[ME] metobit postestimation . . . . . . . Postestimation tools for metobit
(help metobit postestimation)
[ME] mixed . . . . . . . . . . . Multilevel mixed-effects linear regression
(help mixed)
[ME] mixed postestimation . . . . . . . . . Postestimation tools for mixed
(help mixed postestimation)
[META] Intro . . . . . . . . . . . . . . . . . Introduction to meta-analysis
[META] meta regress . . . . . . . . . . . . . . . . Meta-analysis regression
(help meta regress)
[META] meta regress postestimation . . Postestimation tools for meta regress
(help meta regress postestimation)
[META] estat bubbleplot . . . . . . . . . . Bubble plots after meta regress
(help estat bubbleplot)
[META] meta meregress . . . . . . . Multilevel mixed-effects meta-regression
(help meta meregress)
[META] meta multilevel . . . . . Multilevel random-intercepts meta-regression
(help meta multilevel)
[META] meta mvregress . . . . . . . . . . . . . Multivariate meta-regression
(help meta mvregress)
[META] meta mvregress postestimation Postestimation tools for meta mvregress
(help meta mvregress postestimation)
[META] estat heterogeneity (me) Compute multilevel heterogeneity statistics
(help estat heterogeneity me)
[META] estat sd . Display variance components as std. dev. and correlations
(help meta estat sd)
[MI] Estimation . . . . . . . Estimation commands for use with mi estimate
(help mi estimation)
[MI] mi impute . . . . . . . . . . . . . . . . . . . Impute missing values
(help mi impute)
[MI] mi impute intreg . . . . . . . . . . Impute using interval regression
(help mi impute intreg)
[MI] mi impute logit . . . . . . . . . . . Impute using logistic regression
(help mi impute logit)
[MI] mi impute mlogit . . . . Impute using multinomial logistic regression
(help mi impute mlogit)
[MI] mi impute mvn . . . . . . Impute using multivariate normal regression
(help mi impute mvn)
[MI] mi impute nbreg . . . . . . Impute using negative binomial regression
(help mi impute nbreg)
[MI] mi impute ologit . . . . . . Impute using ordered logistic regression
(help mi impute ologit)
[MI] mi impute poisson . . . . . . . . . . Impute using Poisson regression
(help mi impute poisson)
[MI] mi impute regress . . . . . . . . . . . Impute using linear regression
(help mi impute regress)
[MI] mi impute truncreg . . . . . . . . Impute using truncated regression
(help mi impute truncreg)
[MV] manova . . . . . . . Multivariate analysis of variance and covariance
(help manova)
[MV] mvreg . . . . . . . . . . . . . . . . . . . . Multivariate regression
(help mvreg)
[PSS-2] power oneslope . Power analysis for slope test in simple linear reg.
(help power oneslope)
[PSS-2] power rsquared . . Power analysis for R2 test in multiple linear reg.
(help power rsquared)
[PSS-2] power pcorr Power analy. for part.-corr. test in multiple linear reg.
(help power pcorr)
[PSS-2] power cox . . . Power analysis for the Cox proportional hazards model
(help power cox)
[SEM] Intro 5 . . . . . . . . . . . . . . . . . . . . . . . . Tour of models
(help sem introduction)
[SEM] Example 6 . . . . . . . . . . . . . . . . . . . . . Linear regression
(help sem examples)
[SEM] Example 12 . . . . . . . . . . . . . . Seemingly unrelated regression
(help sem examples)
[SEM] Example 33g . . . . . . . . . . . . . . . . . . . Logistic regression
(help sem examples)
[SEM] Example 37g . . . . . . . . . . . . . Multinomial logistic regression
(help sem examples)
[SEM] Example 41g . . Two-level multinomial logistic regression (multilevel)
(help sem examples)
[SEM] Example 43g . . . . . . . . . . . . . . . . . . . . . Tobit regression
(help sem examples)
[SEM] Example 44g . . . . . . . . . . . . . . . . . . . Interval regression
(help sem examples)
[SEM] Example 53g . . . . . . . . . . . . Finite mixture Poisson regression
(help sem examples)
[SEM] Example 54g . . Finite mixture Poisson regression, multiple responses
(help sem examples)
[SEM] gsem family-and-link options . . . . . . . . Family-and-link options
(help gsem family and link options)
[SP] Intro 8 . . . . . . . . . . . . . . . . . . The Sp estimation commands
(help sp intro)
[SP] estat moran Moran's test of residual correlation with nearby residuals
(help estat moran)
[SP] spregress . . . . . . . . . . . . . . . Spatial autoregressive models
(help spregress)
[SP] spregress postestimation . . . . . Postestimation tools for spregress
(help spregress postestimation)
[SP] spxtregress . . . . . . . Spatial autoregressive models for panel data
(help spxtregress)
[ST] PH plots (right-censored) PH-assumption plots for right-censored data
(help ph plots right censored)
[ST] stcox . . . . . . . . . . . . . . . . . Cox proportional hazards model
(help stcox)
[ST] stcox postestimation . . . . . . . . . Postestimation tools for stcox
(help stcox postestimation)
[ST] stcrreg . . . . . . . . . . . . . . . . . . Competing-risks regression
(help stcrreg)
[ST] stcrreg postestimation . . . . . . . Postestimation tools for stcrreg
(help stcrreg postestimation)
[ST] stcurve . Plot survivor or related function after streg, stcox, & more
(help stcurve)
[ST] stintreg . Parametric models for interval-censored survival-time data
(help stintreg)
[ST] streg . . . . . . . . . . . . . . . . . . . Parametric survival models
(help streg)
[ST] streg postestimation . . . . . . . . . Postestimation tools for streg
(help streg postestimation)
[SVY] Survey . . . . . . . . . . . . . . . Introduction to survey commands
(help survey)
[SVY] estat . . . . . . . . . . . Postestimation statistics for survey data
(help svy estat)
[SVY] svy estimation . . . . . . . . . Estimation commands for survey data
(help svy estimation)
[SVY] svy postestimation . . . . . . . . . . . Postestimation tools for svy
(help svy postestimation)
[SVY] svy: tabulate oneway . . . . . . . . . One-way tables for survey data
(help svy: tabulate oneway)
[SVY] svy: tabulate twoway . . . . . . . . . Two-way tables for survey data
(help svy: tabulate twoway)
[TS] arima . . . . . . . ARIMA, ARMAX, and other dynamic regression models
(help arima)
[TS] mswitch . . . . . . . . . . . . . . Markov-switching regression models
(help mswitch)
[TS] mswitch postestimation . . . . . . . Postestimation tools for mswitch
(help mswitch postestimation)
[TS] newey . . . . . . . . . . . Regression with Newey-West standard errors
(help newey)
[TS] newey postestimation . . . . . . . . . Postestimation tools for newey
(help newey postestimation)
[TS] prais . . . . . . . . . . Prais-Winsten and Cochrane-Orcutt regression
(help prais)
[TS] prais postestimation . . . . . . . . . Postestimation tools for prais
(help prais postestimation)
[TS] threshold . . . . . . . . . . . . . . . . . . . . Threshold regression
(help threshold)
[TS] threshold postestimation . . . . . Postestimation tools for threshold
(help threshold postestimation)
[XT] xtabond . . . . . . Arellano-Bond linear dynamic panel-data estimation
(help xtabond)
[XT] xtcloglog . . . Random-effects and population-averaged cloglog models
(help xtcloglog)
[XT] xtdpd . . . . . . . . . . . . . . Linear dynamic panel-data estimation
(help xtdpd)
[XT] xtdpdsys . . . . . . . . . . Arellano-Bover/Blundell-Bond estimation
(help xtdpdsys)
[XT] xteintreg . . . . . . . . Extended random-effects interval regression
(help xteintreg)
[XT] xteoprobit . . . . Extended random-effects ordered probit regression
(help xteoprobit)
[XT] xteprobit . . . . . . . . . Extended random-effects probit regression
(help xteprobit)
[XT] xteregress . . . . . . . . Extended random-effects linear regression
(help xteregress)
[XT] xtfrontier . . . . . . . . Stochastic frontier models for panel data
(help xtfrontier)
[XT] xtgee . . . . . . . . . . . GEE population-averaged panel-data models
(help xtgee)
[XT] xtgls . . GLS linear model with heteroskedastic and correlated errors
(help xtgls)
[XT] xtheckman . . . . . . Random-effects regression with sample selection
(help xtheckman)
[XT] xtheckman postestimation . . . . . Postestimation tools for xtheckman
(help xtheckman postestimation)
[XT] xthtaylor . . . . Hausman-Taylor estimator for error-components model
(help xthtaylor)
[XT] xtintreg . . . . . . . Random-effects interval-data regression model
(help xtintreg)
[XT] xtintreg postestimation . . . . . . Postestimation tools for xtintreg
(help xtintreg postestimation)
[XT] xtivreg . Instr. var. & two-stage least squares for panel-data models
(help xtivreg)
[XT] xtlogit . Fixed-effects, random-effects, & pop.-averaged logit models
(help xtlogit)
[XT] xtnbreg . Fixed-, random-effects, & pop.-averaged neg. binomial models
(help xtnbreg)
[XT] xtnbreg postestimation . . . . . . . Postestimation tools for xtnbreg
(help xtnbreg postestimation)
[XT] xtologit . . . . . . . . . . . Random-effects ordered logistic model
(help xtologit)
[XT] xtoprobit . . . . . . . . . . . . Random-effects ordered probit model
(help xtoprobit)
[XT] xtpcse . . . . Linear regression with panel-corrected standard errors
(help xtpcse)
[XT] xtpcse postestimation . . . . . . . . Postestimation tools for xtpcse
(help xtpcse postestimation)
[XT] xtpoisson . . . Fixed-, random-effects & pop.-averaged Poisson models
(help xtpoisson)
[XT] xtpoisson postestimation . . . . . Postestimation tools for xtpoisson
(help xtpoisson postestimation)
[XT] xtprobit . . . . Random-effects and population-averaged probit models
(help xtprobit)
[XT] xtrc . . . . . . . . . . . . . . . . . . . Random-coefficients model
(help xtrc)
[XT] xtrc postestimation . . . . . . . . . . Postestimation tools for xtrc
(help xtrc postestimation)
[XT] xtreg . . . . . . . . . . . . . . . . . . Linear models for panel data
(help xtreg)
[XT] xtregar . Fixed- & random-effects linear models with an AR(1) disturb.
(help xtregar)
[XT] xttobit . . . . . . . . . . . . . . . . . . Random-effects tobit model
(help xttobit)
[P] _robust . . . . . . . . . . . . . . . . . . Robust variance estimates
(help _robust)
help mata _lsfitqr() . . . Least-squares regression using QR decomposition
(help mata _lsfitqr())
help meqrlogit . Multilevel mixed-effects logistic regression (QR decomp.)
(help meqrlogit)
help meqrlogit postestimation . . . . . Postestimation tools for meqrlogit
(help meqrlogit postestimation)
help meqrpoisson . Multilevel mixed-effects Poisson regression (QR decomp.)
(help meqrpoisson)
help meqrpoisson postestimation . . . Postestimation tools for meqrpoisson
(help meqrpoisson postestimation)
NC461 . . . . . . . . . . . NetCourse 461: Univariate time series with Stata
http://www.stata.com/netcourse/univariate-time-series-intro-nc461/
NC471 . . . . . . . . NetCourse 471: Introduction to panel data using Stata
http://www.stata.com/netcourse/panel-data-intro-nc471/
NC631 . . . . . NetCourse 631: Introduction to survival analysis using Stata
http://www.stata.com/netcourse/intro-survival-analysis-nc631/
Train . . . . . . . . . . . . . . . . . . . Regression modeling using Stata
http://www.stata.com/training/public/regression-modeling-using-stata/
Train . . . . . . . . . . . . . . . . . . . Panel-data analysis using Stata
http://www.stata.com/training/public/panel-data-analysis-using-stata/
Train . . Causal inference using Stata: Estimating average treatment effects
http://www.stata.com/training/public/treatment-effects-using-stata/
Train . . . . . . . . . . . . Introduction to Bayesian analysis using Stata
http://www.stata.com/training/public/bayesian-analysis-using-stata/
Train . . . . . . . . . . . . . . . . . . . . Survival analysis using Stata
http://www.stata.com/training/public/survival-analysis-using-stata/
Book . . . . . . . . . . . . . . . . Microeconometrics Using Stata, 2nd Ed.
. . . . . . . . . . . . . . . . A. Colin Cameron and Pravin K. Trivedi
http://www.stata.com/bookstore/microeconometrics-stata/
Book . . . . . . Multilevel and Longitudinal Modeling Using Stata, 4th Ed.
. . . . . . . . . . . . Sophia Rabe-Hesketh and Anders Skrondal french
http://www.stata.com/bookstore/multilevel-longitudinal-modeling-stata/
Book . Interpreting and Visualizing Regression Models Using Stata, 2nd Ed.
. . . . . . . . . . . . . . . . . . . . . . . . . Michael N. Mitchell
http://www.stata.com/bookstore/interpreting-visualizing-
regression-models/
Book . . . . . . . . Psychological Statistics and Psychometrics Using Stata
. . . . . . . . . . . . . . . . . . . . . . . . . . . . Scott Baldwin
http://www.stata-press.com/books/psychological-statistics-and-
psychometrics-using-stata/
Book . . . . . . . . . . Generalized Linear Models and Extensions, 4th Ed.
. . . . . . . . . . . . . . . . . James W. Hardin and Joseph M. Hilbe
http://www.stata.com/bookstore/generalized-linear-models-extensions/
Book . . . . . . . . . . . . . A Gentle Introduction to Stata, Rev. 6th Ed.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . Alan C. Acock
http://www.stata.com/bookstore/gentle-introduction-to-stata/
Book . . . . An Introduction to Survival Analysis Using Stata, Rev. 3rd Ed.
. . . . . . . . . . . . . . . M. Cleves, W. Gould, and Y. V. Marchenko
http://www.stata.com/bookstore/survival-analysis-stata-introduction/
Book Meta-Analysis in Stata: An Updated Collection from the Journal, 2nd Ed
. . . . . . . . . . Tom M. Palmer and Jonathan A. C. Sterne (editors)
http://www.stata.com/bookstore/meta-analysis-in-stata/
Book Regression Models for Categorical Dep. Variables Using Stata, 3rd Ed.
. . . . . . . . . . . . . . . . . . . J. Scott Long and Jeremy Freese
http://www.stata.com/bookstore/regression-models-categorical-
dependent-variables/
Book . . . . . . . . . . . . . . . . . . Data Analysis Using Stata, 3rd Ed.
. . . . . . . . . . . . . . . . . . . Ulrich Kohler and Frauke Kreuter
http://www.stata.com/bookstore/data-analysis-using-stata/
Book . . . . . . . . . . Maximum Likelihood Estimation with Stata, 5th Ed.
. . . . . . . . . . . . Jeffrey Pitblado, Brian Poi, and William Gould
http://www.stata.com/bookstore/maximum-likelihood-estimation-stata/
Book . . . . . . . . . . An Introduction to Modern Econometrics Using Stata
. . . . . . . . . . . . . . . . . . . . . . . . . Christopher F. Baum
http://www.stata.com/bookstore/modern-econometrics-stata/
Video . . . . . . . . . . . . . . . . . . Simple linear regression in Stata
10/24 http://www.youtube.com/watch?v=8slTP4myjdU
Learn how to fit a simple linear regression model in
Stata using the regress command. Access marginal
means, diagnostics, tests, and predictions for your
model using the Postestimation Selector.
Video . . . . . . . . . . Treatment effects in Stata: Regression adjustment
3/24 http://www.youtube.com/watch?v=YtOKKE6Fj90
Learn how to use the teffects ra command in Stata
to estimate the average treatment effect (ATE), the
average treatment effect on the treated (ATET), and
the potential-outcome means (POMs) from observational
data by regression adjustment.
Video . . . . . . . . . . . . Tour of treatment-effects estimators in Stata
2/24 http://www.youtube.com/watch?v=7i0w93CaOBI
Take a tour of the treatment-effects features for
causal inference in Stata, including the regression-
adjustment estimator, the inverse-probability weights
(IPW) estimator, doubly robust estimators AIPW and
IPWRA, propensity-score matching, and nearest-neighbor
matching.
Video . . . . . . . . . . . . . . Instrumental-variables quantile regression
4/23 http://www.youtube.com/watch?v=5MUpO4X4l7g
Demonstration of the ivqregress command in Stata 18
for quantile regression when we suspect that one or
more of our covariates may be endogenous. The models
fit by ivqregress correct endogeneity bias by using
an instrumental-variables approach.
Video . . . . . . . . . . . . . . . . . . . . . . . Multilevel meta-analysis
4/23 http://www.youtube.com/watch?v=XB8-QrVar3w
Demonstration of Stata 18's new commands,
meta multilevel and meta meregress, for performing
multilevel meta-analysis, including higher-order
models, random slopes, alternative covariance
structures, heterogeneity statistics, and more.
Video . . . . . . . . . . . . . Wild cluster bootstrap for linear regression
4/23 http://www.youtube.com/watch?v=dWzu4Mayd8M
Demonstration of the wildbootstrap command in Stata 18
for wild cluster bootstrap in linear regression, linear
regression with a large indicator-variable set, and
fixed-effects linear models. Wild cluster bootstrap
provides consistent p-values and confidence intervals
in clustered data even with a small number of clusters
or unbalanced clusters.
Video . . . . . Poisson regression with continuous & categorical predictors
10/22 http://www.youtube.com/watch?v=oyeeByvj6fU
This video demonstrates how to fit a Poisson regression
model with both continuous and categorical predictor
variables using factor-variable notation. It also shows
how to test hypotheses about the parameters, estimate
marginal predictions from the model, and graph those
margins.
Video . . . . . . . . . . . . Poisson regression with categorical predictors
10/22 http://www.youtube.com/watch?v=9cPf1Tqn4Bg
This video demonstrates how to fit a Poisson regression
model with a categorical predictor variable using
factor-variable notation. It also shows how to test
hypotheses about the parameters, estimate marginal
predictions from the model, and graph those margins.
Video . . . . . . . . . . . . Poisson regression with continuous predictors
8/22 http://www.youtube.com/watch?v=SH-_rkT8_oE
This video demonstrates how to fit a Poisson regression
model with a continuous predictor variable using
factor-variable notation. It also shows how to test
hypotheses about the parameters, estimate marginal
predictions from the model, and graph those margins.
Video . . . . . . Multinomial probit regression with categorical predictors
8/22 http://www.youtube.com/watch?v=lX7dIppaWGU
This video demonstrates how to fit a multinomial
probit regression model with a categorical predictor
variable using factor-variable notation. It also
shows how to test hypotheses about the parameters,
estimate marginal predictions from the model, and
graph those margins.
Video . . . . . . . Multinomial probit regression with continuous predictors
8/22 http://www.youtube.com/watch?v=0PoSsi2SLz8
This video demonstrates how to fit a multinomial
probit regression model with a continuous predictor
variable using factor-variable notation. It also
shows how to test hypotheses about the parameters,
estimate marginal predictions from the model, and
graph those margins.
Video Multinomial probit regression with continuous & categorical predictors
8/22 http://www.youtube.com/watch?v=lvTitfurnno
This video demonstrates how to fit a multinomial
probit regression model with both continuous and
categorical predictor variables using factor-variable
notation. It also shows how to test hypotheses about
the parameters, estimate marginal predictions from
the model, and graph those margins.
Video Multinomial logistic regression w/ continuous & categorical predictors
8/22 http://www.youtube.com/watch?v=szKsmumcEkg
Learn how to fit a multinomial logistic regression
model with both continuous and categorical predictor
variables using factor-variable notation. It also
shows how to test hypotheses about the parameters,
estimate marginal predictions from the model, and
graph those margins.
Video . . . . . . Multinomial logistic regression with continuous predictors
8/22 http://www.youtube.com/watch?v=8QTxVZm4plA
This video demonstrates how to fit a multinomial
logistic regression model with a continuous
predictor variable using factor-variable notation.
It also shows how to test hypotheses about the
parameters, estimate marginal predictions from the
model, and graph those margins.
Video . . . . . Multinomial logistic regression with categorical predictors
8/22 http://www.youtube.com/watch?v=BNGSKoMekk0
This video demonstrates how to fit a multinomial
logistic regression model with a categorical
predictor variable using factor-variable notation.
It also shows how to test hypotheses about the
parameters, estimate marginal predictions from the
model, and graph those margins.
Video . . . . . . . Probit regression with continuous/categorical predictors
6/22 http://www.youtube.com/watch?v=qYm8r5hyf8I
Learn how to fit a probit regression model with both
continuous and categorical predictor variables using
factor-variable notation. It also shows how to test
hypotheses about the parameters, estimate marginal
predictions from the model, and graph those margins.
Video . . . . . . . . . . . . . Probit regression with continuous predictors
6/22 http://www.youtube.com/watch?v=vgIFiq5OGOw
Learn how to fit a probit regression model with a
continuous predictor variable using factor-variable
notation. It also shows how to test hypotheses
about the parameters, estimate marginal predictions
from the model, and graph those margins.
Video . . . . . . . . . . . . Probit regression with categorical predictors
6/22 http://www.youtube.com/watch?v=jHwocqIOIxU
Learn how to fit a probit regression model with a
categorical predictor variable using factor-variable
notation. It also shows how to test hypotheses
about the parameters, estimate marginal predictions
from the model, and graph those margins.
Video . . . . . . Logistic regression with continuous/categorical predictors
3/22 http://www.youtube.com/watch?v=RqI1lLH3PKo
Learn how to fit a logistic regression model with
both continuous and categorical predictor variables
using factor-variable notation. The video also shows
how to test hypotheses about the parameters, estimate
marginal predictions from the model, and graph those
margins.
Video . . . . . . . . . . . Logistic regression with categorical predictors
3/22 http://www.youtube.com/watch?v=SnEbDgLzhtw
Learn how to fit a logistic regression model with
a categorical predictor variable using factor-
variable notation. It also shows how to test
hypotheses about the parameters, estimate marginal
predictions from the model, and graph those margins.
Video . . . . . . . . . . . . Logistic regression with continuous predictors
3/22 http://www.youtube.com/watch?v=BSRZyP4T-Bw
Learn how to fit a logistic regression model with a
continuous predictor variable using factor-variable
notation. This video also shows how to test
hypotheses about the parameters, estimate marginal
predictions from the model, and graph those margins.
Video . . . . . . . . . . . . . Linear regression with continuous predictors
2/22 http://www.youtube.com/watch?v=D5Szv8SwJN4
Learn how to fit a linear regression model with a
continuous predictor variable using factor-variable
notation. It also shows how to test hypotheses about
the parameters, estimate marginal predictions from the
model, and graph those margins.
Video . . . . . . . . . . . . Linear regression with categorical predictors
2/22 http://www.youtube.com/watch?v=_ti7Lju1odk
Learn how to fit a linear regression model with a
categorical predictor variable using factor-variable
notation. It also shows how to test hypotheses about
the parameters, estimate marginal predictions from the
model, and graph those margins.
Video . . . . . . . Linear regression with continuous/categorical predictors
2/22 http://www.youtube.com/watch?v=7f8dQfYoCG8
Learn how to fit a linear regression model with both
continuous and categorical predictor variables using
factor-variable notation. It also shows how to test
hypotheses about the parameters, estimate marginal
predictions from the model, and graph those margins.
Video . . . Customizable tables: How to create tables for a regression model
5/21 http://www.youtube.com/watch?v=TFFdTIHHtUg
This video demonstrates how to create tables for a
regression model using customizable tables in
Stata 17.
Video . . Customizable tables: Create tables for multiple regression models
5/21 http://www.youtube.com/watch?v=sHs_sk8JkL0
This video demonstrates how to create tables for multiple
regression models using customizable tables in Stata 17.
Video . . . . . . . . . . . . . . . . . . . Nonparametric series regression
6/19 http://www.youtube.com/watch?v=IkOmd-OKAog
Stata's npregress series estimates nonparametric series
regression using a B-spline, spline, or polynomial basis.
Nonparametric regression is agnostic about the functional
form between the outcome and the covariates and is
therefore not subject to misspecification error.
Nonparametric series regression models the mean of the
outcome conditional on a function of the covariates.
This video provides a quick overview of how to fit a
model with npregress series, and explore the results
with margins.
Video . . . . . . . . . . . . . . Extended regression models for panel data
6/19 http://www.youtube.com/watch?v=JHi_uCNvuUI
Learn about Stata's features for fitting extended
regression models for panel data. Fit your model while
simultaneously accounting for endogenous covariates,
sample selection, and endogenous treatment. Estimators
for continuous, binary, ordered, and censored outcomes
are supported.
Video . . . . . . Random-effects regression with endogenous sample selection
6/19 http://www.youtube.com/watch?v=YJ8XPCUbA2g
The new xtheckman command fits random-effects models
with endogenous sample selection. This command
incorporates the correlation within panels to provide
both consistent and efficient estimates. Learn how to
fit models with xtheckman, and how to explore the
results with margins.
Video . . . . . . . . . . . . . . . . . . Bayesian analysis: Multiple chains
6/19 http://www.youtube.com/watch?v=ekhrPThSQEM
Learn about the new features in Stata 16 for performing
Bayesian analysis using multiple chains. Use these
features to simulate multiple MCMC chains, compute
Gelman-Rubin convergence diagnostics, and view posterior
summaries and graphs for the multiple MCMC chains. This
video demonstrates how to fit a Bayesian regression
model with multiple chains, and create diagnostic plots.
Video . . . . . Probit regression with categorical and continuous covariates
9/18 http://www.youtube.com/watch?v=JHZKV9DPxfI
Check out how to fit a probit regression model with both
categorical and continuous covariates and how to use
margins and marginsplot to interpret the results.
Video . . . . . . . . . . . . . Probit regression with continuous covariates
9/18 http://www.youtube.com/watch?v=AunPalHL_us
Discover how to fit a probit regression model with a
continuous covariate and how to use margins and
marginsplot to interpret the results.
Video . . . . . . . . . . . . Probit regression with categorical covariates
6/18 http://www.youtube.com/watch?v=qt8DPrVGCok
Find out how to fit a probit regression model with
a categorical covariate and how to use margins and
marginsplot to interpret the results.
Video . . . . . . Extended regression models, part 4: Interpreting the model
3/18 http://www.youtube.com/watch?v=CUTjPBygMV4
Learn how to interpret the results of Stata's extended
regression models in Stata 15.
Video . . . Extended regression models, part 3: Endogenous sample selection
2/18 http://www.youtube.com/watch?v=xeDIh-jugIc
Learn how to use Stata's extended regression models to
account for endogenous sample selection in Stata 15.
Video . . Extended regression models, part 2: Nonrandom treatment assignment
1/18 http://www.youtube.com/watch?v=5doinKwx2HI
Learn how to use Stata's extended regression models to
account for nonrandom treatment assignment in Stata 15.
Video . . . . . . Extended regression models, part 1: Endogenous covariates
1/18 http://www.youtube.com/watch?v=bPhNq6RYd-I&t=91s
Learn how to use Stata's extended regression models to
account for endogenous covariates in Stata 15.
Video . . . . . . . . . Bayesian linear regression: Customize the MCMC chain
1/18 http://www.youtube.com/watch?v=KStrHq2Nw6w&t=84s
Learn how to customize the MCMC chain when fitting a
Bayesian linear regression model using the bayes prefix
in Stata 15.
Video . . Bayesian linear regression: Checking convergence of the MCMC chain
11/17 http://www.youtube.com/watch?v=W9EUr1rtH-k&t=75s
Learn how to check the convergence of the MCMC chain
after fitting a Bayesian linear regression model using
the bayes prefix in Stata 15.
Video . . . . . . . . . . Bayesian linear regression: Specify custom priors
11/17 http://www.youtube.com/watch?v=76K1Cznzz0Q&t=68s
Learn how to specify custom priors when fitting a
Bayesian linear regression model using the bayes prefix
in Stata 15.
Video . . . . . . . . . . Bayesian linear regression using the bayes prefix
10/17 http://www.youtube.com/watch?v=L7GfMLl7EqM&t=12s
Learn how to fit Bayesian linear regression using the
bayes prefix in Stata 15.
Video . . . . . . . . . . . . . A prefix for Bayesian regression in Stata 15
6/17 http://www.youtube.com/watch?v=BhFYZWYpn5U
Stata's new Bayesian prefix provides a simple and elegant
way of fitting Bayesian regression models. Simply prefix
your estimation command with bayes:! This video provides
a quick overview of the Bayesian prefix and the estimation
commands it supports, including models for continuous,
binary, ordinal, categorical, count, survival, and
multilevel outcomes.
Video . . . . . . . . . . . . . . . . . Nonparametric regression in Stata 15
6/17 http://www.youtube.com/watch?v=w6vAPP311hA
npregress estimates nonparametric kernel regression using
a local-linear or local-constant estimator. Nonparametric
regression, like linear regression, estimates mean
outcomes for a given set of covariates. Unlike linear
regression, nonparametric regression is agnostic about
the functional form between the outcome and the covariates
and is therefore not subject to misspecification error.
This video provides a quick overview of the theory behind
nonparametric kernel regression using Stata.
Video . . . Power anal. for cluster randomized designs and linear regression
6/17 http://www.youtube.com/watch?v=wYozqjgCouc
Stata now offers power and sample-size analysis for
linear regression and for cluster randomized designs
(CRD). You can now use one of the three new methods of
the existing power command -- oneslope, rsquared, or
pcorr -- to compute one of power, sample size, or effect
size for a test of coefficients in a simple or multiple
linear regression. You can also adjust for clustering or
account for a cluster randomized design using the new
cluster option in some of the existing power methods.
Finally, you can add your own methods to power! This
video briefly describes the new power and sample-size
features and demonstrates a point-and-click interface
for one of the examples.
Video . . At a glance: Multilevel tobit and interval regression in Stata 15
6/17 http://www.youtube.com/watch?v=Jqiqd9dkXnY
The new metobit command fits random-effects panel-data
models for which the outcome is censored. Censored
means that rather than the outcome (y) being observed
precisely in all observations, in some observations it
is known only that y ≤ yl (left-censoring), or y ≥ yu
(right-censoring), or yl ≤ y ≤ yu (interval-censoring).
Random effects imply a model for the unobserved time-
invariant component of each panel. Think unobserved
individual ability in a wage model. The random effect
may be constant (random intercept) or change with a
variable (random slope). metobit allows you to fit
models with different levels of nesting, such as
students within a school district within a city.
Video . . . . . . At a glance: Heteroskedastic linear regression in Stata 15
6/17 http://www.youtube.com/watch?v=ZdrJV3WX0S8
hetregress fits linear regressions in which the variance
is an exponential function of covariates that you specify.
It allows you to model the heteroskedasticity.
Video . . . . . . At a glance: Extended regression models (ERMs) in Stata 15
6/17 http://www.youtube.com/watch?v=dr19OfsvSoA&t=3s
Stata's new extended regression models (ERMs) are a
specific class of models that address several
complications that arise frequently in data:
1) endogenous covariates, 2) sample selection, and
3) nonrandom treatment assignment. These complications
can occur alone or in any combination. ERMs allow you
to make valid inferences as though these complications
did not occur in your data.
Video . . . . . . . . . . . . . . . . . . . Threshold regression in Stata 15
6/17 http://www.youtube.com/watch?v=JWsv46rxjTk
Thresholds delineate one state from another. There is
one effect -- one set of coefficients -- up to the
threshold and another effect -- another set of
coefficients -- beyond it.
Video . . . . . . . . . . . . . . . . . Censored Poisson regression in Stata
4/15 http://www.youtube.com/watch?v=6m_SXthPv1U
This video is an introduction to censored Poisson
regression in Stata. We briefly discuss what censoring
is, the causes of censored count data, and how you can
use Stata to analyze censored count data with the new
cpoisson.
Video . . . . . . . . . . . . Regression models for fractional data in Stata
4/15 http://www.youtube.com/watch?v=mJzrWocdWGY
This video is an introduction to Stata's estimators
for modeling fractional responses such as rates and
proportions. Stata 14 includes two new commands that
allow you to estimate beta regression models and
fractional regression models. In this video, we briefly
show you how to fit a fractional regression model and
obtain elasticities using margins.
Video . . . . . . . . . . . . Instrumental variables regression using Stata
9/14 http://www.youtube.com/watch?v=lbnswRJ1qV0
This video demonstrates how to fit instrumental variable
models for endogenous covariates using the ivregress
command.
Video . . . . . . . Treatment effects: Augmented inverse probability weights
10/13 http://www.youtube.com/watch?v=HqShQ1RcP5s
Learn how to estimate treatment effects using augmented
inverse probability weights in Stata. Treatment effects
estimators allow us to estimate the causal effect of a
treatment on an outcome using observational data.
Video . Treatment effects: Inverse prob. weights with regression adjustment
10/13 http://www.youtube.com/watch?v=dmZCSbpL-W4
Explore how to estimate treatment effects using inverse
probability weights with regression adjustment in Stata.
Treatment effects estimators allow us to estimate the
causal effect of a treatment on an outcome using
observational data.
Video . . . . . . . . . . . . Treatment effects: Inverse probability weights
10/13 http://www.youtube.com/watch?v=fmnkEmlJPOU
Watch this demonstration on how to estimate treatment
effects using inverse probability weights with Stata.
Treatment effects estimators allow us to estimate the
causal effect of a treatment on an outcome using
observational data.
Video . . . . . . . . . . . . . . . Treatment effects: Regression adjustment
10/13 http://www.youtube.com/watch?v=TYFbOjWZ7lE
Learn how to estimate treatment effects using
regression adjustment in Stata. Treatment effects
estimators allow us to estimate the causal effect of
a treatment on an outcome using observational data.
Video . . . . . . . . . . . . . . Introduction to treatment effects: Part 1
10/13 http://www.youtube.com/watch?v=p578jxAPJT4
Join us for a conceptual introduction to the estimation
of the causal effect of a treatment on an outcome using
observational data (treatment effects). Part 1
introduces regression adjustment, inverse probability
weights, and doubly robust estimation. Matching is
covered in Part 2, and the use of the treatment effects
commands are covered in subsequent videos.
Video . . . MI, part 3: Imputing a binary variable with logistic regression
4/13 http://www.youtube.com/watch?v=QVvTpPx2LyU
Learn how to impute a single binary variable with
logistic regression using Stata.
Video . . . . . . MI, part 1: Imputing a continuous variable with mi regress
4/13 http://www.youtube.com/watch?v=i6SOlq0mjuc
Discover how to impute a single continuous variable
with mi impute regress using Stata.
Video . . . . . . . . Time series, part 5: Introduction to ARMA/ARIMA models
3/13 http://www.youtube.com/watch?v=8xt4q7KHfBs
Learn how to fit ARMA/ARIMA models in Stata.
Video . . . . . . . . Logistic regression in Stata, part 3: Factor variables
2/13 http://www.youtube.com/watch?v=vCSh613UMic
Learn how to fit a logistic regression model using
factor variables.
Video . . . . . Logistic regression in Stata, part 2: Continuous predictors
2/13 http://www.youtube.com/watch?v=vmZ_uaFImzQ
Learn how to fit a logistic regression model with a
continuous predictor (independent) variable.
Video . . . . . . . Logistic regression in Stata, part 1: Binary predictors
2/13 http://www.youtube.com/watch?v=rSU1L3-xRk0
Explore how to fit a logistic regression model with a
binary predictor (independent) variable.
Video . . . . . . . . . . Introduction to contrasts in Stata: One-way ANOVA
1/13 http://www.youtube.com/watch?v=XaeStjh6n-A
Discover how to use the contrast command to compute
contrasts after a one-way ANOVA model. These include
reference level, grand means, helmert and orthogonal
polynomial contrasts.
Video . . Profile plots and interaction plots: Continuous and cat. variables
1/13 http://www.youtube.com/watch?v=iHfTJIdhwWs
Discover how to use the marginsplot command to graph
predictions from a linear regression model with an
interaction between continuous and categorical
covariates.
Video . Profile plots and interaction plots: Interactions of cat. variables
12/12 http://www.youtube.com/watch?v=7M3vJrLq1t0
Continue exploring how to use the marginsplot command
to graph predictions from a linear regression model
with two categorical covariates.
Video . . Profile plots and interaction plots: A single continuous variable
12/12 http://www.youtube.com/watch?v=O4QbEaHRGT8
Discover how to use the marginsplot command to graph
predictions from a linear regression model with a
continuous covariate.
Video . . Profile plots and interaction plots: A single categorical variable
12/12 http://www.youtube.com/watch?v=7iSa_gboh9I
Discover how to use the marginsplot command to graph
predictions from a linear regression model with a
categorical variable.
Video . . . . . . . . . . . . Introduction to margins, part 3: Interactions
11/12 http://www.youtube.com/watch?v=43uX4D_7uaI
Explore using the margins command to compute
predictions from a linear regression model with an
interaction between categorical and continuous
covariates.
Video . . . . . . . . Introduction to margins, part 2: Continuous variables
11/12 http://www.youtube.com/watch?v=L9-PWY79aVA
Discover using the margins command to compute
predictions from a linear regression model with a
continuous covariate.
Video . . . . . . . . Introduction to margins, part 1: Categorical variables
11/12 http://www.youtube.com/watch?v=XAG4CbIbH0k
Explore the margins command to compute predictions
from a linear regression model with a categorical
covariate.
Video . . . . . Introduction to factor variables, part 3: More interactions
10/12 http://www.youtube.com/watch?v=9vR9n35aX5k
Explore how to use factor variables in Stata to
estimate interactions between a categorical variable
and a continuous variable in regression models.
Video . . . . . . . . Introduction to factor variables, part 2: Interactions
10/12 http://www.youtube.com/watch?v=f-tLLX8v11c
Discover how to use factor variables in Stata to
estimate interactions between two categorical
variables in regression models.
Video . . . . . . . . . Introduction to factor variables, part 1: The basics
10/12 http://www.youtube.com/watch?v=Wa1Nd9epHmY
Discover factor variables and a basic introduction
to using them in regression models.
Video . . . . . . . . . . . . . . . . . . . Analysis of covariance in Stata
10/12 http://www.youtube.com/watch?v=Kb9WG4o9zLk
Learn how to conduct an analysis of covariance (ANCOVA)
in Stata.
Video . . . . . . . . . . . . . . . . . . Simple linear regression in Stata
10/12 http://www.youtube.com/watch?v=HafqFSB9x70
Discover how to fit a simple linear regression model
and graph the results using Stata.
Blog . . . . . Just released: A Gentle Introduction to Stata, Rev. 6th Ed.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Ksionda
2/23 http://blog.stata.com/2023/02/08/just-released-from-stata-
press-a-gentle-introduction-to-stata-revised-sixth-edition/
Blog Just released from Stata Press: Microeconometrics Using Stata, 2nd Ed.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Ksionda
8/22 http://blog.stata.com/2022/08/31/just-released-from-stata-
press-microeconometrics-using-stata-second-edition/
Blog Just released: Multilevel & Longitudinal Modeling Using Stata, 4th Ed.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Ksionda
9/21 http://blog.stata.com/2021/09/29/just-released-from-stata-
press-multilevel-and-longitudinal-modeling-using-stata-
fourth-edition/
Blog . . Customizable tables, part 6: Tables for multiple regression models
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Huber
9/21 http://blog.stata.com/2021/09/02/customizable-tables-in-
stata-17-part-6-tables-for-multiple-regression-models/
Blog . . . . . Customizable tables, part 5: Tables for one regression model
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Huber
8/21 http://blog.stata.com/2021/08/26/customizable-tables-in-
stata-17-part-5-tables-for-one-regression-model/
Blog . Just released: Interpreting & Visualizing Regression Models, 2nd Ed.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Ksionda
12/20 http://blog.stata.com/2020/12/08/just-released-from-stata-
press-interpreting-and-visualizing-regression-models-using-
stata-second-edition/
Blog . . Cal. power using Monte Carlo sim.: Linear and logistic regression
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Huber
8/19 http://blog.stata.com/2019/08/13/calculating-power-using-
monte-carlo-simulations-part-3-linear-and-logistic-regression/
Blog . . . . . . . . . Exploring results of nonparametric regression models
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. MacDonald
6/18 http://blog.stata.com/2018/06/18/exploring-results-of-
nonparametric-regression-models/
Blog . . . . . . . . . . . . . . Ermistatas and Stata’s new ERMs commands
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Gould
3/18 http://blog.stata.com/2018/03/27/ermistatas-and-statas-new-
erms-commands/
Blog Bayesian logistic regression with Cauchy priors using the bayes prefix
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Balov
9/17 http://blog.stata.com/2017/09/08/bayesian-logistic-
regression-with-cauchy-priors-using-the-bayes-prefix/
Blog . . . . Nonparametric regression: Like parametric regression, but not
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Pinzon
6/17 http://blog.stata.com/2017/06/27/nonparametric-regression-
like-parametric-regression-but-not/
Blog . . . . . . . . . . Solving missing data problems using IPW estimators
. . . . . . . . . . . . . . . . . . . . . . C. Lindsey and J. Luedicke
10/16 http://blog.stata.com/2016/10/11/solving-missing-data-
problems-using-inverse-probability-weighted-estimators/
Blog . . Quantile regression allows covariate effects to differ by quantile
. . . . . . . . . . . . . . . . . . . . . . . . . . . . D. M. Drukker
9/16 http://blog.stata.com/2016/09/27/quantile-regression-allows-
covariate-effects-to-differ-by-quantile/
Blog . . . . . . . . . . . . . . . . Cointegration or spurious regression?
. . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Rajbhandari
9/16 http://blog.stata.com/2016/09/06/cointegration-or-spurious-
regression/
Blog . Exact matching on discrete covariates is the same as reg. adjustment
. . . . . . . . . . . . . . . . . . . . . . . . . . . . D. M. Drukker
8/16 http://blog.stata.com/2016/08/16/exact-matching-on-discrete-
covariates-is-the-same-as-regression-adjustment/
Blog . . . . . . Handling factor variables in a poisson command using Mata
. . . . . . . . . . . . . . . . . . . . . . . . . . . . D. M. Drukker
2/16 http://blog.stata.com/2016/02/17/programming-an-estimation-
command-in-stata-handling-factor-variables-in-a-poisson-
command-using-mata/
Blog . . . Testing model specification and using the program version of gmm
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Lindsey
2/16 http://blog.stata.com/2016/02/11/testing-model-specification-
and-using-the-program-version-of-gmm/
Blog . . . . . . . . . . . . . . . . . . . . . . regress, probit, or logit?
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Pinzon
1/16 http://blog.stata.com/2016/01/14/regress-probit-or-logit/
Blog . . . . . . . . . . Introduction to treatment effects in Stata: Part 1
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Huber
07/15 http://blog.stata.com/2015/07/07/introduction-to-treatment-
effects-in-stata-part-1/
Blog . . . . . . . . . . . . Use poisson rather than regress; tell a friend
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Gould
9/11 http://blog.stata.com/2011/08/22/
use-poisson-rather-than-regress-tell-a-friend/
FAQ . . . . . Pooling data and performing Chow tests in linear regression
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Gould
5/15 How can I pool data (and perform Chow tests) in
linear regression without constraining the residual
variances to be equal?
http://www.stata.com/support/faqs/statistics/pooling-data-
and-chow-tests/
FAQ . . . . . . . . . . . . . . . . . . Two-stage least-squares regression
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Wiggins
5/15 Must I use all of my exogenous variables as instruments
when estimating instrumental variables regression?
http://www.stata.com/support/faqs/statistics/instrumental-
variables-regression/
FAQ . . . . . . . . . . . . . . . . Logistic regression with grouped data
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Sribney
4/15 How can I do logistic regression or multinomial
logistic regression with grouped data?
http://www.stata.com/support/faqs/statistics/logistic-
regression-with-grouped-data/
FAQ . . . . Fitting a linear regression with interval constraints using nl
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Canette
4/15 How do I fit a linear regression with interval
(inequality) constraints in Stata?
http://www.stata.com/support/faqs/statistics/
linear-regression-with-interval-constraints/
FAQ . . . . . . . . . . . . Fitting a regression with interval constraints
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Canette
6/13 How do I fit a regression with interval
constraints in Stata?
http://www.stata.com/support/faqs/statistics/regression-with-
interval-constraints/
FAQ . . . . . . . . The appropriate command for matched case-control data
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Sribney
7/11 Can I do n:1 matching with the mcc command?
http://www.stata.com/support/faqs/statistics/matched-case-
control/
FAQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chow tests
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Gould
7/11 Can you explain Chow tests?
https://www.stata.com/support/faqs/statistics/chow-tests/
FAQ . . . . . . . . . . . . . . Comparing xtgls with regress, vce(cluster)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Wiggins
7/11 How does xtgls differ from regression clustered with
robust standard errors?
http://www.stata.com/support/faqs/statistics/xtgls-versus-
regress/
FAQ . . . . Failure time, censoring time, and entry time in the Cox model
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Gould
7/11 Why can't a subject die at time 0?
Why can't a subject enter and die at the same time?
http://www.stata.com/support/faqs/statistics/time-and-cox-
model/
FAQ . . . . Inst. var. triangular/recursive system with corr. disturbances
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Sanchez
7/11 How do I estimate recursive systems using a subset
of available instruments?
http://www.stata.com/support/faqs/statistics/instrumental-
variables-for-recursive-systems/
FAQ . . . . Interpreting coefficients when interactions are in your model
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Higbee
7/11 Why do I see different p-values, etc., when I change
the base level for a factor in my regression?
Why does the p-value for a term in my ANOVA not agree
with the p-value for the coefficient for that term in
the corresponding regression?
http://www.stata.com/support/faqs/statistics/interpreting-
coefficients/
FAQ . . . . . . . . . . . . . . Negative and missing R-squared for 2SLS/IV
. . . . . . . . . . . . . . . . W. Sribney, V. Wiggins and D. Drukker
7/11 For two-stage least-squares (2SLS/IV/ivregress)
estimates, why is the R-squared statistic not
printed in some cases?
For two-stage least-squares (2SLS/IV/ivregress)
estimates, why is the model sum of squares
sometimes negative?
For two-stage least-squares (3SLS/IV/reg3)
estimates, why are the R-squared and model sum
of squares sometimes negative?
http://www.stata.com/support/faqs/statistics/two-stage-least-
squares/
FAQ . . . . . . . . . . . . . . . . . . Problems with stepwise regression
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Sribney
7/11 What are some of the problems with stepwise regression?
http://www.stata.com/support/faqs/statistics/stepwise-
regression-problems/
FAQ . Relation btw official mi & community-contributed ice & mim commands
. . . . . . . . . . . . . . . . . . . . . Y. Marchenko and P. Royston
7/11 What is the relation between the official multiple-
imputation command, mi, and the community-contributed
ice and mim commands?
http://www.stata.com/support/faqs/statistics/mi-versus-ice-
and-mim/
FAQ . . . . . Within group collinearity in conditional logistic regression
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Gould
7/11 Why does clogit sometimes report a coefficient but
missing value for the standard error, confidence
interval, etc.?
Why is there no intercept in the clogit model?
Why can't I use covariates that are constant
within panel?
http://www.stata.com/support/faqs/statistics/within-group-
collinearity-and-clogit/
FAQ . . . . . . xtreg with the mle option versus xtreg with the re option
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Wiggins
7/11 Why does xtreg with the mle option produce different
results from xtreg with only the re option?
http://www.stata.com/support/faqs/statistics/xtreg-mle-
versus-gmm/
FAQ . Bivariate probit with partial observability & single dependent var.
. . . . . . . . . . . . . . . . . . . . . . . . V. Wiggins and B. Poi
8/10 How do I fit a bivariate probit model with partial
observability and only one dependent variable?
http://www.stata.com/support/faqs/statistics/bivariate-probit-
with-partial-observability/
FAQ . . . . . . . . . . . . . . Stepwise regression with the svy commands
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Sribney
7/09 Is there a way in Stata to do stepwise regression with
svy: logit or any of the svy commands?
http://www.stata.com/support/faqs/statistics/stepwise-
regression-with-svy-commands/
FAQ . . . Comparison of std errors for robust, cluster, and std estimators
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Sribney
7/09 How can the standard errors with the
vce(cluster clustvar) option be smaller than those
without the vce(cluster clustvar) option?
http://www.stata.com/support/faqs/statistics/standard-errors-
and-vce-cluster-option/
FAQ . . . . . . . . . . . . Relationship between ordered probit and probit
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Gould
7/09 Is it possible to include a constant term (intercept)
in ordered probit model within Stata?
What is the relationship between ordered probit and
probit?
http://www.stata.com/support/faqs/statistics/ordered-probit-
and-probit/
FAQ . . . . . . . . . . . . . . . . . . . . . . . . . Chow and Wald tests
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Gould
8/07 How can I do a Chow test with the robust variance
estimates, that is, after estimating with
regress, vce(robust)?
http://www.stata.com/support/faqs/statistics/chow-and-wald-
tests/
FAQ . . . . . . Prediction confidence intervals after logistic regression
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Inlow
7/07 How do I obtain confidence intervals for the predicted
probabilities after logistic regression?
http://www.stata.com/support/faqs/statistics/prediction-
confidence-intervals/
FAQ . . . . . . . . . . . . A terminology problem: odds ratio versus odds
. . . . . . . . . . . . . . . . . . . . . . . . W. Gould and J. Hardin
5/05 Why does the manual claim that the odds ratio is constant
in a logistic regression?
http://www.stata.com/support/faqs/statistics/odds-ratio-
versus-odds/
FAQ . . . . . . . . . . . . . . . . . . . . The variance function in nbreg
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Gutierrez
10/01 How do you specify the variance function in nbreg
to coincide with Cameron and Trivedi's (Regression
analysis of count data, page 62) NB1 and NB2 variance
functions?
What is the difference between the models fit using
nbreg, dispersion(mean) and nbreg, dispersion(constant)?
http://www.stata.com/support/faqs/statistics/nbreg-variance-
function/
FAQ . . . . . . Obtaining the standard error of the regression with streg
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Gould
3/01 How can I obtain the standard error of the regression
with streg?
http://www.stata.com/support/faqs/statistics/standard-error-
with-streg/
FAQ . . . . . . . Clarification on analytic weights with linear regression
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Gould
1/99 What is the effect of specifying aweights with regress?
http://www.stata.com/support/faqs/statistics/analytical-
weights-with-linear-regression/
FAQ . . . . . . . . . . . . . Advantages of the robust variance estimator
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Sribney
1/98 What are the advantages of using the robust variance
estimator over the standard maximum-likelihood variance
estimator in logistic regression?
http://www.stata.com/support/faqs/statistics/robust-variance-
estimator/
FAQ . . . . . . Interpreting "outcome does not vary" when running logistic
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Lin
11/96 Why do I get the message "outcome does not vary" when
I perform a logistic or logit regression?
http://www.stata.com/support/faqs/statistics/outcome-does-not-
vary/
FAQ . . . . . . . . . . . Create and export a table of regression results
3/24 How can I easily create and export a table of
regression results from Stata to other formats?
http://www.stata.com/support/faqs/reporting/
export-regression-results-table-formats/
FAQ . . . . . . . . . . . . . . . . . Visual overview for creating graphs
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Wagner
7/06 What kind of graphs can I create in Stata?
https://www.stata.com/support/faqs/graphics/gph/stata-graphs/
FAQ . . . . . . . . . . . . . . . What statistical analysis should I use?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
5/08 https://stats.idre.ucla.edu/stata/whatstat/what-statistical-
analysis-should-i-usestatistical-analyses-using-stata/
FAQ . Test of overall survey regression model in Stata vs. SAS and SUDAAN
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
8/12 https://stats.idre.ucla.edu/stata/faq/adjusted_wald_test/
FAQ . . . . . . . . . How can I understand a 3-way continuous interaction?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
9/11 https://stats.idre.ucla.edu/stata/faq/how-can-i-understand-a-
3-way-continuous-interaction-stata-12/
FAQ . . . . . Continuous by continuous interaction in logistic regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
8/11 https://stats.idre.ucla.edu/stata/faq/how-can-i-understand-a-
continuous-by-continuous-interaction-in-logistic-regression-
stata-12/
FAQ . . . . . Categorical by continuous interaction in logistic regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
8/11 https://stats.idre.ucla.edu/stata/faq/how-can-i-understand-a-
categorical-by-continuous-interaction-in-logistic-regression-
stata-12/
FAQ . . . . Categorical by categorical interaction in logistic regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
8/11 https://stats.idre.ucla.edu/stata/faq/how-can-i-understand-a-
categorical-by-categorical-interaction-in-logistic-regression-
stata-12/
FAQ . . . . How can I understand a categorical by continuous interaction?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
8/11 https://stats.idre.ucla.edu/stata/faq/how-can-i-understand-a-
categorical-by-continuous-interaction-stata-12/
FAQ . . . . . . How can I explain a continuous by continuous interaction?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
8/11 https://stats.idre.ucla.edu/stata/faq/how-can-i-explain-a-
continuous-by-continuous-interaction-stata-12/
FAQ . . . . Omit the main effect in a regression model with an interaction
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
https://stats.oarc.ucla.edu/stata/faq/what-happens-if-you-
omit-the-main-effect-in-a-regression-model-with-an-
interaction/
FAQ . . . . . . . . . . . How can I generate fungible regression weights?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
12/10 https://stats.idre.ucla.edu/stata/faq/fungible/how-can-i-
generate-fungible-regression-weights/
FAQ More of how to use margins to understand multiple interactions in reg.
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
3/10 https://stats.idre.ucla.edu/stata/faq/more-of-how-can-i-use-
the-margins-command-to-understand-multiple-interactions-in-
regression-stata/
FAQ . . . How can I do mediation analysis with a categorical IV in Stata?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
3/10 https://stats.idre.ucla.edu/stata/faq/how-can-i-do-mediation-
analysis-with-a-categorical-iv-in-stata/
FAQ . . . . . . . . . . How can I do simple main effects using anovalator?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
2/10 https://stats.idre.ucla.edu/stata/faq/how-can-i-do-simple-
main-effects-using-anovalator-stata/
FAQ . . . . How to get anova main-effects with dummy coding using margins?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
2/10 https://stats.idre.ucla.edu/stata/faq/how-can-get-anova-main-
effects-with-dummy-coding-using-margin-stata/
FAQ Use margins to understand multiple interactions in logistic regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
12/09 https://stats.idre.ucla.edu/stata/faq/how-can-i-use-the-
margins-command-to-understand-multiple-interactions-in-
logistic-regression-stata/
FAQ How to use margins to understand multiple interactions in reg. & anova
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
12/09 https://stats.idre.ucla.edu/stata/faq/how-can-i-use-the-
margins-command-to-understand-multiple-interactions-in-
regression-and-anova-stata-11/
FAQ . . . . . Identify cases used by an estimation command using e(sample)
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
8/09 https://stats.idre.ucla.edu/stata/faq/how-can-i-identify-
cases-used-by-an-estimation-command-using-esample/
FAQ . . . . . . . . How can I compute effect size in Stata for regression?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
3/09 https://stats.idre.ucla.edu/stata/faq/how-can-i-compute-
effect-size-in-stata-for-regression/
FAQ . . . . How can I get a Somers' D after logistic regression in Stata?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
3/09 https://stats.idre.ucla.edu/stata/faq/how-can-i-get-a-somers-
d-after-logistic-regression-in-stata/
FAQ . . . . . . How can I get an R-squared with robust regression (rreg)?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-get-an-r2-
with-robust-regression-rreg/
FAQ . . . . . . . How to get anova simple main effects with dummy coding?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
9/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-get-anova-
simple-main-effects-with-dummy-coding/
FAQ . . . . . . . . How do I interpret odds ratios in logistic regression?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
8/08 https://stats.idre.ucla.edu/stata/faq/how-do-i-interpret-
odds-ratios-in-logistic-regression/
FAQ . . . . . . . . How can I check for collinearity in survey regression?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-check-for-
collinearity-in-survey-regression/
FAQ . . . . . . . . . . . . . . . How can I do a t-test with survey data?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-do-a-t-test-
with-survey-data/
FAQ . Generating pred. counts from a zip or zinb model based on para. est.
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-manually-
generate-the-predicted-counts-from-a-zip-or-zinb-model-based-
on-the-parameter-estimates/
FAQ . . Est. relative risk using glm for common outcomes in cohort studies
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/07 https://stats.idre.ucla.edu/stata/faq/how-can-i-estimate-
relative-risk-using-glm-for-common-outcomes-in-cohort-studies/
FAQ . . . . . . . . . . . . . . How does xtreg, re differ from xtreg, fe?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/what-is-the-difference-
between-xtreg-re-xtreg-fe/
FAQ . What is seemingly unrelated reg. and how can I perform it in Stata?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/what-is-seemingly-
unrelated-regression-and-how-can-i-perform-it-in-stata/
FAQ . . . . . . . . . . . . . . . . How can I analyze count data in Stata?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-analyze-
count-data-in-stata/
FAQ . . How to do regression when the dependent variable is a proportion?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-does-one-do-
regression-when-the-dependent-variable-is-a-proportion/
FAQ . . . . . . . . . How do I interpret quantile regression coefficients?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-do-i-interpret-
quantile-regression-coefficients/
FAQ . . . . . . How can I do a scatterplot with regression line in Stata?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-do-a-
scatterplot-with-regression-line-in-stata/
FAQ . . . . . How can I graphically compare OLS and BLUP results in Stata?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-graphically-
compare-ols-and-blup-results-in-stata/
FAQ . . . . . How can I compare regression coefficients between 2 groups?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-compare-
regression-coefficients-between-2-groups/
FAQ . How can I compare regression coefficients across 3 (or more) groups?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-compare-
regression-coefficients-across-3-or-more-groups/
FAQ . . . . . . . . How can I find where to split a piecewise regression?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-find-where-
to-split-a-piecewise-regression/
FAQ Can I make regression tables that look like those in journal articles?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-use-estout-
to-make-regression-tables-that-look-like-those-in-journal-
articles/
FAQ . . . . . . . . . . . . How can I run a piecewise regression in Stata?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/how-can-i-run-a-
piecewise-regression-in-stata/
FAQ . . . . . . . . Comparing various methods of analyzing clustered data
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/faq/what-are-the-some-of-
the-methods-for-analyzing-clustered-data-in-stata/
FAQ . . . . . . . . . How can I do regression estimation with survey data?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/05 https://stats.idre.ucla.edu/stata/faq/how-can-i-do-
regression-estimation-with-survey-data/
FAQ . . . . . . . . . . . . How do I use the Stata survey (svy) commands?
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
5/05 https://stats.idre.ucla.edu/stata/faq/how-do-i-use-the-stata-
survey-svy-commands/
FAQ . . . . . . . . . . . . . . . . . Annotated output: Robust regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/output/robust-regression/
FAQ . . . . . . . . . . . . . . . . . Annotated output: Probit regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/output/probit-regression/
FAQ . . . . . . . . . . . . Annotated output: Negative binomial regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
https://stats.oarc.ucla.edu/stata/output/negative-binomial-
regression/
FAQ . . . . . Annotated output: Zero-inflated negative binomial regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/output/zero-inflated-
negative-binomial-regression/
FAQ . . . . . . . . . . . . . . . . Annotated output: Interval regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/output/interval-regression/
FAQ . . . . . . . . . . . . . . . . Annotated output: Truncated regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/output/truncated-regression/
FAQ . . . . . . . . . . . . . . . . . . Annotated output: Tobit regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/output/tobit-regression/
FAQ . . . . . . . . . . Annotated output: Zero-inflated poisson regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/output/zero-inflated-
poisson-regression/
FAQ . . . . . . . . . Annotated output: Zero-truncated poisson regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/output/zero-truncated-
poisson-regression/
FAQ . . . . Annotated output: Zero-truncated negative binomial regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/output/zero-truncated-
negative-binomial-regression/
FAQ . . . . . . . . . . . . Annotated output: Negative binomial regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
9/08 https://stats.idre.ucla.edu/stata/output/zero-truncated-
negative-binomial-regression/
FAQ . . . . . . . . . Annotated Stata output: Logistic regression analysis
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/output/logistic-regression-
analysis/
FAQ . . . . . . . Annotated Stata output: Multinomial logistic regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
https://stats.oarc.ucla.edu/stata/output/multinomial-logistic-
regression/
FAQ . . . . . . . . . . . . Annotated output: Ordered logistic regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/output/ordered-logistic-
regression/
FAQ . . . . . . . . . . . . . . . . . Annotated output: Poisson regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/output/poisson-regression/
FAQ . . . . . . . . . . . . . Annotated Stata output: Regression analysis
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
https://stats.oarc.ucla.edu/stata/output/regression-analysis/
Example . . . . . Textbook: Econometric Analysis of Cross Section & Panel Data
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
2/08 examples from the book Econometric Analysis of Cross
Section and Panel Data by Jeffrey M. Wooldridge
https://stats.idre.ucla.edu/other/examples/eacspd/
Example . . . . Textbook examples: Applied Logistic Regression (2nd Edition)
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
2/08 examples from the book Applied Logistic Regression
(2nd Edition) by David Hosmer and Stanley Lemeshow
https://stats.idre.ucla.edu/other/examples/alr2/
Example . Applied Longitudinal Data Anal.: Modeling Change & Event Occurrence
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
2/08 examples from the book Applied Longitudinal Data
Analysis: Modeling Change and Event Occurrence
by Judith D. Singer and John B. Willett
https://stats.idre.ucla.edu/other/examples/alda/
Example . . . . . . Textbook examples: An Introduction to Categorical Analysis
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
9/07 examples from the book An Introduction to
Categorical Analysis by Alan Agresti
https://stats.idre.ucla.edu/other/examples/icda/
Example . . . . . . . . . . . . . . . Stata web books: Regression with Stata
. . Chen, Ender, Mitchell & Wells (UCLA Academic Technology Services)
6/07 web book Regression with Stata by (in alphabetical
order) Xiao Chen, Philip B. Ender, Michael Mitchell
& Christine Wells
https://stats.idre.ucla.edu/stata/webbooks/reg/
Example . . . . . . . . . . . . Textbook examples: Intro Multilevel Modeling
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
4/07 examples from the book Intro Multilevel Modeling
by Kreft & de Leeuw
https://stats.idre.ucla.edu/other/examples/imm/
Example . Textbook examples: Multilevel Analysis: Techniques and Applications
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
3/07 examples from the book Multilevel Analysis:
Techniques and Applications by Joop Hox
https://stats.idre.ucla.edu/other/examples/ma-hox/
Example . . . . . . . . . . . Stata web books: Logistic Regression with Stata
. . Chen, Ender, Mitchell & Wells (UCLA Academic Technology Services)
9/06 web book Logistic Regression with Stata by (in
alphabetical order) Xiao Chen, Philip B. Ender,
Michael Mitchell & Christine Wells
https://stats.idre.ucla.edu/stata/webbooks/logistic/
Example . . . Applied Regression Analysis, Linear Models, and Related Methods
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
9/06 examples from the book Applied Regression Analysis,
Linear Models, and Related Methods by John Fox
https://stats.idre.ucla.edu/other/examples/ara/
Example . . Textbook examples: Regression Analysis by Example, Third Edition
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/06 examples from the book Regression Analysis by
Example, Third Edition by Samprit Chatterjee,
Ali S. Hadi & Bertram Price
https://stats.idre.ucla.edu/other/examples/chp/
Example . Applied Survival Analysis: Regression Modeling of Time to Event Data
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
5/06 examples from the book Applied Survival Analysis:
Regression Modeling of Time to Event Data
by David W. Hosmer, Jr. and Stanley Lemeshow
https://stats.idre.ucla.edu/other/examples/asa2/
Example . . . . . . Textbook examples: Elementary Survey Sampling, 5th Edition
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/05 examples from the book Elementary Survey Sampling,
Fifth Edition by Richard Scheaffer, William
Mendenhall, & Lyman Ott
https://stats.idre.ucla.edu/other/examples/ess5/
Example . . . Textbook Examples: Methods Matter: Improving Causal Inference
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
11/10 examples from the book Methods Matter: Improving Causal
Inference in Educational and Social Science Research
by Richard J. Murnane and John B. Willett
https://stats.idre.ucla.edu/other/examples/methods-matter/
Example . . . . . . . Textbook Examples: Econometric Analysis, Fourth Edition
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/05 examples from the book Econometric Analysis, Fourth
Edition by William Greene
https://stats.idre.ucla.edu/other/examples/greene/
Example . . . . . . . . . . . Textbook examples: Sampling: Design and Analysis
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
4/05 examples from the book Sampling: Design and
Analysis by Sharon L. Lohr
https://stats.idre.ucla.edu/stata/examples/lohr/
Example . . . . . . Data analysis examples: Mixed effects logistic regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
2/13 https://stats.idre.ucla.edu/stata/dae/mixed-effects-logistic-
regression/
Example . . Data analysis examples: Zero-inflated negative binomial regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/dae/zero-inflated-negative-
binomial-regression/
Example . . . . . . Data analysis examples: Multiple regression power analysis
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
10/08 https://stats.idre.ucla.edu/stata/dae/multiple-regression-
power-analysis/
Example . . . . . . . Data analysis examples: Multivariate regression analysis
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/multivariate-regression-
analysis/
Example . . . . . . . . . . . . . . Data analysis examples: Poisson regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/poisson-regression/
Example . . . . . . . . . Data analysis examples: Negative binomial regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/negative-binomial-
regression/
Example . . . . . . . Data analysis examples: Zero-inflated Poisson regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/zero-inflated-poisson-
regression/
Example . . . . . . Data analysis examples: Zero-truncated poisson regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/zero-truncated-poisson-
regression/
Example . . . . . . . Data analysis examples: Zero-truncated negative binomial
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/zero-inflated-negative-
binomial-regression/
Example . . . . . . . . . . . . . Data analysis examples: Interval regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/interval-regression/
Example . . . . . . . . . . . . . Data analysis examples: Truncated regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/truncated-regression/
Example . . . . . . . . . . Data analysis examples: Exact logistic regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/exact-logistic-
regression/
Example . . . . . . . . . . . . . . Data analysis examples: Robust regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/robust-regression/
Example . . . . . . . . . . . . . . Data analysis examples: Probit regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/probit-regression/
Example . . . . . . . . . Data analysis examples: Ordinal logistic regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/ordered-logistic-
regression/
Example . . . . . . . . . Data analysis examples: Multinomial logit regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/multinomiallogistic-
regression/
Example . . . . . . . . . . . . . . . Data analysis examples: Logit regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/logistic-regression/
Example . . Data analysis examples: Two independent proportions power analysis
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/dae/two-independent-
proportions-power-analysis/
Example . . . . . . . Stata Analysis Tools: Weighted Least Squares Regression
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/ado/analysis/stata-analysis-
toolsweighted-least-squares-regression/
Example . . . . . . . . . . . . . . . . . . . . Useful non-UCLA Stata programs
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/ado/world/
Example . . . . . . . . . . . . . . . . . . . . Seminar: Regression with Stata
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
https://stats.oarc.ucla.edu/stata/seminars/stata-regression/
Example . . . . . . . . . . . . . . . Seminar: Logistic regression with Stata
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
5/07 seminar to help increase skills using logistic
regression analysis
https://stats.idre.ucla.edu/stata/seminars/stata-logistic/
Example . . . Seminar: Beyond binary: Multinomial logistic regression in Stata
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
6/06 https://stats.idre.ucla.edu/stata/seminars/stata-
beyondbinarylogistic/beyond-binary-multinomial-logistic-
regression-in-stata/
Example . . . . . Seminar: Beyond binary: Ordinal logistic regression in Stata
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
3/06 https://stats.idre.ucla.edu/stata/seminars/stata-
beyondbinarylogistic/beyond-binary-ordinal-logistic-
regression-in-stata/
Example . . . . . . . . Stata learning module: A statistical sampler in Stata
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
7/08 https://stats.idre.ucla.edu/stata/modules/a-statistical-
sampler-in-stata/
Example . . . Stata learning module: Graphics: Combining twoway scatterplots
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
8/03 https://stats.idre.ucla.edu/stata/modules/graph8/twoway-
scatter-combine/
Example . . . . . . Fitting a seemingly unrelated regression (sureg) manually
. . . . . . . . . . . . . . . . . . UCLA Academic Technology Services
5/09 https://stats.idre.ucla.edu/stata/code/fitting-a-seemingly-
unrelated-regression-sureg-manually/
SJ-25-1 st0765 . . . . . . . . . . . . . . . . . . . . Binscatter regressions
. . . . . . . M. D. Cattaneo, R. K. Crump, M. H. Farrell, and Y. Feng
(help binsreg, binslogit, binsprobit, binsqreg, binstest,
binspwc, binsregselect if installed)
Q1/25 SJ 25(1):3--50
implements the binscatter methods developed by Cattaneo
et al. (2024a, arXiv:2407.15276 [stat.EM]; 2024b, American
Economic Review 114: 1488–1514)
SJ-25-1 st0766 . . . . . . . . . . . gintreg: Generalized interval regression
. . . . . . . . . . . . . . . . . . . . J. B. McDonald and J. Triplett
(help gintreg, gintregplot if installed)
Q1/25 SJ 25(1):51--76
provides a partially adaptive maximum-likelihood estimation
procedure that 1) generalizes the intreg command by relaxing
the normality assumption and 2) draws from a library of
flexible distributional forms
SJ-25-1 st0767 . Est. & visualization in the linear panel event-study design
. S. Freyaldenhoven, C. B. Hansen, J. Pérez Pérez, and J. M. Shapiro
(help xtevent, xteventplot, xteventtest, get_unit_time_effects
if installed)
Q1/25 SJ 25(1):97--135
fits linear panel models and visualizes the results in
event-study plots
SJ-25-1 st0768 . . . . . . . . . . . . . . xtpb: The pooled Bewley estimator
(help xtpb if installed) . . . P. Asnani, A. Chudik, and B. Strackman
Q1/25 SJ 25(1):136--150
implements the Chudik, Pesaran, and Smith (Forthcoming,
Econometrics and Statistics,
https://doi.org/10.1016/j.ecosta.2023.11.001)
pooled Bewley (PB) estimator of long-run relationships in
dynamic heterogeneous panel-data models
SJ-25-1 st0769 . . . . . . . . . . . . Establishing reference interval bounds
. . . . . . . . . . . . N. H. Bruun, N. M. U. Torp, and S. L. Andersen
(help ros if installed)
Q1/25 SJ 25(1):151--168
estimates upper reference bounds for a dataset with possibly
nondetectable or censored values and possible contamination
in either end
SJ-25-1 st0770 . . . beyondpareto for optimal extreme-value index estimation
J. König, C. Schluter, C. Schröder, I. Retter, and M. Beckmannshagen
(help beyondpareto, pqqplot if installed)
Q1/25 SJ 25(1):169--188
provides command which estimates the extreme-value index
for distributions that are Pareto-like, that is, whose upper
tails are regularly varying and eventually become Pareto
SJ-25-1 st0773 . . . . . . . . . . . Maximum agreement regression with magreg
(help magreg if installed) . . . . . . . . . . . . . . . . M. Bottai
Q1/25 SJ 25(1):237--243
provides a command for estimating the coefficients of
maximum-agreement regression models for an outcome variable
given covariates
SJ-24-4 st0762 . . . . . . . . . . . . . Cluster-weighted models using Stata
. . . . . . . . . . . . . D. Spinelli, S. Ingrassia, and G. Vittadini
(help cwmglm, cwmglm_postestimation if installed)
Q4/24 SJ 24(4):711--745
fits cluster-weighted models based on the most common
generalized linear models with random covariates
SJ-24-3 st0751 . . Getting away from the cutoff in reg. discontinuity designs
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Palomba
(help getaway, ciasearch, ciatest, ciares, ciacs, getawayplot
if installed)
Q3/24 SJ 24(3):371--401
implements the method proposed by Angrist and Rokkanen (2015,
Journal of the American Statistical Association 110: 1331-1344)
to extrapolate treatment-effect estimates "away from the
cutoff", relying on a classical unconfoundedness condition
SJ-24-2 st0745 . . . . . . . . . . . . Two-step analysis of hierarchical data
(help twostep if installed) . . . . . . . . J. Giesecke and U. Kohler
Q2/24 SJ 24(2):213--249
provides a bundle of programs to perform analyses of
hierarchical data applying the two-step approach
SJ-24-2 st0747 . . . First-stage analysis for instr.-variables quantile reg.
. . . . . . . . . . . . . J. Alejo, A. F. Galvao, and G. Montes-Rojas
(help fsivqreg if installed)
Q2/24 SJ 24(2):273--286
presents a first-stage linear regression command, fsivqreg,
for an instrumental-variables quantile regression model
SJ-24-2 st0748 . . Estimating Skellam distribution and regression parameters
(help skellamreg if installed) . . . . . V. Verardi and C. Vermandele
Q2/24 SJ 24(2):287--300
estimates the parameters of a Skellam distribution and
Skellam regression model
SJ-24-1 dm0112_1 . . . . . . . . . . . . . . . . . Software update for txtreg
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Schwarz
(help txtreg_train, txtreg_predict, txtreg_analyze if installed)
Q1/24 SJ 24(1):182--184
fixes a bug that prevented the running of the commands in some
versions of the scikit-learn Python package
SJ-23-4 st0733 Leverage, influence, & the jackknife in clustered reg. models
. . . . . . . . . . . . J. G. MacKinnon, M. O. Nielsen, and M. D. Webb
(help summclust if installed)
Q4/23 SJ 23(4):942--982
summarizes the cluster structure of the dataset for linear
regression models with clustered disturbances
SJ-23-4 gn0097 . . . Review of Microeconometrics Using Stata, Second Edition
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Kripfganz
Q4/23 SJ 23(4):1062--1073 (no commands)
book review of Microeconometrics Using Stata, Second Edition,
by A. Colin Cameron and Pravin K. Trivedi (2022, Stata Press)
SJ-23-4 gn0098 . Review of A Gentle Introduction to Stata, Rev. Sixth Edition
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Musau
Q4/23 SJ 23(4):1074--1081 (no commands)
book review of A Gentle Introduction to Stata, Revised Sixth
Edition, by Alan Acock (2023, Stata Press)
SJ-23-3 st0724 . . . . . . . . . . . . . . . . . . Robit regression in Stata
(help robit if installed) . . . . . . . . R. B. Newson and M. Falcaro
Q3/23 SJ 23(3):658--682
implements robit regression which replaces the underlying
normal distribution in the probit model with a Student's t
distribution and has been advocated as being particularly
suitable for estimating inverse-probability weights and
propensity scoring more generally
SJ-23-3 dm0112 . . . . . . . . Estimating text regressions using txtreg_train
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Schwarz
(help txtreg_train, txtreg_predict, txtreg_analyze if installed)
Q3/23 SJ 23(3):799--812
estimates text regressions for continuous, binary, and
categorical variables based on text strings
SJ-23-3 gn0094 . . Review of Multilevel and Longitudinal Modeling Using Stata
. . . . . . . . . . . . . . . . . . . . . L. Grilli and C. Rampichini
3/23 SJ 23(3):901--904 (no commands)
reviews Multilevel and Longitudinal Modeling Using Stata,
Fourth Edition, by Rabe-Hesketh and Skrondal (2022, Stata Press)
SJ-23-2 st0717 . . . . . . . . . Pseudo-observations in a multistate setting
. . . . . . . . . . . . M. Overgaard, P. K. Andersen, and E. T. Parner
(help stpmstate if installed)
Q2/23 SJ 23(2):491--517
calculates pseudo-observations in a multistate setting based
on the Aalen–Johansen estimator
SJ-23-1 st0703 . . . . . . . . . . . acreg: Arbitrary correlation regression
. . . . . . . . . F. Colella, R. Lalive, S. O. Sakalli, and M. Thoenig
(help acreg if installed)
Q1/23 SJ 23(1):119--147
implements the arbitrary clustering correction of standard
errors proposed in Colella et al. (2019, IZA discussion
paper 12584)
SJ-23-1 gr0009_2 . . . . Software update for model diagnostic graph commands
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. J. Cox
(help anovaplot, indexplot, modeldiag, ofrtplot, ovfplot,
qfrplot, racplot, rdplot, regplot, rhetplot, rvfplot2,
rvlrplot, rvpplot2 if installed)
Q1/23 SJ 23(1):298--299
various major and minor updates have been made to the
command and documentation
SJ-22-4 st0693 . . . . . . . rcm: A command for the regression control method
(help rcm if installed) . . . . . . . . . . . . . . G. Yan and Q. Chen
Q4/22 SJ 22(4):842--883
efficiently implements the regression control method with
or without covariates
SJ-22-4 st0097_3 . . . . . . . . . . . . . . . . Software update for gologit2
(help gologit2 if installed) . . . . . . . . . . . . . . R. Williams
Q4/22 SJ 22(4):1004
includes several enhancements
SJ-22-3 st0684 . . . . . . . Bunching estimation of elasticities using Stata
. . . . . . . . M. Bertanha, A. H. McCallum, A. Payne, and N. Seegert
(help bunching, bunchbounds, bunchtobit, bunchfilter if installed)
Q3/22 SJ 22(3):597--624
implements new nonparametric and semiparametric
identification methods for estimating elasticities
developed by Bertanha, McCallum, and Seegert (2021,
Technical Report 2021-002, Board of Governors of the
Federal Reserve System)
SJ-22-3 st0690 . . . . Dynamic panel regression under irregular time spacing
(help xtusreg if installed) . . . . . . . . . . . Y. Sasaki and Y. Xin
Q3/22 SJ 22(3):713--724
estimates parameters of fixed-effects dynamic panel
regression models under unequal time spacing
SJ-22-2 st0676 . . . . . Smoothed instrumental variables quantile regression
(help sivqr if installed) . . . . . . . . . . . . . . . . D. M. Kaplan
Q2/22 SJ 22(2):379--403
estimates the coefficients of the instrumental variables
quantile regression model introduced by Chernozhukov and
Hansen (2005, Econometrica 73: 245–261)
SJ-22-2 st0679 Stata tip 146: Using margins after a Poisson regression model
. . . . . . . . . . . . . . . M. Falcaro, R. B. Newson, and P. Sasieni
Q2/22 SJ 22(2):460--464 (no commands)
tip on using margins after a Poisson regression model to
estimate the number of events prevented by an intervention
SJ-22-1 st0666 . . . . . Binary contrasts for unordered polytomous regressors
(help binarycontrast if installed) . . . . . J. Freese and S. Johfre
Q1/22 SJ 22(1):125--133
computes the binary contrasts for factor variables from the
most recently fitted model
SJ-22-1 st0668 . . . . . . . . . . . . . Analyzing coarsened categorical data
(help pccfit, pccprob if installed) W. Vach, C. Alder, and S. Pichler
Q1/22 SJ 22(1):158--194
facilitates maximum likelihood estimation in the situation
of coarsened categorical data with or without probabilistic
information for a wide range of parametric models for
categorical outcomes -- in the cases both of a nominal and
an ordinal scale
SJ-22-1 st0376_3 . . . . . . . . . . . . . . . . . . Software update for strs
(help strs if installed) . . . . . . . P. W. Dickman and E. Coviello
Q1/22 SJ 22(1):238--241
several enhancements to the strs command
SJ-21-4 st0657 . . . . . . . Implementing quantile selection models in Stata
(help qregsel if installed) . . . . . . . . E. Munoz and M. Siravegna
Q4/21 SJ 21(4):952--971
implements a copula-based sample-selection correction for
quantile regression recently proposed by Arellano and
Bonhomme (2017, Econometrica 85: 1–28)
SJ-21-4 gn0089 Review of Interpreting & Visualizing Regression Models, 2nd Ed
. . . . . . . . . . . . . . . . . . . . . . A. MacIsaac and B. Weaver
Q4/21 SJ 21(4):1034--1046 (no commands)
reviews Interpreting and Visualizing Regression Models
Using Stata, Second Edition, by Michael N. Mitchell
(2021, Stata Press).
SJ-21-3 st0646 . . . A regression-with-residuals method for causal mediation
(help rwrmed if installed) . . A. Linden, C. Huber, and G. T. Wodtke
Q3/21 SJ 21(3):559--574
performs mediation analysis using the methods proposed by
Wodtke and Zhou (2020, Epidemiology 31: 369-375)
SJ-21-3 st0648 . Arellano & Bonhomme quantile reg. with selection correction
(help arhomme if installed) . . . . . . . . . M. Biewen and P. Erhardt
Q3/21 SJ 21(3):602--625
implements different variants of the Arellano and Bonhomme
(2017) estimator for quantile regression with selection
correction along with standard errors based on bootstrapping
and subsampling
SJ-21-3 st0653 . . . . Instrument-free inference with endogenouse regressors
(help kinkyreg if installed) . . . . . S. Kripfganz and J. F. Kiviet
Q3/21 SJ 21(3):772--813
provides kinky least-squares inference for linear regression
models with endogenous regressors, which adopts an alternative
approach to identification
SJ-21-2 st0638 . . . . . . . . . Weighted mixed-effects dose-response models
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Orsini
(help drmeta, drmeta_graph, drmeta_gof, drmeta_predict if installed)
Q2/21 SJ 21(2):320--347
provides increased understanding of mixed-effects dose-response
models suitable for tables of correlated estimates
SJ-21-2 st0641 . Stacked linear reg. analysis testing across OLS regressions
. . . . . . . . . . . . . . . . . . . M. Oberfichtner and H. Tauchmann
(help stackreg, xtstackreg if installed)
Q2/21 SJ 21(2):411--429
provides tests of hypotheses of parallel form in several
regressions in a more general way (such as allowing panel
data) than is available in commnds suest and mvreg
SJ-21-2 st0644 . . . . . . . . . . . . Bootstrap internal validation command
. . . . . . Fernandez-Felix, Garcia-Esquinas, Muriel, Royuela, Zamora
(help bsvalidation if installed)
Q2/21 SJ 21(2):498--509
provides a bootstrap internal validation of a logistic
regression model
SJ-21-2 st0393_3 . . . . . . . . . . . . . . . . Software update for aidsills
(help aidsills if installed) . . . . . . . S. Lecocq and J.-M. Robin
Q2/21 SJ 21(2):556--557
now allows weights
SJ-21-1 st0630 Consistent estimation of linear reg. models using matched data
(help msreg if installed) . . . M. Hirukawa, D. Liu, and A. Prokhorov
Q1/21 SJ 21(1):123--140
implements two consistent estimators as proposed in Hirukawa
and Prokhorov (2018) for linear regression models using
matched data
SJ-21-1 gn0085 Review of Psychological Statistics & Psychometrics Using Stata
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Wells
Q1/21 SJ 21(1):259--262 (no commands)
book review of Psychological Statistics and Psychometrics
Using Stata, by Scott A. Baldwin
SJ-21-1 st0173_2 Software update for sregress, mmregress, msregress, mregress
. . . . . . . . . . . . . . . . . . . . . . . V. Verardi and C. Croux
Q1/21 SJ 21(1):272
sregress, mmregress, msregress, and mregress have been
superseded by robreg (type ssc install robreg); mcd has
been superseded by robmv (type ssc install robmv)
SJ-21-1 st0259_1 . . . . . . . . . . . . . . . . Software update for smultiv
. . . . . . . . . . . . . . . . . . . . . . V. Verardi and A. McCathie
Q1/21 SJ 21(1):272
smultiv has been superseded by robmv, which can be downoaded
by typing ssc install robmv
SJ-21-1 st0611_1 . . . . . . . . . . . . . . . Software update for ivmediate
(help ivmediate if installed) . C. Dippel, A. Ferrara, and S. Heblich
Q1/21 SJ 21(1):272
changes how matrix elements are called under the full option
to make the syntax compatible with versions prior to Stata 16
SJ-20-4 st0620 . . . . Analysis of RD designs with multiple cutoffs or scores
. . . . . . . . . . . M. D. Cattaneo, R. Titiunik, and G. Vazquez-Bare
(help rdmc, rdmcplot, rdms if installed)
Q4/20 SJ 20(4):866--891
introduces the Stata (and R) package rdmulti, for analyzing
regression-discontinuity (RD) designs with multiple cutoffs
or multiple scores
SJ-20-4 st0585_1 . . . . . . . . . Software update for simarwilson and gciget
. . . . . . . . . . . . . . . . . . . . O. Badunenko and H. Tauchmann
(help simarwilson, ftruncreg if installed)
Q4/20 SJ 20(4):1028--1030
updated to tremendously reduce the run time; the reduction in
computing time is achieved through ftruncreg (included with
this update)
SJ-20-3 st0448_1 . . . . Models of dynamic compositional dependent variables
Y.S. Jung, F.D.S. Souza, A.Q. Philips, A. Rutherford, and G.D. Whitten
(help dynsimpie, cfbplot, effectsplot, dynsimpiecoef if installed)
Q3/20 SJ 20(3):584--603
presents an update to dynsimpie that greatly enhances
the range of models that can be estimated and presented
SJ-20-3 st0611 Causal mediation analysis in instrumental-variables regression
(help ivmediate if installed) . C. Dippel, A. Ferrara, and S. Heblich
Q3/20 SJ 20(3):613--626
estimates causal mediation effects in instrumental-variables
settings using the framework developed by Dippel et al. (2020,
unpublished manuscript)
SJ-20-3 st0612 . Endogenous switching regression model of count-data outcome
(help escount, lncount, teescount if installed) . . . . . . T. Hasebe
Q3/20 SJ 20(3):627--646
estimates an endogenous switching model with count-data
outcomes, where a potential outcome differs across two
alternate treatment statuses
SJ-20-3 st0613 . . . . . . . . . . Smooth varying-coefficient models in Stata
. . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Rios-Avila
(help vc_pack, vc_bw, vc_bwalt, vc_reg, vc_bsreg, vc_preg,
vc_predict, vc_test, vc_graph, kweight if installed)
Q3/20 SJ 20(3):647--679
estimates a particular type of semiparametric model known as
the smooth varying-coefficient model (Hastie and Tibshirani,
1993, Journal of the Royal Statistical Society, Series B 55:
757-796), based on kernel regression methods
SJ-20-3 st0614 . Uniform nonparametric inference for time series using Stata
(help tssreg if installed) . . . . . . . . J. Li, Z. Liao, and M. Gao
Q3/20 SJ 20(3):706--720
conducts nonparametric series estimation and uniform
inference for time-series data, including the case with
independent data as a special case
SJ-20-3 st0383_1 . . . . . . . . . . . . . . . . . Software update for gsreg
(help gsreg if installed) . . . . . . . . . P. Gluzmann and D. Panigo
Q3/20 SJ 20(3):757--758
new gsreg command is faster, robust, and more flexible
SJ-20-2 st0597 . . . . . . A practical generalized propensity-score estimator
(help qcte if installed) J. Alejo, A. F. Galvao, and G. Montes-Rojas
Q2/20 SJ 20(2):276--296
implements several methods for estimation and inference
for quantile treatment-effects models with a continuous
treatment
SJ-20-2 st0598 . Estimating selection models without an instrument with Stata
. . . . . . . . . X. D'Haultfoeuille, A. Maurel, X. Qiu, and Y. Zhang
(eqregsel if installed)
Q2/20 SJ 20(2):297--308
provides bootstrap inference for sample-selection models
via extremal quantile regression
SJ-20-2 gr0083 . . . . . . Visualization strategies for regression estimates
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. A. Taylor
Q2/20 SJ 20(2):309--335 (no commands)
illustrates a variant of the coefficient plot for regression
models with p-values constructed using permutation tests
SJ-20-2 st0604 . . . Exponential regression models with two-way fixed effects
(help twexp, twgravity if installed) . . . K. Jochmans and V. Verardi
Q2/20 SJ 20(2):468--480
implements the estimators developed in Jochmans
(2017, Review of Economics and Statistics 99: 478-485)
for exponential regression models with two-way fixed
effects
SJ-20-1 st0588 . . . . . . . . Recentered influence functions (RIFs) in Stata
. . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Rios-Avila
(help rifvar, rifhdreg, rifsureg2, oaxaca_rif, uqreg, hvar,
rifsureg if installed)
Q1/20 SJ 20(1):51--94
provides recentered influence functions (RIFs) that analyze
unconditional partial effects on quantiles in a regression
analysis framework (unconditional quantile regressions) to
facilitate the use of RIFs in the analysis of outcome
distributions: create RIFs for a large set of distributional
statistics, estimate RIF regressions enabling the use of
high-dimensional fixed effects, and provide Oaxaca-Blinder
decomposition analysis (RIF decompositions)
SJ-20-1 st0589 . Fast Poisson estimation with high-dimensional fixed effects
. . . . . . . . . . . . . . S. Correia, P. Guimaraes, and T. Z. Zylkin
(help ppmlhdfe if installed)
Q1/20 SJ 20(1):95--115
estimates (pseudo-)Poisson regression models with multiple
high-dimensional fixed effects
SJ-20-1 st0590 . . . . . . . . . . Estimating errors-in-variables regression
. . . . . . . . . . . . . . . . . . J. R. Lockwood and D. F. McCaffrey
Q1/20 SJ 20(1):116--130 (no commands)
uses analysis and simulation to demonstrate that standard
errors reported by eivreg are negatively biased under
assumptions typically made in latent-variable modeling,
leading to confidence interval coverage that is below the
nominal level
SJ-20-1 st0594 . . Model selection and prediction with regularized regression
. . . . . . . . . . . . . A. Ahrens, C. B. Hansen, and M. E. Schaffer
(help cvlassologit, cvlasso, lasso2, lassologit, lassopack,
rlassologit, rlasso if installed)
Q1/20 SJ 20(1):176--235
provides a suite of programs for regularized regression:
lasso, square-root lasso, elastic net, ridge regression,
adaptive lasso, and postestimation ordinary least squares
SJ-19-4 st0575 . . . Advice on using heteroskedasticity-based identification
. . . . . . . . . . . . . . . . . . . . . . . C. F. Baum and A. Lewbel
Q4/19 SJ 19(4):757--767 (no commands)
gives advice and instructions to researchers who want to use
a heteroskedasticity-based estimator for linear regression
models containing an endogenous regressor when no external
instruments or other such information is available
SJ-19-4 st0576 Censored quantile instrumental-variable estimation with Stata
. . . . . . V. Chernozhukov, I. Fernandez-Val, S. Han, and A. Kowalski
(help cqiv if installed)
Q4/19 SJ 19(4):768--781
introduces a command, cqiv, that simplifies application
of the censored quantile instrumental-variable estimator
SJ-19-4 st0579 . . . . . . . . . . . . . . distcomp: Comparing distributions
(help distcomp if installed) . . . . . . . . . . . . . . D. M. Kaplan
Q4/19 SJ 19(4):832--848
assesses whether two distributions differ at each possible
value while controlling the probability of any false positive,
even in finite samples
SJ-19-4 st0580 . . . . . . . . . . Many instruments: Implementation in Stata
(help mivreg if installed) . . . . . . . S. Anatolyev and A. Skolkova
Q4/19 SJ 19(4):849--866
implements consistent estimation and testing in linear
instrumental-variables regressions with many (possibly
weak) instruments
SJ-19-4 st0583 . . . . Performance simulations for categorical mediation: khb
. . . . . . . . . . . . . . . . E. K. Smith, M. G. Lacy, and A. Mayer
Q4/19 SJ 19(4):913--930 (no commands)
evaluates khb's performance in fitting ordinal logistic
regression models as an exemplar of the wider set of
models to which it applies
SJ-19-4 st0585 . . . . . . . . Simar and Wilson two-stage efficiency analysis
(help simarwilson, gciget if installed) O. Badunenko and H. Tauchmann
Q4/19 SJ 19(4):950--988
implements the procedures proposed by Simar and Wilson
(2007, Journal of Econometrics 136: 31-64) for regression
analysis of data envelopment analysis efficiency scores
SJ-19-3 st0568 . . Two-sample IV regression with potentially weak instruments
(help weaktsiv if installed) . . . . . . . . . . J. Choi and S. Shen
Q3/19 SJ 19(3):581--597
provides for two-sample instrumental-variables regression
models with one endogenous regressor and potentially weak
instruments
SJ-19-3 gr0077 . . . . . . . . Added-variable plots with confidence intervals
(help avciplot, avciplots if installed) . . . . . . . . . J. L. Gallup
Q3/19 SJ 19(3):598--614
presents new command avciplot that is an improvement of
Stata's avplot command; adds a confidence interval and
other options
SJ-19-2 st0555 . . . qmodel: A command for fitting parametric quantile models
. . . . . . . . . . . . . . . . . . . . . . . M. Bottai and N. Orsini
(help qmodel, qmodel postestimation if installed)
Q2/19 SJ 19(2):261--293
fits parametric models for the conditional quantile function of
an outcome variable given covariates
SJ-19-2 st0558 . . . . . Generalized two-part fractional regression with cmp
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. N. Wulff
Q2/19 SJ 19(2):375--389 (no commands)
shows how generalized two-part fractional regression models can
be fit with the community-contributed cmp command
SJ-19-2 st0143_5 . . . . . . . . . . . . . . . Software update for felsdvreg
(help felsdvreg if installed) . . . . . . . . . . . . . T. Cornelissen
Q2/19 SJ 19(2):497
bug fixed when using the option noisily
SJ-19-1 st0551 . . . piaactools: A program for data analysis with PIAAC data
. . . . . . . . . . . . . . . . . . . . M. Jakubowski and A. Pokropek
(help piaacdes, piaactab, piaacreg if installed)
Q1/19 SJ 19(1):112--128
facilitates analysis with PIAAC data
SJ-19-1 st0554 . . . Power calculations for regression-discontinuity designs
. . . . . . . . . . . M. D. Cattaneo, R. Titiunik, and G. Vazquez-Bare
(help rdpow, rdsampsi if installed)
Q1/19 SJ 19(1):210--245
conducts power calculations and survey sample selection when
using local polynomial estimation and inference methods in
regression-discontinuity designs
SJ-18-4 st0547 . . . . . . . . Nonparametric instrumental-variable estimation
. . . . . . . . . . . . . . . . D. Chetverikov, D. Kim, and D. Wilhelm
(help npiv, npivcv if installed)
Q4/18 SJ 18(4):937--950
implements nonparametric instrumental-variable estimation
methods without and with a cross-validated choice of tuning
parameters
SJ-18-4 st0097_2 . . . . . . . . . . . . . . . . Software update for gologit2
(help gologit2 if installed) . . . . . . . . . . . . . . R. Williams
Q4/18 SJ 18(4):997
includes several enhancements
SJ-18-3 st0376_2 . . . . . . . . . . . . . . . . . . Software update for strs
(help strs if installed) . . . . . . . P. W. Dickman and E. Coviello
Q3/18 SJ 18(3):758--759
adds some new features and fixes some bugs
SJ-18-2 st0511_1 . . . . . . . . . . . . . . . . Software update for adfmaxur
(help adfmaxur if installed) . . . . . . . . J. Otero and C. F. Baum
Q2/18 SJ 18(2):489
help file of adfmaxur extended with additional examples
SJ-18-1 st0511 . Unit-root tests based on forward/reverse Dickey-Fuller reg.
(help adfmaxur if installed) . . . . . . . . J. Otero and C. F. Baum
Q1/18 SJ 18(1):22--28
computes the Leybourne (1995, Oxford Bulletin of Economics and
Statistics 57: 559-571) unit-root statistic for different
numbers of observations and the number of lags of the dependent
variable in the test regressions
SJ-18-1 st0513 . Fitting mixture reg. models for bounded dependent variables
. . . . . . . . . . . . . . . . . . L. A. Gray and M. Hernandez Alava
(help betamix, betamix_postestimation if installed)
Q1/18 SJ 18(1):51--75
uses the beta distribution to fit mixture regression models for
dependent variables bounded in an interval
SJ-18-1 st0522 . . . . . Manipulation testing based on density discontinuity
. . . . . . . . . . . . . . . . M. D. Cattaneo, M. Jansson, and X. Ma
(help rddensity, rdbwdensity if installed)
Q1/18 SJ 18(1):234--261
implements automatic manipulation tests based on density
discontinuity and are constructed using the results for local-
polynomial density estimators in Cattaneo, Jansson, and Ma
(2017b, Simple local polynomial density estimators, Working
paper, University of Michigan)
SJ-18-1 st0279_2 . . . . . . . . . . . . . . . . Software update for gpoisson
(help gpoisson if installed) . . T. Harris, Z. Yang, and J. W. Hardin
Q1/18 SJ 18(1):290
commands were updated to use the modern free-parameter notation
in Stata 15
SJ-18-1 st0336_1 . . . . . Software update for nbregp, zignbreg, and zinbregp
. . . . . . . . . . . . . . . . . . . . . J. W. Hardin and J. M. Hilbe
(help nbregp, nbregp postestimation, zignbreg,
zignbreg postestimation, zinbregp, zinbregp postestimation
if installed)
Q1/18 SJ 18(1):290
nbregp command now accepts the irr option and updated to use the
modern free-parameter notation in Stata 15
SJ-18-1 st0337_1 . . . . . . . . Software update for betabin, zibbin, and zib
. . . . . . . . . . . . . . . . . . . . . J. W. Hardin and J. M. Hilbe
(help betabin, betabin postestimation, zibbin,
zibbin postestimation, zib, zib postestimation if installed)
Q1/18 SJ 18(1):290
options removed; estimation commands were updated to use the
modern free-parameter notation in Stata 15
SJ-17-4 gr0071 . Calibration of dichot. outcome models with calibration belt
. . G. Nattino, S. Lemeshow, G. Phillips, S. Finazzi, and G. Bertolini
(help calibrationbelt if installed)
Q4/17 SJ 17(4):1003--1014
implements the calibration belt (a graphical approach to
evaluate the goodness of fit of binary outcome models by
examining the relationship between estimated probabilities
and observed outcome rates) and its associated test
SJ-17-4 st0393_2 . . . . . . . . . . . . . . . . Software update for aidsills
(help aidsills if installed) . . . . . . . S. Lecocq and J.-M. Robin
Q4/17 SJ 17(4):1024
error messages added; bug fix for calculation of the Stone price
index
SJ-17-3 st0488 . . . . Model selection for univariable fractional polynomials
(help fp_select if installed) . . . . . . . . . . . . . . . P. Royston
Q3/17 SJ 17(3):619--629
presents fp_select, a postestimation tool for fp that allows
selection of a parsimonious fractional polynomial model
according to a closed test procedure called the fractional
polynomial selection procedure or function selection procedure
SJ-17-3 st0400_1 . . . . . . . . . . . . . . . . Software update for penlogit
. . . . . . . . . . . . . A. Discacciati, N. Orsini, and S. Greenland
(help penlogit if installed)
Q3/17 SJ 17(3):779
corrects a bug that resulted in the postestimation command
predict calculating the linear predictor (log odds) instead
of the probability of a positive outcome
SJ-17-2 st0475 Regression clustering for panel-data models with fixed effects
(help xtregcluster if installed) . D. Christodoulou and V. Sarafidis
Q2/17 SJ 17(2):314--329
implements the panel regression clustering approach developed
by Sarafidis and Weber (2015, Oxford Bulletin of Economics and
Statistics 77: 274-296)
SJ-17-2 st0366_1 . . rdrobust: Software for regression-discontinuity designs
. . . . . S. Calonico, M. D. Cattaneo, M. H. Farrell, and R. Titiunik
(help rdrobust, rdbwselect, rdplot if installed)
Q2/17 SJ 17(2):372--404
describes a major upgrade to the Stata (and R) rdrobust package,
which provides a wide array of estimation, inference, and
falsification methods for the analysis and interpretation of
regression-discontinuity designs
SJ-17-2 st0480 . . . . . . . . Estimating responsiveness scores using rscore
(help rscore if installed) . . . . . . . . . . . . . . . . G. Cerulli
Q2/17 SJ 17(2):422--441
computes unit-specific responsiveness scores using an iterated
random-coefficient regression approach
SJ-17-2 st0376_1 . . . . . . . . . . . . . . . . . . Software update for strs
(help strs if installed) . . . . . . . P. W. Dickman and E. Coviello
Q2/17 SJ 17(2):515--516
fixes the incorrect estimates of the Pohar Perme (actuarial)
estimator when all individuals in an interval died
SJ-16-4 st0456 Gen. reg.-adj. est. for avg. treatment effects from panel data
. . . . . . . . . . . . . . . . . . . . . . . . . . . . D. M. Drukker
Q4/16 SJ 16(4):826--836 (no commands)
illustrates that the simple regression-adjustment estimator
is inconsistent for the average treatment effect when the
random effects affecting treatment assignment are correlated
with the random effects that affect the potential outcomes
SJ-16-2 st0433 . . . . . . . . Regression models for bivariate count outcomes
(help bivcnto if installed) . . . . . . . . . . X. Xu and J. W. Hardin
Q2/16 SJ 16(2):301--315
fits regression models suitable for analyzing correlated
count outcomes
SJ-16-2 st0435 . . Regression discontinuity designs under local randomization
. . . . . . . . . . . M. D. Cattaneo, R. Titiunik, and G. Vazquez-Bare
(help rdrandinf, rdwinselect, rdsensitivity, rdrbounds if installed)
Q2/16 SJ 16(2):331--367
conducts finite-sample inference in regression discontinuity
designs under a local randomization assumption
SJ-16-2 st0438 . . . . . . Fixed effects in unconditional quantile regression
(help xtrifreg if installed) . . . . . . . . . . . . . . N. T. Borgen
Q2/16 SJ 16(2):403--415
introduces the xtrifreg command which has many of the same
features as rifreg but can be used to include a large number
of fixed effects, to estimate cluster-robust standard errors,
and to estimate cluster-bootstrapped standard errors
SJ-16-1 st0419 . . . . . . . . . . . Regressions are commonly misinterpreted
. . . . . . . . . . . . . . . . . . . . . . . . . . . . D. C. Hoaglin
Q1/16 SJ 16(1):5--22 (no commands)
discusses misinterpretation of regression coefficients in
multivariable models for linear regression, logistic regression,
and other generalized linear models, as well as for survival,
longitudinal, and hierarchical regressions; suggests caution in
calculating predictions that average over other variables
SJ-16-1 st0420 . . Reg. are commonly misinterpreted: Comments on the article
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. W. Hardin
Q1/16 SJ 16(1):23--24 (no commands)
comments on Hoaglin's Regressions are commonly misinterpreted
article
SJ-16-1 st0421 . . Reg. are commonly misinterpreted: Comments on the article
. . . . . . . . . . . . . . . . . . . . . J. S. Long and D. M. Drukker
Q1/16 SJ 16(1):25--29 (no commands)
comments on Hoaglin's Regressions are commonly misinterpreted
article
SJ-16-1 st0422 . . . . . Regressions are commonly misinterpreted: A rejoinder
. . . . . . . . . . . . . . . . . . . . . . . . . . . . D. C. Hoaglin
Q1/16 SJ 16(1):30--36 (no commands)
Hoaglin's response to Hardin, Long, and Drukker's comments
SJ-16-1 st0425 . . . Extension of mfp using the ACD covariate transformation
(help mfpa if installed) . . . . . . . . P. Royston and W. Sauerbrei
Q1/16 SJ 16(1):72--87
provides enhanced parametric multivariable modeling using the
ACD covariate transformation; allows user-selected covariates
SJ-16-1 st0429 . . . . bivariate ordinal regressions with residual dependence
. . . . . . . . . . . . . . . . . . . M. Hernandez-Alava and S. Pudney
(help bicop, bicop postestimation if installed)
Q1/16 SJ 16(1):159--184
fits a model consisting of a pair of ordinal regressions with a
flexible residual distribution, with each marginal distribution
specified as a two-part normal mixture, and stochastic
dependence governed by a choice of copula functions
SJ-16-1 st0393_1 . . . . . . . . . . . . . . . . Software update for aidsills
(help aidsills if installed) . . . . . . . S. Lecocq and J.-M. Robin
Q1/16 SJ 16(1):244
errors corrected and postestimation command added
SJ-15-4 st0413 Best subsets variable selection in nonnormal regression models
(help gvselect if installed) . . . . . . . C. Lindsey and S. Sheather
Q4/15 SJ 15(4):1046--1059
performs variable selection on a wide variety of normal and
nonnormal regression models
SJ-15-4 st0156_2 . . . . . . . . . . . . . . . . . Software update for mvmeta
(help mvmeta, mvmeta_make if installed) . . . . . . . . . I. R. White
Q4/15 SJ 15(4):1186--1187
modified to work with the new network suite for network
meta-analysis; bugs fixed; new options added
SJ-15-4 st0315_2 . . . . . . . . . . Software update for sfcross and sfpanel
. . . . . . . . . . . F. Belotti, S. Daidone, G. Ilardi, and V. Atella
(help sfcross, sfcross_postestimation, sfpanel,
sfpanel_postestimation if installed)
Q4/15 SJ 15(4):1186--1187
fixes issues with sfpanel
SJ-15-3 st0397 . Prediction in linear index models with endogenous regressors
. . . . . . . . . . . . . . . . . . . . C. L. Skeels and L. W. Taylor
Q3/15 SJ 15(3):627--644
demonstrates that predictions after linear index models with
endogenous regressors (such as ivprobit) have limited
usefulness, especially for out-of-sample predictions;
outlines a command ivpredict to overcome the problem
SJ-15-3 st0398 Fitting fixed- & random-effects meta-analysis with sem & gsem
. . . . . . . . . . . . . . . . . . . T. M. Palmer and J. A. C. Sterne
Q3/15 SJ 15(3):645--671
demonstrates how to fit fixed- and random-effects meta-analysis,
meta-regression, and multivariate outcome meta-analysis models
under the structural equation modeling framework
SJ-15-3 st0400 Approx. Bayesian logistic regression via penalized likelihood
. . . . . . . . . . . . . A. Discacciati, N. Orsini, and S. Greenland
(help penlogit if installed)
Q3/15 SJ 15(3):712--736
provides approximate Bayesian logistic regression using
penalized likelihood estimation via data augmentation
SJ-15-3 st0405 . . Treatment-effect estimation under alternative assumptions
(help didq if installed) . . . . . . . . . . . R. Mora and I. Reggio
Q3/15 SJ 15(3):796--808
provides treatment-effect estimation in a difference-in-
difference framework under alternative assumptions relating
dynamics for controls and treated in absence of treatment;
allows identification for any given assumption in the family
of alternative identifying assumptions
SJ-15-3 st0202_1 Reg. analysis of censored data using pseudo-obs.: An update
. . . . . . . . . . . . M. Overgaard, P. K. Andersen, and E. T. Parner
(help stpsurv, stpci, stpmean, stplost if installed)
Q3/15 SJ 15(3):809--821
generate pseudo-observations of the survival function, the
cumulative incidence function under competing risks, the
restricted mean survival-time function, and the cause-specific
lost-lifetime function
SJ-15-3 st0391_1 . . . . . . . . . . . . . . . . Software update for nomolog
(help nomolog if installed) . . . . . . . . A. Zlotnik and V. Abraira
Q3/15 SJ 15(3):899
bug fixes in nomolog
SJ-15-2 st0383 . . . . . . . . . . . . . . . . . . . Global search regression
(help gsreg if installed) . . . . . . . . . P. Gluzmann and D. Panigo
Q2/15 SJ 15(2):325--349
provides a new automatic model-selection technique for
cross-section, time-series, and panel-data regressions
SJ-15-2 st0388 . . . . . . . . . . . . . . . . . . Modeling heaped count data
. . . . . . . . Cummings, Hardin, McLain, Hussey, Bennett, and Wingood
(help heapcr, heapcr postestimation, ziheapcr,
ziheapcr postestimation, heapr, heapr postestimation, ziheapr,
ziheapr postestimation if installed)
Q2/15 SJ 15(2):457--479
models heaped count data using the Poisson, generalized
Poisson, and negative binomial distributions along with their
zero-inflated versions
SJ-15-2 st0391 . Nomogram generator for predictive logistic regression models
(help nomolog if installed) . . . . . . . . A. Zlotnik and V. Abraira
Q2/15 SJ 15(2):537--546
provides a general-purpose nomogram generator that works
after arbitrary logit or logistic commands
SJ-15-2 st0393 . Est. almost-ideal demand systems with endogenous regressors
(help aidsills if installed) . . . . . . . S. Lecocq and J.-M. Robin
Q2/15 SJ 15(2):554--573
estimates almost-ideal demand systems and their quadratic
extensions
SJ-15-2 st0394 . . . . . . . . . . . . . . Speaking Stata: Species of origin
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. J. Cox
Q2/15 SJ 15(2):574--587 (no commands)
explores origins (some fixed and natural, others just
conventional and sometimes not even convenient) and considers
how best to model simple trends and seasonal periodicities as
well as defining noncalendar years
SJ-15-2 gn0065 . . . Review of A Gentle Introduction to Stata, Fourth Edition
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Collier
Q2/15 SJ 15(2):588--593 (no commands)
book review of A Gentle Introduction to Stata, Fourth Edition
by Alan Acock (2014)
SJ-15-2 st0395 . . . . . . . . . . . . . Stata tip 125: Binned residual plots
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Kasza
Q2/15 SJ 15(2):599--604 (no commands)
describes binned residual plots for assessing the fit of
regression models for binary outcomes
SJ-15-2 st0279_1 . . . . . . . . . . . . . . . . Software update for gpoisson
(help gpoisson if installed) . . T. Harris, Z. Yang, and J. W. Hardin
Q2/15 SJ 15(2):605--606
likelihood evaluator for gpoisson updated and the iteration
log of the comparison Poisson model is now displayed
SJ-15-2 st0315_1 . . . . . . . . . . Software update for sfcross and sfpanel
. . . . . . . . . . . F. Belotti, S. Daidone, G. Ilardi, and V. Atella
(help sfcross, sfcross_postestimation, sfpanel,
sfpanel_postestimation if installed)
Q2/15 SJ 15(2):605--606
fixes two issues with sfpanel
SJ-15-1 st0376 . . . . . . . . . . Estimating and modeling relative survival
(help strs if installed) . . . . . . . P. W. Dickman and E. Coviello
Q1/15 SJ 15(1):186--215
provides life-table estimation of relative survival
SJ-15-1 gr0059_1 . . . . . . . . . . . . . . . . Software update for coefplot
(help coefplot if installed) . . . . . . . . . . . . . . . . B. Jann
Q1/15 SJ 15(1):324
fixed extra observations left behind in the dataset
SJ-15-1 st0213_2 . . . . . . . . . . . . . . . . Software update for vselect
(help vselect if installed) . . . . . . . . C. Lindsey and S. Sheather
Q1/15 SJ 15(1):324
nmodels() option has been added; best option has been fixed
SJ-14-4 gr0059 . . . . . Plotting regression coefficients and other estimates
(help coefplot if installed) . . . . . . . . . . . . . . . . B. Jann
Q4/14 SJ 14(4):708--737
alternative of marginsplot that plots results from any
estimation command and combines results from several models
into one graph
SJ-14-4 st0358 . . . . . Estimation of multiprocess survival models with cmp
. . . . . . . . . . . . . . . . . . . . . . . T. Bartus and D. Roodman
Q4/14 SJ 14(4):756--777 (no commands)
describes multiprocess survival models and demonstrates
theoretical and practical aspects of estimation
SJ-14-4 st0359 . . . . . . . . Commands to implement double-hurdle regression
. . . . . . . . . . . . . . . . . . . . . . C. Engel and P. G. Moffatt
(help dhreg, xtdhreg, bootdhreg if installed)
Q4/14 SJ 14(4):778--797
maximum likelihood estimation of the double-hurdle model for
continuously distributed outcomes with option to fit a p-tobit
model; provides bootstrap and panel data extensions
SJ-14-4 st0366 . . Robust data-driven inference in reg.-discontinuity design
. . . . . . . . . . . . . S. Calonico, M. D. Cattaneo, and R. Titiunik
(help rdrobust, rdbwselect, rdplot if installed)
Q4/14 SJ 14(4):909--946
conducts robust data-driven statistical inference in
regression-discontinuity designs
SJ-14-4 st0367 . . . . . . . . . . Fitting ordinal logistic regression models
(help adjcatlogit, ccrlogit, ucrlogit if installed) . M. W. Fagerland
Q4/14 SJ 14(4):947--964
performs adjacent-category logistic regression, constrained
continuation-ratio logistic regression, and unconstrained
continuation-ratio logistic regression for ordered response
data
SJ-14-4 st0339_1 . . . . . . . . . . . . . . . . . . Software update for acd
(help acd if installed) . . . . . . . . . . . . . . . . . . P. Royston
Q4/14 SJ 14(4):997
bug fix concerning the handling of missing values in the
expression
SJ-14-3 st0349 . . . . . . . . . . Merger simulation with nested logit demand
(help mergersim if installed) . . . . J. Bjornerstedt and F. Verboven
Q3/14 SJ 14(3):511--540
implements merger simulation in Stata as a postestimation
command, that is, after estimating an aggregate nested
logit demand system with a linear regression model
SJ-14-3 st0353 . . . . . . . . . . . . . . . Space-filling location selection
(help spacefill if installed) . . . . . . . . . M. Bia and P. Van Kerm
Q3/14 SJ 14(3):605--622
implements a space-filling location-selection algorithm
SJ-14-2 st0336 . . . . . . . . . . . . . . . Negative binomial(p) regression
. . . . . . . . . . . . . . . . . . . . . J. W. Hardin and J. M. Hilbe
(help nbregp, nbregp postestimation, zignbreg,
zignbreg postestimation, zinbregp, zinbregp postestimation
if installed)
Q2/14 SJ 14(2):280--291
estimates regression models for count data based on the
negative binomial(p) distribution and allows for over-
dispersed count outcomes
SJ-14-2 st0337 . . . . . . . . . . . . . . . . . . Binomial regression models
. . . . . . . . . . . . . . . . . . . . . J. W. Hardin and J. M. Hilbe
(help betabin, betabin postestimation, zibbin,
zibbin postestimation, zib, zib postestimation if installed)
Q2/14 SJ 14(2):292--303
provides estimation and testing of binomial and beta-binomial
regression models with and without zero inflation
SJ-14-2 st0339 . . . . . . . . . . . Modeling sigmoid dose-response functions
(help acd if installed) . . . . . . . . . . . . . . . . . . P. Royston
Q2/14 SJ 14(2):329--341
flexible parametric modeling of a covariate effect whose
functional form is singly or doubly asymptotic or which has
a sigmoid shape or component
SJ-14-2 st0340 . . . . . . . . . . . . . . . . . . . . . . From Stata to aML
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Ayllon
Q2/14 SJ 14(2):342--362 (no commands)
explains how to exploit Stata to run multilevel multiprocess
regressions with applied maximum likelihood (aML)
SJ-14-2 st0342 . Power analyses for detecting eff. for multiple coefficients
(help powermr3, powersim3 if installed) . . . . . . . . C. L. Aberson
Q2/14 SJ 14(2):389--397
conducts power analysis for multiple regression
SJ-14-2 st0085_2 Software update for estadd, estout, _eststo, eststo, esttab
(help estadd, estout, _eststo, eststo, esttab if installed) . B. Jann
Q2/14 SJ 14(2):451
new features added and various problems fixed
SJ-14-1 st0333 . . . Stata tip 118: Orthogonalizing powered and product terms
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Sauer
Q1/14 SJ 14(1):226--229 (no commands)
tip on using residual centering as an alternative to mean
centering for orthogonalizing powered and product terms
SJ-13-4 st0315 . . . . . . . . . . . Stochastic frontier analysis using Stata
. . . . . . . . . . . F. Belotti, S. Daidone, G. Ilardi, and V. Atella
(help sfcross, sfcross_postestimation, sfpanel,
sfpanel_postestimation if installed)
Q4/13 SJ 13(4):719--758
estimates cross-sectional and panel-data stochastic frontier
models
SJ-13-4 st0321 . . A score test for group comparisons in single-index models
(help scoregrp if installed) . . . . . . . . . . . . . . P. Guimaraes
Q4/13 SJ 13(4):876--883
provides a score test for the equality of one or more
parameters across groups of observations following estimation
of a single-index model
SJ-13-3 gr0056 . . . . Plotting the marginal effects of continuous predictors
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Royston
(help marginscontplot if installed)
Q3/13 SJ 13(3):510--527
plots the marginal effect of continuous covariates in
regression models; nonlinear relationships involving
transformed covariates may also be plotted on the original
scale
SJ-13-3 st0143_4 . . . . . . . . . . . . . . . Software update for felsdvreg
(help felsdvreg if installed) . . . . . . . . . . . . . T. Cornelissen
Q3/13 SJ 13(3):667
bug fixed and description of the orig option extended
SJ-13-3 st0294_1 . . . . . . . . . . . . . . . . Software update for laplace
(help laplace if installed) . . . . . . . . . M. Bottai and N. Orsini
Q3/13 SJ 13(3):667
bug fixed and now allows fweights and pweights
SJ-13-2 st0294 . . . . . . . . . . . . . . . A command for Laplace regression
(help laplace if installed) . . . . . . . . . M. Bottai and N. Orsini
Q2/13 SJ 13(2):302--314
estimates Laplace regression, which models quantiles of a
possibly censored outcome variable given covariates
SJ-13-2 st0299 . . . . . . . . . . Goodness-of-fit tests for categorical data
. . . . . . . . . . . . . . . . . . . . . . R. Bellocco and S. Algeri
Q2/13 SJ 13(2):356--365 (no commands)
discusses choice of analytical units of reference (subjects
or groups of subjects that have the same covariate pattern)
and how that affects the definition of the saturated model
and conclusions from goodness-of-fit tests
SJ-13-1 st0285 . . . . . . . . . . . . . . . . . Regression anatomy, revealed
(help reganat if installed) . . . . . . . . . . . . . . . . V. Filoso
Q1/13 SJ 13(1):92--106
implements graphically the method of regression anatomy
SJ-13-1 st0290 . . . . Doubly robust estimation in generalized linear models
(help drglm if installed) . . N. Orsini, R. Bellocco, and A. Sjolander
Q1/13 SJ 13(1):185--205
implements the most common doubly robust estimators for
generalized linear models
SJ-13-1 gn0056 . . . . . . Review of Data Analysis Using Stata, Third Edition
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . L. P. Schumm
Q1/13 SJ 13(1):206--211 (no commands)
book review of Data Analysis Using Stata, Third Edition by
Kohler and Kreuter (2012)
SJ-12-4 st0273 . . . . . . . . . . . A generalized missing-indicator approach
. . . . . . . . . V. Dardanoni, G. De Luca, S. Modica, and F. Peracchi
(help gmi if installed)
Q4/12 SJ 12(4):575--604
uses model reduction or Bayesian model averaging techniques
in the context of the generalized missing-indicator approach
to estimates a linear regression model using data where some
covariate values are missing but imputations are available
to fill in the missing values
SJ-12-4 st0165_1 . Fitting & modeling cure with flex. param. survival models
. . . . . . . . . . . . . . . . . T. M.-L. Andersson and P. C. Lambert
(help stpm2, stpm2_postestimation if installed)
Q4/12 SJ 12(4):623--638
updated for flexible parametric models that enable cure
modeling
SJ-12-4 sg97_5 . A programmer's command to build formatted statistical tables
(help frmttable if installed) . . . . . . . . . . . . . . J. L. Gallup
Q4/12 SJ 12(4):655--673
create formatted tables from statistics and write them to
Word or LaTeX files
SJ-12-4 st0278 . Robinson's square root of N consistent semipar. reg. estim.
(help semipar if installed) . . . . . . . . V. Verardi and N. Debarsy
Q4/12 SJ 12(4):726--735
presents Robinson's double residual semiparametric regression
estimator and Hardle and Mammen's specification test
SJ-12-4 st0279 . . . Underdispersed count data with generalized Poisson reg.
(help gpoisson if installed) . . T. Harris, Z. Yang, and J. W. Hardin
Q4/12 SJ 12(4):736--747
models underdispersed count data with generalized Poisson
regression; also suitable as an alternative to negative
binomial regression for overdispersed data
SJ-12-4 st0159_1 . . . . . . . . . . . . . . . . Software update for xtabond2
(help xtabond2 if installed) . . . . . . . . . . . . . . . D. Roodman
Q4/12 SJ 12(4):766--767
bug fixes and added features such as support for factor
variables
SJ-12-4 st0224_1 . . . . . . . . . . . . . . Software update for cmp and ghk2
(help cmp, ghk2 if installed) . . . . . . . . . . . . . . . D. Roodman
Q4/12 SJ 12(4):766--767
bug fixes, speed improvements, and added features such
as support for factor variables
SJ-12-3 st0267 . . . . . . . Fixed-effects estimation in normal linear models
. . . . . . D. F. McCaffrey, J. R. Lockwood, K. Mihaly, and T. R. Sass
Q3/12 SJ 12(3):406--432 (no commands)
review of commands for fixed-effects estimation of one-level
and two-level normal linear models
SJ-12-3 st0269 . . . . . . A generalized Hosmer-Lemeshow goodness-of-fit test
(help mlogitgof if installed) . . . . M. W. Fagerland and D. W. Hosmer
Q3/12 SJ 12(3):447--453
implements a generalized Hosmer-Lemeshow goodness-of-fit test
for multinomial logistic regression models
SJ-12-3 sg151_2 . Sensible parameters for univariate and multivariate splines
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. B. Newson
(help bspline, flexcurv, frencurv if installed)
Q3/12 SJ 12(3):479--504
added an easy-to-use command that generates reference splines
with automatically generated, sensibly spaced knots
SJ-12-3 st0272 . Long-run covariance and its app. in cointegration regression
. . . . . . . . . . . . . . . . . . . . . . . . . . Q. Wang and N. Wu
(help lrcov, hacreg, cointreg if installed)
Q3/12 SJ 12(3):515--542
computes long-run covariance with a prewhitening strategy
and various kernel functions; obtains heteroskedasticity-
and autocorrelation-consistent standard errors; provides
cointegration regression
SJ-12-3 gn0053 . . . Review of Interpreting and Visualizing Regression Models
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. C. Acock
Q3/12 SJ 12(3):562--564 (no commands)
book review of Interpreting and Visualizing Regression Models
Using Stata by Michael N. Mitchell
SJ-12-3 st0231_1 . . . . Software update for lqreg, lqregpred, and lqregplot
. . . . . . . . . . . . . . . . . . . . . . . N. Orsini and M. Bottai
(lqreg, lqreg_postestimation, lqregpred, lqregplot if installed)
Q3/12 SJ 12(3):570
cluster() option has been fixed
SJ-12-2 st0252 . . . . . . . . . . A robust instrumental-variables estimator
(help robivreg if installed) . . . . . . R. Desbordes and V. Verardi
Q2/12 SJ 12(2):169--181
implements an instrumental-variables estimator robust
to outliers and allowing the usual identification and
overidentifying restrictions tests
SJ-12-2 st0257 . . . Threshold regression for time-to-event analysis: stthreg
. . . . . . . . . . . T. Xiao, G. A. Whitmore, X. He, and M.-L. T. Lee
(help stthreg, sttrkm, trhr, trpredict if installed)
Q2/12 SJ 12(2):257--283
fits a threshold regression model
SJ-12-2 st0259 . . . . . The S-estimator of multivariate location and scatter
(help smultiv if installed) . . . . . . . . V. Verardi and A. McCathie
Q2/12 SJ 12(2):299--307
provides the S-estimator of multivariate location and scatter
SJ-12-2 st0261 . . . . . . . . . . Stata tip 108: On adding and constraining
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. L. Buis
Q2/12 SJ 12(2):342--344 (no commands)
tip showing the use of constraint as an alternative to
summing variables
SJ-12-2 st0087_1 . . . . . . . . . . . . . . . . . Software update for boost
(help boost if installed) . . . . . . . . . . . . . . . . M. Schonlau
Q2/12 SJ 12(2):352
now supports 64-bit architecture under Window; other
additions and bug fixes
SJ-12-2 st0150_4 . . . . . . . . Software update for doseresponse and gpscore
(help doseresponse, gpscore if installed) . . . . M. Bia and A. Mattei
Q2/12 SJ 12(2):352
error fixed and weights properly included
SJ-12-1 sg97_4 . . . . . . . . A new system for formatting estimation tables
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. L. Gallup
(help outreg, outreg complete, outreg update,
greek in word if installed)
Q1/12 SJ 12(1):3--28
creates tables from the results of Stata estimation commands
and generates formatted Microsoft Word or LaTeX files
SJ-11-4 st0239 . Bayesian model averaging and weighted-average least squares
(help bma, wals if installed) . . . . . . G. De Luca and J. R. Magnus
Q4/11 SJ 11(4):518--544
provides exact Bayesian model-averaging estimator and
weighted-average least-squares estimator for linear
regression models with uncertainty about the choice of
the explanatory variables
SJ-11-4 st0241 . . . Multivariate decomposition for nonlinear response models
(help mvdcmp if installed) . D. A. Powers, H. Yoshioka, and M.-S. Yun
Q4/11 SJ 11(4):556--576
a general-purpose multivariate decomposition command for
nonlinear response models that incorporates several recent
contributions to overcome various problems dealing with
path dependence and identification
SJ-11-4 st0143_3 . Fit a linear model with two high-dimensional fixed effects
(help felsdvreg if installed) . . . . . . . . . . . . . T. Cornelissen
Q4/11 SJ 11(4):634
saved results extended allowing calculation of various
R-squared measures; bug fix for the grouponly option
SJ-11-3 st0231 . . . . . . . . . . . . Logistic quantile regression in Stata
. . . . . . . . . . . . . . . . . . . . . . . N. Orsini and M. Bottai
(lqreg, lqreg_postestimation, lqregpred, lqregplot if installed)
Q3/11 SJ 11(3):327--344
estimation, prediction, and graphical representation of
logistic quantile regression
SJ-11-3 st0233 . . . . . . . . . Impact of interventions on discrete outcomes
(help switch_probit if installed) . . . . . . M. Lokshin and Z. Sajaia
Q3/11 SJ 11(3):368--385
implements the maximum likelihood method to fit the model
of the binary choice with binary endogenous regressors
SJ-11-3 st0234 . . . . . An application of multiple-source information models
. . . . . . M. P. Caria, R. Bellocco, M. R. Galanti, and N. J. Horton
Q3/11 SJ 11(3):386--402 (no commands)
describes regression-based methods for analyzing
multiple-source data in Stata; example combines two
measures of body mass index and relates them to smoking
onset
SJ-11-3 st0049_1 Instrumental variables, bootstrapping, and gen. lin. models
. . . . . . . . . . . J. W. Hardin, R. J. Carroll, and H. Schmiediche
(rcal if installed)
Q3/11 SJ 11(3):478
bug fix for rcal
SJ-11-3 st0215_1 . Tab. and plot results after flex. modeling of quant. cov.
(help xblc if installed) . . . . . . . . . N. Orsini and S. Greenland
Q3/11 SJ 11(3):478
bug fix and added graph options for xblc
SJ-11-2 st0224 . . . . Fitting fully observed recursive mixed-process models
(help cmp, ghk2 if installed) . . . . . . . . . . . . . . . D. Roodman
Q2/11 SJ 11(2):159--206
fits simultaneous-equation fully observed recursive
mixed-process models
SJ-11-2 st0225 . . . . . . . . . . . . . . . poisson: Some convergence issues
(help ppml if installed) . . . J. M. C. Santos Silva and S. Tenreyro
Q2/11 SJ 11(2):207--212
provides improved Poisson regression by checking for the
existence of the estimates and providing two methods for
dropping regressors that cause nonexistence of estimates
SJ-11-2 st0156_1 . . . . Multivariate random-effects meta-regression: Updates
(help mvmeta, mvmeta_make if installed) . . . . . . . . . I. R. White
Q2/11 SJ 11(2):255--270
extension of mvmeta command to handle meta-regression
SJ-11-2 st0162_1 . Estimating adjusted risk ratios for matched/unmatched data
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Cummings
Q2/11 SJ 11(2):290--298 (no commands)
shows how the margins command and the robust variance
option (vce(robust)) for conditional Poisson regression
(xtpoisson, fe) make it easier to estimate adjusted
risk ratios
SJ-11-1 st0215 Tabulate and plot results after flex. modeling of quant. cov.
(help xblc if installed) . . . . . . . . . N. Orsini and S. Greenland
Q1/11 SJ 11(1):1--29
provides a postestimation command that facilitates the
presentation of the association between a quantitative
covariate and the response variable
SJ-11-1 st0219 . . . . . . . . . . . Right-censored Poisson regression model
. . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Raciborski
(help rcpoisson, rcpoisson_postestimation if installed)
Q1/11 SJ11(1):95--105
estimates right-censored count-data models with a constant
and variable censoring threshold
SJ-11-1 st0221 . Tip 94: Prediction parameters for par. survival reg. models
. . . . . . . . . . . . . . . . . . . . T. Boswell and R. G. Gutierrez
Q1/11 SJ 11(1):143--144 (no commands)
tip on manipulating prediction parameters for parametric
survival regression models
SJ-11-1 srd3_1 . . . . . . . . . . . . . . . . . . Software update for bound
(help bound if installed) . . . . . . . . . . . . . . . . R. Goldstein
Q1/11 SJ 11(1):155
updated to Stata 11.1
SJ-11-1 srd13_2 . . . . . . . . . . . . . . . . . . Software update for maxr2
(help maxr2 if installed) . . . . . . . . . . . . . . . . R. Goldstein
Q1/11 SJ 11(1):155
updated to Stata 11.1; now supports factor variables and
can be used after areg and ivreg2
SJ-11-1 st0213_1 . . . . . . . . . . . . . . . . Software update for vselect
(help vselect if installed) . . . . . . . . C. Lindsey and S. Sheather
Q1/11 SJ 11(1):155
fix() option fixed
SJ-10-4 st0207 . Suite of commands for fitting skew-normal and skew-t models
. . . . . . . . . . . . . . . . . . . Y. V. Marchenko and M. G. Genton
(help skewnreg, skewtreg, mskewnreg, mskewtreg,
skew_postestimation, skewrplot if installed)
Q4/10 SJ 10(4):507--539
provides univariate and multivariate skew-normal and skew-t
regressions
SJ-10-4 st0208 . . . . . . . . Fitting heterogeneous choice models with oglm
(help oglm if installed) . . . . . . . . . . . . . . . . R. Williams
Q4/10 SJ 10(4):540--567
shows how oglm (ordinal generalized linear models) can be
used to estimate heterogeneous choice and related models
SJ-10-4 st0212 . Procedure to fit models with high-dimensional fixed effects
. . . . . . . . . . . . . . . . . . . . . P. Guimaraes and P. Portugal
Q4/10 SJ 10(4):628--649 (no commands)
describes an iterative approach for the estimation of
linear regression models with high-dimensional fixed
effects
SJ-10-4 st0213 . . . . . . . . . . . Variable selection in linear regression
(help vselect if installed) . . . . . . . . C. Lindsey and S. Sheather
Q4/10 SJ 10(4):650--669
performs variable selection after a linear regression
SJ-10-4 st0150_3 . . . . . . . . Software update for doseresponse and gpscore
(help doseresponse, gpscore if installed) . . . . M. Bia and A. Mattei
Q4/10 SJ 10(4):692
updated to Stata 11
SJ-10-4 st0182_1 . . . . . . . . . . . . . . . . Software update for ldecomp
(help ldecomp if installed) . . . . . . . . . . . . . . . . M. L. Buis
Q4/10 SJ 10(4):692
bug fix for ldecomp
SJ-10-3 st0202 . . . . Regression analysis of censored data using pseudo-obs.
. . . . . . . . . . . . . . . . . . . E. T. Parner and P. K. Andersen
(help stpsurv, stpci, stpmean if installed)
Q3/10 SJ 10(3):408--422
produces pseudo-observations for use in direct regression
modeling of the survival function, the restricted mean, and
the cumulative incidence function in competing risks with
right-censored data
SJ-10-3 st0203 . . . . . Estimation of quantile treatment effects with Stata
(help ivqte and locreg if installed) . . . . M. Frolich and B. Melly
Q3/10 SJ 10(3):423--457
provides: Koenker and Bassett classical quantile regression
estimator extended to heteroskedasticity consistent standard
errors; Abadie, Angrist, and Imbens instrumental-variable
quantile regression estimator; Firpo unconditional quantile
treatment effects estimator; and Frolich and Melly instrumental-
variable estimator for unconditional quantile treatment effects
SJ-10-2 st0188 . . . Optimal power transformation via inverse response plots
(help irp if installed) . . . . . . . . . . C. Lindsey and S. Sheather
Q2/10 SJ 10(2):200--214
provides the inverse response plot of a response on its
predictors
SJ-10-2 st0189 . . . . . . . . Model fit assessment via marginal model plots
(help mmp if installed) . . . . . . . . . . C. Lindsey and S. Sheather
Q2/10 SJ 10(2):215--225
provides marginal model plots for a regression model
SJ-10-2 gn0050 . . . . . . . . . . . . Review of Multivariable Model-Building
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. D. Dupont
Q2/10 SJ 10(2):297--302 (no commands)
book review of Multivariable Model-Building: A Pragmatic
Approach to Regression Analysis Based on Fractional
Polynomials for Modeling Continuous Variables, by
P. Royston and W. Sauerbrei
SJ-10-2 st0173_1 . . . . . . . . . . . . . . . . . . Software update for mcd
. . . . . . . . . . . . . . . . . . . . . . . V. Verardi and C. Croux
(mmregress, sregress, msregress, mregress, mcd if installed)
Q2/10 SJ 10(2):313
outlier option replaced by generate() option in mcd
SJ-10-2 st0099_1 . . . . . . . . . . . . . . Software update for svylogitgof
. . . . . . . . . . . . . K. J. Archer, S. Lemeshow, and M. I. Lichter
(help svylogitgof if installed)
Q2/10 SJ 10(2):313
svylogitgof has been improved
SJ-10-1 st0182 . . . . . . . . . Direct and indirect effects in a logit model
(help ldecomp if installed) . . . . . . . . . . . . . . . . M. L. Buis
Q1/10 SJ 10(1):11--29
decompose a total effect in a logit model into direct and
indirect effects
SJ-10-1 dm0045 . . . Using the WDI database for statistical analysis in Stata
(help wdireshape, paverage if installed) . . . . . . . . P. W. Jeanty
Q1/10 SJ 10(1):30--45
enables efficient management of world development indicators
datasets
SJ-10-1 st0183 . . . . . . . Tabulating SPost results using estout and esttab
. . . . . . . . . . . . . . . . . . . . . . . . B. Jann and J. S. Long
Q1/10 SJ 10(1):46--60 (no commands)
facilitates tabulating results from the SPost user package
via the estout user package
SJ-10-1 st0184 . . . . . . . . Power transformation via multivariate Box-Cox
(help mboxcox, mbctrans if installed) . . . C. Lindsey and S. Sheather
Q1/10 SJ 10(1):69--81
computes the normalizing scaled power transformations for a
set of variables via the multivariate Box-Cox transformation
SJ-10-1 st0186 . . . . Creating synthetic discrete-response regression models
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. M. Hilbe
Q1/10 SJ 10(1):104--124
presents code for the creation of synthetic binomial, count,
and categorical response models
SJ-10-1 gr0009_1 . . . . Software update for model diagnostic graph commands
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. J. Cox
(help anovaplot, indexplot, modeldiag, ofrtplot, ovfplot,
qfrplot, racplot, rdplot, regplot, rhetplot, rvfplot2,
rvlrplot, rvpplot2 if installed)
Q1/10 SJ 10(1):164
provides new command rbinplot for plotting means or medians
of residuals by bins; provides new options for smoothing
using restricted cubic splines; updates anova examples
SJ-9-4 st0178 . Partial effects in probit/logit with triple dummy interact.
(help inteff3 if installed) . . . . . . T. CorneliBen and K. Sonderhof
Q4/09 SJ 9(4):571--583
analyzes partial effects in probit and logit models with
a triple dummy-variable interaction term
SJ-9-4 st0179 . . . . . . . . . . . . Cragg's tobit alternative using Stata
(help craggit if installed) . . . . . . . . . . . . . . . W. J. Burke
Q4/09 SJ 9(4):584--592
introduces the command craggit, which simultaneousely
fits both tiers of Cragg's "two-tier" alternative to
tobit for corner-solution models
SJ-9-4 st0150_2 The dose-response function adj. for the gen. propensity score
(help doseresponse, gpscore if installed) . . . . M. Bia and A. Mattei
Q4/09 SJ 9(4):652
updated to specify version 10 in order to run correctly
under Stata 11
SJ-9-3 st0173 . . . . . . . . . . . . . . . . . . Robust regression in Stata
. . . . . . . . . . . . . . . . . . . . . . . V. Verardi and C. Croux
(mmregress, sregress, msregress, mregress, mcd if installed)
Q3/09 SJ 9(3):439--453
provides alternatives to rreg and qreg for robust-to-outlier
regression; presents a graphical tool that recognizes the
type of detected outliers
SJ-9-3 st0067_4 . Mult. imp.: update of ice, with emphasis on cat. variables
(help ice, uvis if installed) . . . . . . . . . . . . . . . P. Royston
Q3/09 SJ 9(3):466--477
update of ice package with emphasis on categorical
variables; clarifies relationship between ice and mi
SJ-9-2 st0162 . . . . . . . . . Methods for estimating adjusted risk ratios
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Cummings
Q2/09 SJ 9(2):175--196 (no commands)
discusses several methods for estimating adjusted risk ratios
and shows how they can be executed in Stata
SJ-9-2 st0163 . metandi: Meta-anal. of diag. acc. using hier. logistic reg.
. . . . . . . . . . . . . . . . . . . . . R. M. Harbord and P. Whiting
(help metandi, metandiplot, metandi_postestimation
if installed)
Q2/09 SJ 9(2):211--229
introduces the command metandi for meta-analyzing
diagnostic accuracy data
SJ-9-2 st0165 . Further dev. of flexible param. models for survival analysis
. . . . . . . . . . . . . . . . . . . . . P. C. Lambert and P. Royston
(help stpm2, stpm2_postestimation if installed)
Q2/09 SJ 9(2):265--290
introduces stpm2, which extends the class of flexible
parametric survival models programmed with stpm
SJ-9-2 st0096_2 . GLS for trend estimation of summarized dose-response data
(help glst if installed) . . N. Orsini, R. Bellocco, and S. Greenland
Q2/09 SJ 9(2):327
update for glst that makes sure the dataset is sorted by
the study identification variable when pooling multiple
studies
SJ-9-2 st0143_2 . fit a linear model with two high-dimensional fixed effects
(help felsdvreg if installed) . . . . . . . . . . . . . T. Cornelissen
Q2/09 SJ 9(2):327
bug fix for the grouponly option
SJ-9-2 st0152_1 . The Blinder-Oaxaca decomposition for nonlinear reg. models
(help nldecompose if installed) . M. Sinning, M. Hahn, and T. K. Bauer
Q2/09 SJ 9(2):327
calculation of sample means are now adjusted for the use
of weights and svy commands
SJ-9-1 st0154 . . . . . . . . . . . Estimation and comparison of ROC curves
. . . . . . . . . . . . . . . . . M. S. Pepe, G. Longton, and H. Janes
(help comproc, roccurve if installed)
Q1/09 SJ 9(1):1--16
comprehensive suite of commands for performing ROC analysis
SJ-9-1 st0155 . . . . . . . . . . . Accommodating covariates in ROC analysis
. . . . . . . . . . . . . . . . . H. Janes, G. Longton, and M. S. Pepe
(help comproc, roccurve, rocreg if installed)
Q1/09 SJ 9(1):17--39
describes three ways of incorporating covariate information
in an ROC analysis
SJ-9-1 st0156 . . . . . . . . . . Multivariate random-effects meta-analysis
(help mvmeta, mvmeta_make, if installed) . . . . . . . . I. R. White
Q1/09 SJ 9(1):40--56
maximum likelihood, restricted maximum likelihood, or
method-of-moments estimation of random-effects multivariate
meta-analysis models
SJ-9-1 st0159 . . How to do xtabond2: An intro. to difference and system GMM
(help xtabond2 if installed) . . . . . . . . . . . . . . . D. Roodman
Q1/09 SJ 9(1):86--136
introduces linear GMM; describes how limited time span
and potential for fixed effects and endogenous regressors
drive the design of estimators; shows how to apply
difference and system GMM estimators with xtabond2
SJ-9-1 st0160 . . . . Evaluating concavity for production and cost functions
. . . . . . . . . . . . . . . . . . . . . . . . C. F. Baum and T. Linz
Q1/09 SJ 9(1):161--165 (no commands)
discusses how to evaluate production and cost functions
SJ-9-1 st0096_1 . GLS for trend estimation of summarized dose-response data
(help glst if installed) . . N. Orsini, R. Bellocco, and S. Greenland
Q1/09 SJ 9(1):173
software update for glst that includes new options to
investigate specific studies included in the dose-response
meta-analysis
SJ-9-1 st0143_1 . fit a linear model with two high-dimensional fixed effects
(help felsdvreg if installed) . . . . . . . . . . . . . T. Cornelissen
Q1/09 SJ 9(1):173
software update for the felsdvreg command that allows
F tests to be optional, making felsdvreg more efficient
SJ-8-4 st0151 . . . The Blinder-Oaxaca decomposition for linear reg. models
(help oaxaca if installed) . . . . . . . . . . . . . . . . . B. Jann
Q4/08 SJ 8(4):453--479
implements the Blinder-Oaxaca decomposition, which is often
used to study mean outcome differences between groups
SJ-8-4 st0152 . . The Blinder-Oaxaca decomposition for nonlinear reg. models
(help nldecompose if installed) . M. Sinning, M. Hahn, and T. K. Bauer
Q4/08 SJ 8(4):480--492
implements a general Blinder-Oaxaca decomposition for
nonlinear models
SJ-8-4 sbe23_1 . . . . . . . . . . . . . . . . . . . Meta-regression in Stata
(help metareg if installed) . . . . R. M. Harbord and J. P. T. Higgins
Q4/08 SJ 8(4):493--519
presents a revised version of the metareg command, which
performs meta-analysis regression on study-level summary
data
SJ-8-4 st0136_1 . . . Erratum and discussion of propensity-score reweighting
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Nichols
Q4/08 SJ 8(4):532--539 (no commands)
discusses propensity-score reweighting
SJ-8-4 gn0043 . . Review of Multilevel & Long. Modeling Using Stata, 2nd Ed
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. J. Horton
Q4/08 SJ 8(4):579--582 (no commands)
book review of Multilevel and Longitudinal Modeling
Using Stata, 2nd Edition by Rabe-Hesketh and Skrondal
SJ-8-4 gr0024_1 . . . . . . . . . . Graphical representation of interactions
(help fintplot if installed) . . . . F. M.-S. Barthel and P. Royston
Q4/08 SJ 8(4):594
fixed bug causing an error in the calculation of the hazard
ratio or relative risk of treatment for the second level of
the covariate alone
SJ-8-4 st0150_1 The dose-response function adj. for the gen. propensity score
(help doseresponse, gpscore if installed) . . . . M. Bia and A. Mattei
Q4/08 SJ 8(4):594
improved handling of predicted probabilities; correction
of some references to variables named treatment_level_plus
or similar
SJ-8-3 st0148 . . . . . Semiparametric analysis of case-control genetic data
Y.V. Marchenko, R.J. Carroll, D.Y. Lin, C.I. Amos, and R.G. Gutierrez
(haplologit if installed)
Q3/08 SJ 8(3):305--333
implements efficient profile-likelihood semiparametric
methods for fitting gene-environment models in the special
cases of a rare disease, a single candidate gene in Hardy-
Weinberg equilibrium, and independence of genetic and
environmental factors
SJ-8-3 st0149 . . . Implementing double-robust estimators of causal effects
(help dr if installed) . R. Emsley, M. Lunt, A. Pickles, and G. Dunn
Q3/08 SJ 8(3):334--353
presents a double-robust estimator for pretest-posttest
studies
SJ-8-3 st0150 The dose-response function adj. for the gen. propensity score
(help doseresponse, gpscore if installed) . . . . M. Bia and A. Mattei
Q3/08 SJ 8(3):354--373
estimates the propensity score with a continuous treatment,
tests the balancing property of the generalized propensity
score, and estimates the dose-response function
SJ-8-3 dm0037 . . . . . . . . . . . . . Creating print-ready tables in Stata
(help xml_tab if installed) . . . . . . . . . M. Lokshin and Z. Sajaia
Q3/08 SJ 8(3):374--389
outputs the results of estimation commands and Stata
matrices directly into tables in XML format
SJ-8-3 st0123_1 ML and two-step estimation of ordered-probit selection model
(help oheckman if installed) . . . . . . . R. Chiburis and M. Lokshin
Q3/08 SJ 8(3):452
bug fix for yif() option of oheckman causing predict
to fail
SJ-8-2 st0143 . . fit a linear model with two high-dimensional fixed effects
(help felsdvreg if installed) . . . . . . . . . . . . . T. Cornelissen
Q2/08 SJ 8(2):170--189
uses a memory-saving decomposition of the design matrix
to facilitate the estimation of a linear model with two
high-dimensional fixed effects
SJ-8-2 st0144 . SNP and SML est. of uni- and bivariate binary-choice models
(help sml, snp if installed) . . . . . . . . . . . . . . . G. De Luca
Q2/08 SJ 8(2):190--220
provides semi-nonparametric and semiparametric maximum
likelihood estimators for univariate and bivariate
binary-choice models
SJ-8-2 st0147 . . . . . . . . . . . . . . Stata tip 63: Modeling proportions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. F. Baum
Q2/08 SJ 8(2):299--303 (no commands)
tip on how to model a response variable that appears
as a proportion or fraction
SJ-8-1 st0141 . . . . . . Stata tip 58: nl is not just for nonlinear models
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. P. Poi
Q1/08 SJ 8(1):139--141 (no commands)
tip showing examples where nl is preferable to regress,
even when the model is linear in the parameters
SJ-8-1 st0126_1 . . . . . . QIC program and model selection in GEE analyses
(help qic if installed) . . . . . . . . . . . . . . . . . . . . J. Cui
Q1/08 SJ 8(1):146
general negative binomial distribution now included with qic
SJ-7-4 st0067_3 . . . . Multiple imputation of missing values: Update of ice
(help ice, ice_reformat, micombine, uvis if installed) . . P. Royston
Q4/07 SJ 7(4):445--464
update of ice allowing imputation of left-, right-, or
interval-censored observations
SJ-7-4 st0030_3 . . . . Enhanced routines for IV/GMM estimation and testing
. . . . . . . . . . . . . C. F. Baum, M. E. Schaffer, and S. Stillman
(help ivactest, ivendog, ivhettest, ivreg2, ivreset,
overid, ranktest if installed)
Q4/07 SJ 7(4):465--506
extension of IV and GMM estimation addressing hetero-
skedasticity- and autocorrelation-consistent standard
errors, weak instruments, LIML and k-class estimation,
tests for endogeneity and Ramsey's regression
specification-error test, and autocorrelation tests
for IV estimates and panel-data IV estimates
SJ-7-4 st0136 . . . . . . . . . . . Causal inference with observational data
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Nichols
Q4/07 SJ 7(4):507--541 (no commands)
discusses problems with inferring causal relationships
from nonexperimental data and describes four broad classes
of methods designed to allow estimation of and inference
about causal parameters
SJ-7-3 st0128 . Robust std. err. for panel reg. with cross-sect. dependence
(help xtscc, xtscc_postestimation if installed) . . . . . . D. Hoechle
Q3/07 SJ 7(3):281--312
estimates pooled ordinary least-squares/weighted least-squares
regression and fixed-effects regression models with Driscoll
and Kraay standard errors
SJ-7-3 st0129 . Est. dichotomous & ordinal item response models with gllamm
(help gllamm, gllapred if installed) . . X. Zheng and S. Rabe-Hesketh
Q3/07 SJ 7(3):313--333
describes the one- and two-parameter logit models for
dichotomous items, the partial-credit and rating scale
models for ordinal items, and an extension of these models
where the latent variable is regressed on explanatory
variables
SJ-7-3 st0132 . . Profile likelihood for estimation and confidence intervals
(help pllf if installed) . . . . . . . . . . . . . . . . . P. Royston
Q3/07 SJ 7(3):376--387
computes and plots the maximum likelihood estimate and
profile likelihood-based confidence interval for one
parameter in a wide variety of regression models
SJ-7-2 st0122 . . . Improved GEE analysis via xtqls for quasi-least squares
(help xtqls if installed) . J. Shults, S. J. Ratcliffe, and M. Leonard
Q2/07 SJ 7(2):147--166
uses QLS as an alternative for estimating correlation
parameters within a GEE model
SJ-7-2 st0123 . ML and two-step estimation of ordered-probit selection model
(help oheckman if installed) . . . . . . . R. Chiburis and M. Lokshin
Q2/07 SJ 7(2):167--182
two-step and full-information maximum likelihood (FIML)
estimation of a regression model with an ordered-probit
selection rule
SJ-7-2 st0124 . . Commands for assessing confounding effects in epi. studies
(help chest, confall, confgr if installed) . . . . . . . . . Z. Wang
Q2/07 SJ 7(2):183--196
two postestimation commands for assessing confounding
effects in epidemiological studies
SJ-7-2 st0126 . . . . . . . QIC program and model selection in GEE analyses
(help qic if installed) . . . . . . . . . . . . . . . . . . . . J. Cui
Q2/07 SJ 7(2):209--220
provides the quasilikelihood under the independence
model criterion (QIC) method for GEE model selection
SJ-7-2 st0127 . . . . . . . . . predict and adjust with logistic regression
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. L. Buis
Q2/07 SJ 7(2):221--226 (no commands)
discusses subtle differences in interpretation of average
predicted values computed by adjust and predict after
logistic regression
SJ-7-2 st0085_1 . . . . . . . . . . . . Making regression tables simplified
(help estadd, estout, _eststo, eststo, esttab if installed) . B. Jann
Q2/07 SJ 7(2):227--244
introduces the eststo and esttab commands (stemming from
estout) that simplify making regression tables from stored
estimates
SJ-7-2 st0097_1 . . . . . . . . . . . . . . . . Software update for gologit2
(help gologit2 if installed) . . . . . . . . . . . . . . R. Williams
Q2/07 SJ 7(2):280
now supports prefix commands and allows different link
functions
SJ-7-1 st0120 . Multivar. modeling with cubic reg. splines: A prin. approach
(help mvrs, uvrs, splinegen if installed) P. Royston and W. Sauerbrei
Q1/07 SJ 7(1):45--70
discusses how to limit instability and provide sensible
regression models when using spline functions in a
multivariable setting
SJ-6-4 st0116 . . . . Speaking Stata: In praise of trigonometric predictors
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. J. Cox
Q4/06 SJ 6(4):561--579 (no commands)
discusses the use of sine and cosine as predictors in
modeling periodic time series and other kinds of periodic
responses
SJ-6-4 st0105_1 . . . . . . . . . . . . . . . . Software update for mtreatnb
(help mtreatnb if installed) . . . . . . . . P. Deb and P. K. Trivedi
Q4/06 SJ 6(4):597
bug fix to allow _rmcoll to handle multicollinearity
between regressors; iteration log now shown by default
SJ-6-4 st0053_3 . . . . . . . . . . . . . . . . Software update for locpoly
. . . . . . . . . . R. G. Gutierrez, J. M. Linhart, and J. S. Pitblado
(help locpoly if installed)
Q4/06 SJ 6(4):597
update permitting override of default axes titles
SJ-6-3 st0107 ML estimation of endog. switching and sample selection models
(help ssm if installed) . . . . . . . . A. Miranda and S. Rabe-Hesketh
Q3/06 SJ 6(3):285--308
discusses gllamm and wrapper ssm, for fitting models in
which the response depends on a dummy or regime-switch
variable when the outcome variable is binary, count or
ordinal
SJ-6-3 gr0024 . . . . . . . . . . . Graphical representation of interactions
(help fintplot if installed) . . . . F. M.-S. Barthel and P. Royston
Q3/06 SJ 6(3):348--363
illustrates interactions between treatment and covariates
or between two covariates using forest plots under the Cox
proportional hazards or the logistic regression model
SJ-6-3 st0109 Difference-based semipar. estim. of partial linear reg. models
(help plreg if installed) . . . . . . . . . . . . . . . . . M. Lokshin
Q3/06 SJ 6(3):377--383
describes plreg, which implements the difference-based
algorithm for fitting partial linear regression models
SJ-6-2 st0105 . MSL of neg. binom. reg. mod. with multinom. endog. treatment
(help mtreatnb if installed) . . . . . . . . P. Deb and P. K. Trivedi
Q2/06 SJ 6(2):246--255
introduces a new command, mtreatnb, for specification
and estimation of a multinomial treatment effects
negative binomial regression model
SJ-6-2 gn0032 . . Review of Reg. Models for Cat. Dependent Var. Using Stata
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Williams
Q2/06 SJ 6(2):273--278 (no commands)
book review of Regression Models for Categorical
Dependent Variables Using Stata, 2nd Edition by
Long and Freese
SJ-6-2 srd13_1 . . . . . . . . . . . . . . . . . . Software update for maxr2
(help maxr2 if installed) . . . . . . . . . . . . . . . . R. Goldstein
Q2/06 SJ 6(2):284
bug fix for maxr2
SJ-6-2 st0045_2 . . . . . . . . . . Software update for mvprobit and mvppred
(help mvppred, mvprobit if installed) L. Cappellari and S. P. Jenkins
Q2/06 SJ 6(2):284
bug fix; help files updated
SJ-6-1 st0096 . . GLS for trend estimation of summarized dose-response data
(help glst if installed) . . N. Orsini, R. Bellocco, and S. Greenland
Q1/06 SJ 6(1):40--57
trend estimation across different exposure levels for either
single or multiple summarized case-control, incidence-rate,
and cumulative incidence data
SJ-6-1 st0097 . Gen. ordered logit/part. prop. odds mod. for ordinal depvars
(help gologit2 if installed) . . . . . . . . . . . . . . R. Williams
Q1/06 SJ 6(1):58--82
program for generalized ordered logit models; fits
proportional odds/parallel-lines model, partial
proportional odds model, and logistic regression model
SJ-6-1 st0098 . . . . . . . . . . . Explained variation for survival models
(help str2ph, str2d if installed) . . . . . . . . . . . . . P. Royston
Q1/06 SJ 6(1):83--96
introduces a new measure of explained variation for use
with censored survival data
SJ-6-1 st0099 . . GOF test for logistic reg. fitted using survey sample data
(help svylogitgof if installed) . . . . . K. J. Archer and S. Lemeshow
Q1/06 SJ 6(1):97--105
estimates the F-adjusted mean residual test after svy: logit
or svy: logistic
SJ-6-1 gn0031 . . Review of Multilevel and Longitudinal Modeling Using Stata
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Wolfe
Q1/06 SJ 6(1):138--143 (no commands)
book review of Multilevel and Longitudinal Modeling
Using Stata by Rabe-Hesketh and Skrondal
SJ-5-4 st0091 . . Estimation and inference in dynamic unbalanced panel-data
(help xtlsdvc if installed) . . . . . . . . . . . . . . G. S. F. Bruno
Q4/05 SJ 5(4):473--500
provides a bias-corrected least-squares dummy variable
(LSDV) estimator and bootstrap variance-covariance matrix
for dynamic (possibly) unbalanced panel-data models with
(possibly) few subjects and strictly exogenous regressors
SJ-5-4 st0092 Extended GLM: simultaneous est. of flex. link & variance func.
(help pglm if installed) . . . . . . . . . . . . . . . . . . A. Basu
Q4/05 SJ 5(4):501--516
simultaneously solves the extended estimating equations
estimator for parameters in the link and variance functions
along with those of the linear predictor in a generalized
linear model (GLM)
SJ-5-4 st0067_2 . . . . Multiple imputation of missing values: Update of ice
(help ice, micombine, mijoin if installed) . . . . . . . . P. Royston
Q4/05 SJ 5(4):527--536
update of mvis (renamed to ice); imputation of missing
values now achieved by sampling from the posterior
predictive distribution
SJ-5-4 st0094 . CIs for predicted outcomes in reg. models for cat. outcomes
(help prvalue, prgen if installed) . . . . . . . J. Xu and J. S. Long
Q4/05 SJ 5(4):537--559
discusses endpoint transformation, delta method, and
bootstrapping for computing confidence intervals for
predictions and discrete changes in predictions for
regression models for categorical outcomes; ability to
compute confidence intervals added to prvalue and prgen
SJ-5-4 gn0030 . . Review of Data Analysis Using Stata by Kohler and Kreuter
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . L. P. Schumm
Q4/05 SJ 5(4):594--600 (no commands)
book review of Data Analysis Using Stata by Kohler
and Kreuter (2005)
SJ-5-4 st0030_2 . . . . . . Software update for ivreg2, overid, and ivendog
. . . . . . . . . . . . . C. F. Baum, M. E. Schaffer, and S. Stillman
(help ivendog, ivhettest, ivreg2, overid if installed)
Q4/05 SJ 5(4):607
enhanced first-stage regression diagnostics, tests for
overidentification, redundancy of instruments, and
continuously updated GMM estimation (CUE)
SJ-5-3 st0085 . . . . . . . . Making regression tables from stored estimates
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Jann
(help estadd, estout, estout_defaults_options,
estout_intro, estout_label_subopts, estout_labeling_options,
estout_layout_options, estout_output_options,
estout_parameter_statistics_options,
estout_significance_stars_options,
estout_summary_statistics_options, estoutdef if installed)
Q3/05 SJ 5(3):288--308
introduces new estout package, which produces regression
tables for use with spreadsheets, LaTeX, HTML, or word
processors
SJ-5-3 st0087 . Boosted reg. (boosting): An intro. tutorial and Stata plugin
(help boost if installed) . . . . . . . . . . . . . . . . M. Schonlau
Q3/05 SJ 5(3):330--354
overview of boosting or boosted regression; introduces
boost command
SJ-5-3 st0089 . . . . . . . A simple approach to fit the beta-binomial model
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Guimaraes
Q3/05 SJ 5(3):385--394 (no commands)
shows how to estimate the parameters of the beta-binomial
distribution and the Dirichlet-multinomial distribution
SJ-5-3 st0090 . Stings in the tails: Detecting & dealing with censored data
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. M. Conroy
Q3/05 SJ 5(3):395--404 (no commands)
discusses the effects of clustering at extreme values
and of graininess
SJ-5-3 gr0017 . . . . . . . . . . . . . A multivariable scatterplot smoother
(help mrunning, running if installed) . . . . P. Royston and N. J. Cox
Q3/05 SJ 5(3):405--412
presents an extension to running for use in a
multivariable context
SJ-5-3 gn0029 . . . . . . . Review of Statistics for Epidemiology by Jewell
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Bellocco
Q3/05 SJ 5(3):461--464 (no commands)
book review of Statistics for Epidemiology by Jewell (2004)
SJ-5-3 sg139_1 . . . . . . . . . . . . . . . . . Software update for logitem
(help logitem if installed) . . . . . . . . . M. Cleves and A. Tosetto
Q3/05 SJ 5(3):470--471
updated to include prediction program file logite_p
SJ-5-3 st0071_2 . . . . . . . . . . . . . . . . Software update for movestay
(help movestay if installed) . . . . . . . . M. Lokshin and Z. Sajaia
Q3/05 SJ 5(3):470--471
minor bug fix and new option added for movestay;
modifications made to mspredict
SJ-5-2 st0083 . . . . . Exploratory analysis of SNP for quantitative traits
(help hwsnp, qtlsnp, if installed) . . . . . . . . . . . M. A. Cleves
Q2/05 SJ 5(2):141--153
commands for screening and testing multiple SNPs (single
nucleotide polymorphism) for association with quantitative
traits
SJ-5-2 st0067_1 . . . . . . . Multiple imputation of missing values: update
(help ice, micombine, mijoin if installed) . . . . . . . . P. Royston
Q2/05 SJ 5(2):188--201
substantial update to mvis (renamed ice) and some
improvements to micombine
SJ-5-2 st0084 . . Estimation and testing of fixed-effect panel-data systems
. . . . . . . . . . . . . . . . . . . . . . . . . J. L. Blackwell, III
Q2/05 SJ 5(2):202--207 (no commands)
describes how to specify, estimate, and test multiple-equation,
fixed-effect, panel-data equations in Stata
SJ-5-2 gn0028 . . . . . . . . Review of Regression Methods in Biostatistics
. . . . . . . . . . . . . . . . . . S. Lemeshow and M. L. Moeschberger
Q2/05 SJ 5(2):274--278 (no commands)
book review of Regression Methods in Biostatistics:
Linear, Logistic, Survival, and Repeated Measures Models
by Vittinghof, Glidden, Shiboski, and McCulloch (2005)
SJ-5-2 sed9_2 . . . . . . . . . . . . . . . . . Software update for running
(help running if installed) . . P. Sasieni, P. Royston, and N. J. Cox
Q2/05 SJ 5(2):285
running rewritten to support Stata 8 graphics and otherwise
modernized; now attributable to the three authors named above
SJ-5-2 st0045_1 . . . . . . . . . . Software update for mvprobit and mvppred
(help mvppred, mvprobit if installed) . L. Cappellari & S. P. Jenkins
Q2/05 SJ 5(2):285
software updated with an option for using antithetic
acceleration (to reduce simulation variance) and also
updated to allow the technique() option of ml
SJ-5-2 st0053_2 . . . . . . . . . . . . . . . . Software update for locpoly
. . . . . . . . . . R. G. Gutierrez, J. M. Linhart, and J. S. Pitblado
(help locpoly if installed)
Q2/05 SJ 5(2):285
bug fix for locpoly; dialog boxes updated for Stata 9
SJ-5-1 gn0020 . . . . . . . . . . . A short history of Statistics with Stata
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . L. Hamilton
Q1/05 SJ 5(1):35--37 (no commands)
a history of Statistics with Stata, as told by its author
SJ-5-1 st0080 . . . Stata: The language of choice for time-series analysis?
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. F. Baum
Q1/05 SJ 5(1):46--63 (no commands)
discusses the use of Stata for the analysis of time series
and panel data.
SJ-5-1 st0081 Visualizing main effects & interactions for binary logit mod.
. . . . . . . . . . . . . . . . . . . . . . M. N. Mitchell and X. Chen
(help vibl, viblicc, viblidb, vibligraph, viblmcc, viblmdb,
viblmgraph if installed)
Q1/05 SJ 5(1):64--82
presents new package vibl as visualization tool for
interpreting main effects and interactions in logit models
when using predicted probabilities
SJ-5-1 st0053_1 . . . . . . . . . . . . . . . . Software update for locpoly
. . . . . . . . . . . R. Gutierrez, J. M. Linhart, and J. S. Pitblado
(help locpoly if installed)
Q1/05 SJ 5(1):139
bug fix for locpoly
SJ-5-1 st0071_1 . . . . . . . . . . . . . . . . Software update for movestay
(help movestay if installed) . . . . . . . . M. Lokshin and Z. Sajaia
Q1/05 SJ 5(1):139
bug fix for movestay
SJ-4-4 st0075 . Controlling time-dep. confounding using marg. struct. models
. Z. Fewell, M.A. Hernan, F. Wolfe, K. Tilling, H. Choi, J.A.C. Sterne
Q4/04 SJ 4(4):402--420 (no commands)
describes the use of marginal structural models to
estimate exposure or treatment effects in the presence
of time-dependent confounders affected by prior treatment
SJ-4-4 st0077 . . CIs for the variance comp. of random-effects linear models
(help xtvc if installed) . . . . . . . . . . M. Bottai and N. Orsini
Q4/04 SJ 4(4):429--435
confidence intervals for the variance components of
random-effects linear regression models.
SJ-4-4 st0079 Help desk: Seemingly unrelated reg. with unbalanced equations
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. McDowell
Q4/04 SJ 4(4):442--448 (no commands)
demonstrates how to estimate the parameters of a system
of seemingly unrelated regressions when the equations
are unbalanced
SJ-4-4 gr0009 . . . . . . . . . . Speaking Stata: Graphing model diagnostics
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. J. Cox
(help anovaplot, indexplot, modeldiag, ofrtplot, ovfplot,
qfrplot, racplot, rdplot, regplot, rhetplot, rvfplot2,
rvlrplot, rvpplot2 if installed)
Q4/04 SJ 4(4):449--475
plotting diagnostic information calculated from residuals
and fitted values from regression models with continuous
responses
SJ-4-4 sqv10_1 . . . . . . . . . . . . . . . . . . Software update for mcross
(help mcross, if installed) . . . . . . . . . . . . . . D. Blanchette
Q4/04 SJ 4(4):490
mcross updated to extend support to mlogit and svymlogit;
original program by W. H. Rogers
SJ-4-4 st0038_1 . . . . . . . . . . . . . . . . Software update for cdsimeq
(help cdsimeq if installed) . . . . . . . . . . . . . . O. M. G. Keshk
Q4/04 SJ 4(4):491
updated to allow all postestimation commands except
lrtest and suest
SJ-4-3 st0067 . . . . . . . . . . . . Multiple imputation of missing values
(help micombine, mijoin, mvis if installed) . . . . . . . . P. Royston
Q3/04 SJ 4(3):227--241
implementation of the MICE (multivariate imputation by
chained equations) method of multiple multivariate data
imputation
SJ-4-3 gr32_1 . Graphing confidence ellipses: An update of ellip for Stata 8
(help ellip, ellip_dlg if installed) . . . . . . . . A. Alexandersson
Q3/04 SJ 4(3):242--256
ellip command for graphing confidence ellipses updated
to use Stata 8 graphics and provide new features
SJ-4-3 st0069 . . . . . Understanding the multinomial-Poisson transformation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Guimaraes
Q3/04 SJ 4(3):265--273 (no commands)
discusses the data transformations required to transform
a Poisson regression into a logit model and vice versa
SJ-4-3 st0070 . . . . . . . . . . . . . . . Analysis of matched cohort data
(help csmatch if installed) . . . . . . . P. Cummings and B. McKnight
Q3/04 SJ 4(3):274--281
command for estimating risk ratios using matched-pair
cohort data
SJ-4-3 st0071 . Maximum likelihood est. of endogenous switching reg. models
(help movestay if installed) . . . . . . . . M. Lokshin and Z. Sajaia
Q3/04 SJ 4(3):282--289
implementation of the maximum likelihood method for
fitting endogenous switching regression models
SJ-4-3 sg144_1 . . . . . . . . . . . . . . . . . . Software update for dtobit
(help dtobit if installed) . . . . . . . . . . . . . . . . . R. Cong
Q3/04 SJ 4(3):359
bug fix for dtobit
SJ-4-2 st0030_1 . . . . . . . . . . Software update for ivreg2 and ivhettest
. . . . . . . . . . . . . C. F. Baum, M. E. Schaffer, and S. Stillman
(help ivreg2, overid, ivendog, ivhettest if installed)
Q2/04 SJ 4(2):224
ivreg2 now supports autocorrelation consistent and
heteroskedasticity and autocorrelation consistent
covariance matrix estimation, and the score() option
has changed to pscore(); ivhettest also enhanced
SJ-4-1 st0056 . . Semi-nonparametric est. of extended ordered probit models
(help sneop if installed) . . . . . . . . . . . . . . . M. B. Stewart
Q1/04 SJ 4(1):27--39
presents a semi-nonparametric estimator for a series of
generalized models that nest the ordered probit model and
thereby relax the distributional assumption in that model
SJ-4-1 st0001_2 . Flexible parametric alternatives to the Cox model: update
(help bhcalc, stpm if installed) . . . . . . . . . . . . . P. Royston
Q1/04 SJ 4(1):98--101
presents stpm as updated for Stata 8.1
SJ-3-4 st0047 . . . . . Measurement error, GLMs, and notational conventions
. . . . . . . . . . . . . . . . . . . . J. W. Hardin and R. J. Carroll
Q4/03 SJ 3(4):329--341 (no commands)
discusses additive measurement error in a generalized
linear-model context, types of measurement error and
their effects on fitted models, and notational
conventions
SJ-3-4 st0048 . Variance est. for inst. var. approach to meas. error in GLMs
. . . . . . . . . . . . . . . . . . . . J. W. Hardin and R. J. Carroll
Q4/03 SJ 3(4):342--350 (no commands)
variance estimation for the instrumental variables
approach to measurement error in generalized linear
models
SJ-3-4 st0049 Instrumental variables, bootstrapping, and gen. linear models
(help qvf if installed) . J. W. Hardin, R. J. Carroll & H. Schmiediche
Q4/03 SJ 3(4):351--360
provides the qvf command for fitting generalized linear
models including instrumental variables and measurement
error; comparison to Stata's glm command
SJ-3-4 st0050 . Reg.-calibration for fitting GLMs with additive meas. error
(help rcal if installed) J. W. Hardin, R. J. Carroll & H. Schmiediche
Q4/03 SJ 3(4):361--372
discusses the method of regression calibration for
fitting generalized linear models with additive
measurement error
SJ-3-4 st0051 Simulation extrapolation for fitting GLMs w/ add. meas. error
. . . . . . . . . . . . J. W. Hardin, R. J. Carroll and H. Schmiediche
(help simex, simexplot if installed)
Q4/03 SJ 3(4):373--385
discusses the method of simulation extrapolation for
fitting generalized linear models with additive
measurement error
SJ-3-4 st0053 . . From the help desk: Local polynomial reg. & Stata plugins
. . . . . . . . . . R. G. Gutierrez, J. M. Linhart, and J. S. Pitblado
(help locpoly if installed)
Q4/03 SJ 3(4):412--419
provides command for performing local polynomial regression;
discusses use of a Stata plugin
SJ-3-4 st0054 . . . . . . . . . . Stata tip 1: The eform() option of regress
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Newson
Q4/03 SJ 3(4):445 (no commands)
tips for using the eform() option of regress
SJ-3-3 st0041 Odds ratios & logistic reg.: examples of use & interpretation
. . . . . . . . . . . . . . . . . S. M. Hailpern and P. F. Visintainer
Q3/03 SJ 3(3):213--225 (no commands)
discusses logistic regression for adjustment of confounding
in epidemiologic studies; relates logit model to Cornfield's
2x2 table in both cohort and case-control studies
SJ-3-3 st0045 . Multivariate probit reg. using simulated maximum likelihood
(help mvppred, mvprobit if installed) . L. Cappellari & S. P. Jenkins
Q3/03 SJ 3(3):278--294
command providing maximum likelihood multivariate probit
regression using the GHK simulation method
SJ-3-3 sg151_1 . . . . . . . . . . . . . . . . . Software update for bspline
(help bspline, frencurv if installed) . . . . . . . . . . . R. Newson
Q3/03 SJ 3(3):325
now includes bspline.pdf manual
SJ-3-2 st0038 CDSIMEQ: A program to implement two-stage probit least squares
(help cdsimeq if installed) . . . . . . . . . . . . . . O. M. G. Keshk
Q2/03 SJ 3(2):157--167
two-stage probit least squares estimation for simultaneous
equation models in which one of the endogenous variables
is continuous and the other is dichotomous
SJ-3-1 st0030 . . . . Instrumental variables and GMM: Estimation and testing
. . . . . . . . . . . . . . C. F. Baum, M. E. Schaffer, & S. Stillman
(help ivreg2, overid, ivendog, ivhettest if installed)
Q1/03 SJ 3(1):1--31
extended instrumental variables routine that provides
generalized methods of moments (GMM) estimates as well
as additional diagnostic tests for overidentification,
endogeneity, and heteroskedasticity
SJ-2-4 st0024 . . . . . . . . . . . . . . Using Aalen's linear hazards model
(help stlh if installed) . . . . . . . . . D. W. Hosmer & P. Royston
Q4/02 SJ 2(4):331--350
estimates & tests for the significance of the time-varying
regression coefficients in Aalen's linear hazards model
SJ-2-4 st0027 . . . . . . . . . . Programmable GLM: Two user defined links
(help logit_nr, relsurv if installed) . . . W. Guan & R. G. Gutierrez
Q4/02 SJ 2(4):378--390
two customized links for glm -- relative survival model
of Hakulinen and Tenkanen, and a logistic model that
accounts for natural response
SJ-2-4 st0022_1 . . . . . . . . . . . . . . Software update for leastlikely
(help leastlikely if installed) . . . . . . . . . . . . . . J. Freese
Q4/02 SJ 2(4):432
bug fix for leastlikely
SJ-2-3 st0021 . . . . . . . . . . . . . . . . . . . . Measuring effect size
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. M. Conroy
Q3/02 SJ 2(3):290--295 (no commands)
case study showing superiority of regression over t tests,
exploratory scatterplot smoothing as a key method of
checking form of relationship, and the value of logistic
regression followed by adjust
SJ-2-3 st0022 . . Least likely observations in reg. models for cat. outcomes
(help leastlikely if installed) . . . . . . . . . . . . . . J. Freese
Q3/02 SJ 2(3):296--300
command for identifying poorly fitting observations for
maximum-likelihood regression models for categorical
dependent variables
SJ-2-2 st0001_1 . . . . . . . . . . . . . . . . . . Software update for stpm
(help stpm, bhcalc if installed) . . . . . . . . . . . . . P. Royston
Q2/02 SJ 2(2):226
bug fix for stpm
SJ-2-1 st0006 . . . . Parametric frailty and shared frailty survival models
. . . . . . . . . . . . . . . . . . . . . . . . . . . R. G. Gutierrez
Q1/02 SJ 2(1):22--44 (no commands)
primer for fitting parametric frailty and shared frailty
survival models in Stata via the streg command
SJ-2-1 st0008 . . . . . . . . . . . . . . . . . . . . . Quantitative traits
(help qhapipf if installed) . . . . . . . . . . . . . . . A. P. Mander
Q1/02 SJ 2(1):65--70
models the relationship between a quantitative trait and
the genotype of a person using regression and log-linear
modeling when phase is unknown
SJ-2-1 gn0002 . . . . . . . . . . . . . . . . . . Review of Long and Freese
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Hendrickx
Q1/02 SJ 2(1):103--105 (no commands)
book review of Regression Models for Categorical Dependent
Variables Using Stata by Long and Freese (2001)
SJ-1-1 st0001 . Flexible parametric alternatives to the Cox model, and more
(help stpm, bhcalc if installed) . . . . . . . . . . . . . P. Royston
Q4/01 SJ 1(1):1--28
implementation and description of a class of flexible
parametric survival models which use maximum likelihood
for parameter estimation and a restricted cubic regression
spline in log time to model the baseline distribution
function; supports interval-censored data and covariates
with non-proportional effects
SJ-1-1 st0002 . . . . . . . . . . . Predicted probabilities for count models
(help prcounts if installed) . . . . . . . . J. S. Long and J. Freese
Q4/01 SJ 1(1):51--57
postestimation command for generating predicted probabilities
after poisson, nbreg, zip, and zinb
Search of web resources from Stata and other users
--------------------------------------------------
(contacting http://www.stata.com)
1221 packages found (Stata Journal listed first)
------------------------------------------------
st0791 from http://www.stata-journal.com/software/sj25-4
SJ25-4 st0791. Weighted-average least squares: ... / Weighted-average
least squares: Beyond the / classical linear regression model / by
Giuseppe De Luca, University of Palermo, / Palermo, Italy / Jan R. Magnus,
Vrije Universiteit Amsterdam, / Amsterdam, The Netherlands / Support:
st0775 from http://www.stata-journal.com/software/sj25-2
SJ25-2 st0775. Network instrumental-variables regres / Network
instrumental-variables regression / by Pablo Estrada, Emory University,
Atlanta, GA / Juan Estrada, Analysis Group Economic / Consulting,
Washington, DC / Kim Huynh, Bank of Canada, Ottawa, Canada / David
st0765 from http://www.stata-journal.com/software/sj25-1
SJ25-1 st0765. Binscatter regressions / Binscatter regressions / by Matias
D. Cattaneo, Princeton University, / Princeton, NJ / Richard K. Crump,
Federal Reserve Band of New / York, New York, NY / Max H. Farrell, UC
Santa Barbara, Santa / Barbara, CA / Yingjie Feng, Tsinghua University,
st0766 from http://www.stata-journal.com/software/sj25-1
SJ25-1 st0766. Generalized interval regression / Generalized interval
regression / by James B. McDonald, Department of Economics, / Brigham
Young University, Provo, UT / Jacob Triplett, Kenan-Flagler Business /
School, University of North Carolina, / Chapel Hill, NC / Support:
st0769 from http://www.stata-journal.com/software/sj25-1
SJ25-1 st0769. Regression order statistics ... / Regression order
statistics -- estimating / reference bounds for a dataset with possibly /
nondetectable or censored positive / measurements and possibly
contaminated / in either end / by Niels Henrik Bruun, Aalborg University /
st0773 from http://www.stata-journal.com/software/sj25-1
SJ25-1 st0773. Maximum agreement regression / Maximum agreement regression
/ by Matteo Bottai, Division of Biostatistics, / Institute of
Environmental Medicine, / Karolinska Institutet, Stockholm, Sweden /
Support: matteo.bottai@ki.se / After installation, type help {cmd:magreg}
st0751 from http://www.stata-journal.com/software/sj24-3
SJ24-3 st0751. Getting away from the cutoff ... / Getting away from the
cutoff in regression / discontinuity designs / by Filippo Palomba,
Princeton University, / Princeton, NJ / Support: fpalomba@princeton.edu /
After installation, type help {cmd:ciacs}, / {cmd:ciares},
st0747 from http://www.stata-journal.com/software/sj24-2
SJ24-2 st0747. First-stage analysis for ... / First-stage analysis for
instrumental-variables / quantile regression / by Javier Alejo,
IECON-Universidad de la / Rep{c u'}blica, Montevideo, Uruguay / Antonio F.
Galvao, Michigan State University, / East Lansing, MI / Gabriel
st0748 from http://www.stata-journal.com/software/sj24-2
SJ24-2 st0748. Skellam regression estimator / Skellam regression estimator
/ by Vincenzo Verardi, CRED, DEFIPP, FNRS, / University of Namur, Namur,
Belgium / Catherine Vermandele, LMTD, Université libre / de Bruxelles,
Brussels, Belgium / Support: vincenzo.verardi@unamur.be, /
dm0112_1 from http://www.stata-journal.com/software/sj24-1
SJ24-1 dm0112_1. Update: Estimating text regressions ... / Update:
Estimating text regressions using / txtreg_train / by Carlo Schwarz,
Department of Economics, / Bocconi University, Milano, Italy / Support:
carlo.schwarz@unibocconi.it / After installation, type help
st0740 from http://www.stata-journal.com/software/sj24-1
SJ24-1 st0740. Propensity-score residual ... / Propensity-score residual
regression for / overlap-weight average treatment effect / by Myoung-jae
Lee, Department of Economics, / Korea University, Seoul, Republic of Korea
/ Chirok Han, Department of Economics, Korea / University, Seoul, Republic
st0741 from http://www.stata-journal.com/software/sj24-1
SJ24-1 st0741. Nearly collinear robust ... / Nearly collinear robust
instrumental-variables / regression / by Alwyn Young, London School of
Economics, / London, U.K. / Support: a.young@lse.ac.uk / After
installation, type help {cmd:pariv} / DOI: 10.1177/1536867X241233668
st0731 from http://www.stata-journal.com/software/sj23-4
SJ23-4 st0731. Program for stacking regression / Program for stacking
regression / by Achim Ahrens, ETH Zurich, Zurich, Switzerland / Christian
B. Hansen, University of Chicago, / Chicago, IL / Mark E. Schaffer,
Heriot-Watt University, / Edinburgh, U.K. / Support:
st0732 from http://www.stata-journal.com/software/sj23-4
SJ23-4 st0732. Complete subset averaging two-stage ... / Complete subset
averaging two-stage / least-squares regression / by Seojeong Lee,
Department of Economics, Seoul / National University, Seoul, Korea / Siha
Lee, Department of Economics, McMaster / University, Hamilton, Canada /
st0734 from http://www.stata-journal.com/software/sj23-4
SJ23-4 st0734. Autoregressive distributed lag ... / Autoregressive
distributed lag regression model / by Sebastian Kripfganz, University of
Exeter / Business School, Exeter, U.K. / Daniel C. Schneider, Max Planck
Institute for / Demographic Research, Rostock, Germany / Support:
dm0112 from http://www.stata-journal.com/software/sj23-3
SJ23-3 dm0112. Training text regression models ... / Training text
regression models in Stata / by Carlo Schwarz, Bocconi University, Milano,
/ Italy / Support: carlo.schwarz@unibocconi.it / After installation, type
help {cmd:txtreg_train} / , {cmd:txtreg_predict}, and /
st0724 from http://www.stata-journal.com/software/sj23-3
SJ23-3 st0724. Robit regression / Robit regression / by Roger Newson,
King's College London, London, / U.K. / Milena Falcaro, King's College
London, / London, U.K. / Support: roger.newson@kcl.ac.uk, /
milena.falcaro@kcl.ac.uk / After installation, type help {cmd:robit} /
st0703 from http://www.stata-journal.com/software/sj23-1
SJ23-1 st0703. Arbitrary correlation regression / Arbitrary correlation
regression / by Fabrizio Colella, University College London, / London,
U.K. / Rafael Lalive, HEC Lausanne, Lausanne, / Switzerland / Seyhun Orcan
Sakalli, King's College London, / London, U.K. / Mathias Thoenig, HEC
pr0076 from http://www.stata-journal.com/software/sj22-4
SJ22-4 pr0076. Machine learning regression in Stata / Machine learning
regression in Stata / by Giovanni Cerulli, IRCrES-CNR, Rome, Italy /
Support: giovanni.cerulli@ircres.cnr.it / After installation, type help
{cmd:c_ml_stata_cv}, / {cmd:get_train_test}, and {cmd:r_ml_stata_cv} /
st0693 from http://www.stata-journal.com/software/sj22-4
SJ22-4 st0693. Efficient implementation of ... / Efficient implementation
of the regression / control method, also known as the panel-data /
approach for program evaluation (Hsiao, Ching, / and Wan 2012) / by
Guanpeng Yan, Shandong University, Jinan, / China / Qiang Chen, Shandong
st0683 from http://www.stata-journal.com/software/sj22-3
SJ22-3 st0683. Two-regime switching ordered ... / Two-regime switching
ordered probit regression / by Jochem Huismans, Amsterdam School of /
Economics, University of Amsterdam, / Amsterdam, the Netherlands / Jan
Willem Nijenhuis, Nedap NV, Groenlo, the / Netherlands / Andrei Sirchenko,
st0672 from http://www.stata-journal.com/software/sj22-2
SJ22-2 st0672. Binary spatial autoregressive models / Binary spatial
autoregressive models / by Daniele Spinelli, Department of Statistics /
and Quantitative Methods, University of / Milan-Bicocca, Milan, Italy /
Support: daniele.spinelli@unimib.it / After installation, type help
st0676 from http://www.stata-journal.com/software/sj22-2
SJ22-2 st0676. sivqr: Smoothed IV quantile regression / sivqr: Smoothed IV
quantile regression / by David M. Kaplan, Department of Economics, /
University of Missouri, Columbia, MO / Support: kaplandm@missouri.edu /
After installation, type help {cmd:sivqr} / DOI:
st0679 from http://www.stata-journal.com/software/sj22-2
SJ22-2 st0679. Using margins after a Poisson ... / Using margins after a
Poisson regression model / to fit the number of events prevented by an /
intervention / by Milena Falcaro, King's College London, / London, UK /
Roger B. Newson, King's College London, / London, UK / Peter Sasieni,
st0657 from http://www.stata-journal.com/software/sj21-4
SJ21-4 st0657. Quantile regression corrected for ... / Quantile regression
corrected for sample / selection / by Ercio A. Munoz, CUNY Graduate
Center, New / York, NY / Mariel Siravegna, Georgetown University, /
Washington, DC / Support: emunozsaavedra@gc.cuny.edu, /
st0658 from http://www.stata-journal.com/software/sj21-4
SJ21-4 st0658. On identification and estimation ... / On identification
and estimation of Heckman / models / by Jonathan A. Cook, / Joon-Suk Lee,
/ Noah Newberger, / Support: jacookuci.edu, / joonsuk.leeoutlook.com, /
noahnewberger@gmail.com / After installation, type help /
st0646 from http://www.stata-journal.com/software/sj21-3
SJ21-3 st0646. Causal mediation analysis using ... / Causal mediation
analysis using regression with / residuals / by Ariel Linden, Linden
Consulting Group, San / Francisco, CA / Chuck Huber, StataCorp, College
Station, TX / Geoffrey T. Wodtke, Department of Sociology, / University of
st0393_3 from http://www.stata-journal.com/software/sj21-2
SJ21-2 st0393_3. Update: Estimating almost-ideal ... / Update: Estimating
almost-ideal demand systems / with endogenous regressors / by S{c
e:}bastien Lecocq, Universite Paris-Saclay, / INRAE, UR ALISS / Jean-Marc
Robin, Sciences Po, Paris, France, / and UCL, London, UK / Support:
st0641 from http://www.stata-journal.com/software/sj21-2
SJ21-2 st0641. Stacked linear regression analysis ... / Stacked linear
regression analysis to facilitate / testing of multiple hypotheses / by
Michael Oberfichtner, Institute for / Employment Research (IAB), Nurnberg,
/ Germany / Harald Tauchmann, University of Erlangen- / Nuremberg (FAU),
st0173_2 from http://www.stata-journal.com/software/sj21-1
SJ21-1 st0173_2. Update: MM-robust regression / Update: MM-robust
regression / by Vincenzo Verardi, Universite de Namur, Namur, / Belgium /
Christophe Croux, Katholieke University / Leuven, Leuven, Belgium /
Support: vverardiunamur.be, / christophe.crouxecon.kuleuven.ac.be /
st0630 from http://www.stata-journal.com/software/sj21-1
SJ21-1 st0630. Consistent estimation of linear ... / Consistent estimation
of linear regression / models using matched data / by Masayuki Hirukawa,
Ryukoku University, Kyoto, / Japan / Di Liu, StataCorp, College Station,
TX / Artem Prokhorov, University of Sydney / Business School, Sydney,
st0616 from http://www.stata-journal.com/software/sj20-4
SJ20-4 st0616. Mixed-effects regression for linear ... / Mixed-effects
regression for linear, nonlinear, / and user-defined models / by Michael
J. Crowther, University of Leicester, / Leicester, UK / Support:
michael.crowther@le.ac.uk / After installation, type help {cmd:merlin} /
st0620 from http://www.stata-journal.com/software/sj20-4
SJ20-4 st0620. Analysis of regression-discontinuity ... / Analysis of
regression-discontinuity designs / with multiple cutoffs or multiple
scores / by Matias D. Cattaneo, Princeton University, / Princeton, NJ /
Rocio Titiunik, Princeton University, / Princeton, NJ / Gonzalo
st0383_1 from http://www.stata-journal.com/software/sj20-3
SJ20-3 st0383_1. Update: Global search regression ... / Update: Global
search regression (gsreg): A new / automatic model-selection technique for
/ cross-section, time-series, and panel-data / regressions / by Pablo
Gluzmann, CONICET-CEDLAS, UNLP / Demian Panigo, CEIL-CONICET, UNM, UNLP /
st0612 from http://www.stata-journal.com/software/sj20-3
SJ20-3 st0612. Endogenous switching regression ... / Endogenous switching
regression model and / treatment effects with count data outcome / by
Takuya Hasebe, Sophia University, Tokyo, / Japan / Support:
thasebe@sophia.ac.jp / After installation, type help {cmd:escount}, /
gr0083 from http://www.stata-journal.com/software/sj20-2
SJ20-2 gr0083. Visualization strategies for ... / Visualization strategies
for regression / estimates with randomization inference / by Marshall A.
Taylor, Department of Sociology, / New Mexico State University, Las
Cruces, / NM / Support: mtaylor2@nmsu.edu / DOI:
st0598 from http://www.stata-journal.com/software/sj20-2
SJ20-2 st0598. Estimation method for ... / Estimation method for
sample-selection models / based on extremal quantile regressions / by
Xavier D'Haultfoeuille, CREST-ENSAE, Paris, / France / Arnaud Maurel, Duke
University, NBER, and / IZA, Durham, NC / Xiaoyun Qiu, Northwestern
st0600 from http://www.stata-journal.com/software/sj20-2
SJ20-2 st0600. Nonparametric testing for ... / Nonparametric testing for
significance of / regressors and for the presence of / measurement error /
by Young Jun Lee, University of Copenhagen, / Copenhagen, Denmark / Daniel
Wilhelm, University College London, / CeMMAP / Support: yjl@econ.ku.dk,
st0604 from http://www.stata-journal.com/software/sj20-2
SJ20-2 st0604. Method of moment estimators from ... / Method of moment
estimators from Jochmans (2017) / for fitting exponential regression
models / with two-way fixed effects from a panel data / with self links /
by Koen Jochmans, University of Cambridge / Vincenzo Verardi, Universite
st0588 from http://www.stata-journal.com/software/sj20-1
SJ20-1 st0588. Seemingly unrelated recentered ... / Seemingly unrelated
recentered influence / function regression / by Fernando Rios-Avila, Levy
Economics Institute / of Bard College, Annandale-on-Hudson, NY / Support:
friosavi@levy.org / After installation, type help {cmd:hvar}, /
st0589 from http://www.stata-journal.com/software/sj20-1
SJ20-1 st0589. Poisson pseudolikelihood regression ... / Poisson
pseudolikelihood regression with / multiple levels of fixed effects / by
Sergio Correia, Federal Reserve Board, / Washington, DC / Paulo Guimaraes,
Banco de Portugal, Porto, / Portugal / Tom Zylkin, University of Richmond,
st0590 from http://www.stata-journal.com/software/sj20-1
SJ20-1 st0590. Recommendations about estimating ... / Recommendations
about estimating / errors-in-variables regression in Stata / by J. R.
Lockwood, Educational Testing Service, / Princeton, NJ / Daniel F.
McCaffrey, Educational Testing / Service, Princeton, NJ / Support:
st0568 from http://www.stata-journal.com/software/sj19-3
SJ19-3 st0568. Two-sample instrumental-variables ... / Two-sample
instrumental-variables regression / with potentially weak instruments / by
Jaerim Choi, University of Hawaii at Manoa, / Honolulu, HI / Shu Shen,
University of California, Davis, / Davis, CA / Support:
st0573 from http://www.stata-journal.com/software/sj19-3
SJ19-3 st0573. Dynamic panel-data model allowing ... / Dynamic panel-data
model allowing threshold and / endogeneity (regression) / by Myung Hwan
Seo, Department of Economics and / Institute of Economic Research, Seoul /
National University, Seoul, Korea / Sueyoul Kim, Department of Economics,
st0558 from http://www.stata-journal.com/software/sj19-2
SJ19-2 st0558. Generalized two-part fractional ... / Generalized two-part
fractional regression with / cmp / by Jesper N. Wulff, Aarhus University,
Aarhus, / Denmark / Support: jwulff@econ.au.dk / DOI:
10.1177/1536867X19854017
st0554 from http://www.stata-journal.com/software/sj19-1
SJ19-1 st0554. Power calculations for... / Power calculations for
regression-discontinuity / designs using robust bias-corrected local /
polynomial inference / by Matias D. Cattaneo, University of Michigan, /
Ann Arbor, MI / Rocio Titiunik, University of Michigan, Ann / Arbor, MI /
st0546 from http://www.stata-journal.com/software/sj18-4
SJ18-4 st0546. Generalized continuation-ratio... / Generalized
continuation-ratio regression models / by Shawn Bauldry, Purdue
University, West / Lafayette, IN / Jun Xu, Ball State University, Muncie,
IN / Andrew S. Fullerton, Oklahoma State / University, Stillwater, OK /
st0547 from http://www.stata-journal.com/software/sj18-4
SJ18-4 st0547. Nonparametric instrumental-variable.. / Nonparametric
instrumental-variable regression / of a scalar outcome on a scalar
endogenous / regressor and a vector of exogenous / by Denis Chetverikov,
Department of Economics, / University of California, Los Angeles, Los /
st0548 from http://www.stata-journal.com/software/sj18-4
SJ18-4 st0548. Regression with heteroskedasticity... / Regression with
heteroskedasticity and / autocorrelation robust (HAR) standard errors / by
Xiaoqing Ye, School of Mathematics and / Statistics, South-Central
University for / Nationalities, Wuhan, China / Yixiao Sun, Department of
st0279_2 from http://www.stata-journal.com/software/sj18-1
SJ18-1 st0279_2. Update: Generalized Poisson... / Update: Generalized
Poisson regression / by Tammy Harris, Department of Epidemiology and /
Biostatistics, University of South / Carolina / Zhao Yang, Quintiles, Inc.
/ James W. Hardin, Department of Epidemiology / and Biostatistics,
st0336_1 from http://www.stata-journal.com/software/sj18-1
SJ18-1 st0336_1. Update: Negative binomial(p)... / Update: Negative
binomial(p) regression models / by James W. Hardin, Department of
Epidemiology / and Biostatistics, University of South / Carolina / Joseph
M. Hilbe, School of Social and Family / Dynamics, Arizona State University
st0337_1 from http://www.stata-journal.com/software/sj18-1
SJ18-1 st0337_1. Update: Estimation and testing of... / Update: Estimation
and testing of binomial and / beta-binomial regression models with and /
without zero inflation / by James W. Hardin, Department of Epidemiology /
and Biostatistics, University of South / Carolina / Joseph M. Hilbe,
st0378_1 from http://www.stata-journal.com/software/sj18-1
SJ18-1 st0378_1. Update: Regression models for... / Update: Regression
models for truncated / distributions / by James W. Hardin, Department of
Epidemiology / and Biostatistics, University of South / Carolina / Joseph
M. Hilbe, School of Social and Family / Dynamics, Arizona State University
st0513 from http://www.stata-journal.com/software/sj18-1
SJ18-1 st0513. Mixture beta regression model / Mixture beta regression
model / by Laura A. Gray, School of Health and Related / Research, Health
Economics and Decision / Science, University of Sheffield, / Sheffield, UK
/ Monica Hernandez-Alava, School of Health and / Related Research, Health
st0393_2 from http://www.stata-journal.com/software/sj17-4
SJ17-4 st0393_2. Update: Estimating almost-ideal... / Update: Estimating
almost-ideal demand systems / with endogenous regressors / by S\xe9bastien
Lecocq, INRA, ALISS, Paris, France / Jean-Marc Robin, Sciences Po, Paris,
France, / and UCL, London, UK / Support: Sebastien.Lecocq@ivry.inra.fr /
st0400_1 from http://www.stata-journal.com/software/sj17-3
SJ17-3 st0400_1. Update: Penalized logistic regression / Update: Penalized
logistic regression / by Andrea Discacciati, Unit of Biostatistics and /
Unit of Nutritional Epidemiology, / Institute of Environmental Medicine, /
Karolinska Institutet, Stockholm, Sweden / Nicola Orsini, Unit of
st0491 from http://www.stata-journal.com/software/sj17-3
SJ17-3 st0491. Goodness-of-fit tests for ordinal logistic regression /
Goodness-of-fit tests for ordinal logistic / regression / by Morten W.
Fagerland, Oslo Centre for / Biostatistics and Epidemiology, Research /
Support Services, Oslo University / Hospital, Oslo, Norway / David W.
st0366_1 from http://www.stata-journal.com/software/sj17-2
SJ17-2 st0366_1. Update: Local polynomial... / Update: Local polynomial
regression-discontinuity / estimation with robust bias-corrected
confidence / intervals and inference procedures / by Sebastian Calonico,
University of Miami, / Miami, FL / Matias D. Cattaneo, University of
st0474 from http://www.stata-journal.com/software/sj17-2
SJ17-2 st0474. Estimate time-dependent generalized... / Estimate
time-dependent generalized estimating / equation weights used to perform
regression / conditioning on continuation (RCC), and / perform RCC if
requested / by Eric J. Daza, Stanford Prevention Research / Center,
st0469 from http://www.stata-journal.com/software/sj17-1
SJ17-1 st0469. Erickson-Whited linear... / Erickson-Whited linear
errors-in-variables panel / regression with identification from /
higher-order cumulants and moments / by Timothy Erickson, Bureau of Labor
Statistics, / Washington, DC / Robert Parham, University of Rochester, /
st0456 from http://www.stata-journal.com/software/sj16-4
SJ16-4 st0456. A generalized regression-adjustment.. / A generalized
regression-adjustment estimator / for average treatment effects from panel
data / by David M. Drukker, StataCorp, College Station, TX / Support:
ddrukker@stata.com
st0455 from http://www.stata-journal.com/software/sj16-3
SJ16-3 st0455. Estimation of panel vector... / Estimation of panel vector
autoregression / in Stata / by Michael R. M. Abrigo, Philippine Institute
/ for Development Studies, Philippines / Inessa Love, Department of
Economics, / University of Hawai`i at Manoa, USA / Support:
st0433 from http://www.stata-journal.com/software/sj16-2
SJ16-2 st0433. Bivariate count regression models / Bivariate count
regression models / by Xinling Xu, Department of Epidemiology and /
Biostatistics, University of South / Carolina / James W. Hardin,
Department of Epidemiology / and Biostatistics, Institute for Families /
st0438 from http://www.stata-journal.com/software/sj16-2
SJ16-2 st0438. Fixed effects in unconditional... / Fixed effects in
unconditional quantile / regression / by Nicolai T. Borgen, Department of
Sociology / and Human Geography, University of Oslo / Support:
n.t.borgen@sosgeo.uio.no / After installation, type help xtrifreg
st0393_1 from http://www.stata-journal.com/software/sj16-1
SJ16-1 st0393_1. Update: Estimating almost-ideal... / Update: Estimating
almost-ideal demand systems / with endogenous regressors / by S\xe9bastien
Lecocq, INRA, ALISS, Paris, France / Jean-Marc Robin, Sciences Po, Paris,
France, / and UCL, London, UK / Support: Sebastien.Lecocq@ivry.inra.fr /
st0419 from http://www.stata-journal.com/software/sj16-1
SJ16-1 st0419. Regressions are commonly... / Regressions are commonly
misinterpreted / by David C. Hoaglin, Independent consultant, / Sudbury,
MA / Support: dchoaglin@gmail.com
st0429 from http://www.stata-journal.com/software/sj16-1
SJ16-1 st0429. BICOP generalized bivariate... / BICOP generalized
bivariate ordinal regression / model / by Monica Hernandez Alava, ScHARR,
HEDS, / University of Sheffield, UK / Stephen Pudney, Institute for Social
and / Econmic Research, University of Essex, UK / Support:
st0156_2 from http://www.stata-journal.com/software/sj15-4
SJ15-4 st0156_2. Update: Multivariate... / Update: Multivariate
random-effects / meta-regression / by Ian White, MRC Biostatistics Unit,
Cambridge, / UK / Support: ian.white@mrc-bsu.cam.ac.uk / After
installation, type help mvmeta and / mvmeta_make
st0202_1 from http://www.stata-journal.com/software/sj15-3
SJ15-3 st0202_1. Update: Regression analysis... / Update: Regression
analysis of censored data / using pseudo-observations / by Morten
Overgaard, Aarhus University, Aarhus, / Denmark / Per K. Andersen,
University of Copenhagen, / Copenhagen, Denmark / Erik T. Parner, Aarhus
st0391_1 from http://www.stata-journal.com/software/sj15-3
SJ15-3 st0391_1. Update: Nomogram for... / Update: Nomogram for logistic
regression / by Alexander Zlotnik, Technical University of / Madrid,
Department of Electronic / Engineering, Madrid, Spain and Admissions, /
Clinical Documentation and Clinical / Information Systems Department,
st0397 from http://www.stata-journal.com/software/sj15-3
SJ15-3 st0397. Prediction in linear index... / Prediction in linear index
models with / endogenous regressors / by Christoper L. Skeels, Department
of / Economics, University of Melbourne, / Melbourne, Australia / Larry W.
Taylor , Department of Economics, / Lehigh University, Bethlehem, PA /
st0400 from http://www.stata-journal.com/software/sj15-3
SJ15-3 st0400. Penalized logistic regression / Penalized logistic
regression / by Andrea Discacciati, Unit of Biostatistics and / Unit of
Nutritional Epidemiology, / Institute of Environmental Medicine, /
Karolinska Institutet, Stockholm, Sweden / Nicola Orsini, Unit of
st0279_1 from http://www.stata-journal.com/software/sj15-2
SJ15-2 st0279_1. Update: Generalized Poisson... / Update: Generalized
Poisson regression / by Tammy Harris, Department of Epidemiology and /
Biostatistics, University of South / Carolina / Zhao Yang, Quintiles, Inc.
/ James W. Hardin, Department of Epidemiology / and Biostatistics,
st0383 from http://www.stata-journal.com/software/sj15-2
SJ15-2 st0383. Global search regression... / Global search regression
(gsreg): A new / automatic model-selection technique for / cross-section,
time-series, and panel-data / by Pablo Gluzmann, CONICET-CEDLAS, UNLP /
Demian Panigo, CEIL-CONICET, UNM, UNLP / Support: gluzmann@yahoo.com /
st0391 from http://www.stata-journal.com/software/sj15-2
SJ15-2 st0391. Nomogram for logistic regression / Nomogram for logistic
regression / by Alexander Zlotnik, Technical University of / Madrid,
Department of Electronic / Engineering, Madrid, Spain and Admissions, /
Clinical Documentation and Clinical / Information Systems Department,
st0393 from http://www.stata-journal.com/software/sj15-2
SJ15-2 st0393. Estimating almost-ideal... / Estimating almost-ideal demand
systems with / endogenous regressors / by Sebastien Lecocq, INRA, ALISS,
Paris, France / Jean-Marc Robin, Sciences Po, Paris, France, / and UCL,
London, UK / Support: Sebastien.Lecocq@ivry.inra.fr / After
gr0059_1 from http://www.stata-journal.com/software/sj15-1
SJ15-1 gr0059_1. Update: Plotting regression... / Update: Plotting
regression coefficients and / other estimates / by Ben Jann, Institute of
Sociology, University / of Bern, Bern, Switzerland / Support:
ben.jann@soz.unibe.ch / After installation, type help coefplot
st0213_2 from http://www.stata-journal.com/software/sj15-1
SJ15-1 st0213_2. Update: Variable selection... / Update: Variable
selection in linear regression / by Charles Lindsey, StataCorp, / College
Station, TX / Simon Sheather, Texas A&M Statistics, / College Station, TX
/ Support: clindsey@stata.com / After installation, type help vselect
st0378 from http://www.stata-journal.com/software/sj15-1
SJ15-1 st0378. Regression models for... / Regression models for truncated
distributions / by James W. Hardin, Institute for Families in / Society,
Department of Epidemiology and / Biostatistics, University of South /
Carolina, Columbia, SC / Joseph M. Hilbe, School of Social and Family /
gr0059 from http://www.stata-journal.com/software/sj14-4
SJ14-4 gr0059. Plotting regression... / Plotting regression coefficients
and other / estimates / by Ben Jann, Institute of Sociology, University /
of Bern, Bern, Switzerland / Support: ben.jann@soz.unibe.ch / After
installation, type help coefplot
st0359 from http://www.stata-journal.com/software/sj14-4
SJ14-4 st0359. Double-hurdle regression / Double-hurdle regression / by
Christoph Engel, Max Planck Institute for / Research on Collective Goods,
Bonn, / Germany / Peter G. Moffatt, School of Economics, / University of
East Anglia, Norwich, UK / Support: engel@coll.mpg.de, /
st0366 from http://www.stata-journal.com/software/sj14-4
SJ14-4 st0366. Robust data-driven inference... / Robust data-driven
inference in the regression- / discontinuity design / by Sebastian
Calonico, University of Miami, / Coral Gables, FL / Matias D. Cattaneo,
University of Michigan, / Ann Arbor, MI / Rocio Titiunik, University of
st0367 from http://www.stata-journal.com/software/sj14-4
SJ14-4 st0367. adjcatlogit, ccrlogit, and... / adjcatlogit, ccrlogit, and
ucrlogit: Estimating / ordinal logistic regression models / by Morten W.
Fagerland, Unit of Biostatistics / and Epidemiology, Oslo University /
Hospital, Norway / Support: morten.fagerland@medisin.uio.no / After
st0085_2 from http://www.stata-journal.com/software/sj14-2
SJ14-2 st0085_2. Update: Making regression... / Update: Making regression
tables from stored / estimates / by Ben Jann, University of Bern /
Support: jann@soz.unibe.ch / After installation, type help estout, /
esttab, eststo, estadd, and estpost
st0336 from http://www.stata-journal.com/software/sj14-2
SJ14-2 st0336. Negative binomial(p)... / Negative binomial(p) regression
models / by James W. Hardin, Department of Epidemiology / and
Biostatistics, Institute for Families / in Society, University of South
Carolina, / Columbia, SC / Joseph M. Hilbe, School of Social and Family /
st0337 from http://www.stata-journal.com/software/sj14-2
SJ14-2 st0337. Estimation and Testing of... / Estimation and Testing of
Binomial and Beta- / Binomial Regression Models with and without / Zero
Inflation / by James W. Hardin, Department of Epidemiology / and
Biostatistics, Institute for Families / in Society, University of South
st0342 from http://www.stata-journal.com/software/sj14-2
SJ14-2 st0342. Power for multiple regression... / Power for multiple
regression with three / predictors / by Christopher L. Aberson, Department
of / Psychology, Humboldt State University, / Arcata, CA / Support:
chris.aberson@humboldt.edu / After installation, type help powersim3
st0317 from http://www.stata-journal.com/software/sj13-4
SJ13-4 st0317. Double-hurdle regression / Double-hurdle regression / by
Bruno Garcia, The College of William and Mary / Support:
bsgarcia@email.wm.edu / After installation, type help dblhurdle
st0294_1 from http://www.stata-journal.com/software/sj13-3
SJ13-3 st0294_1. Update: Laplace regression / Update: Laplace regression /
by Matteo Bottai, Unit of Biostatistics, / Institute of Environmental
Medicine, / Karolinska Institutet, Stockholm, Sweden / Nicola Orsini, Unit
of Biostatistics and Unit / of Nutritional Epidemiology, Institute of /
st0291 from http://www.stata-journal.com/software/sj13-2
SJ13-2 st0291. Maximum likelihood and... / Maximum likelihood and
generalized spatial two- / stage least-squares estimators for a spatial- /
autoregressive model with spatial-autoregressive / disturbances / by David
M. Drukker, StataCorp, College Station, TX / Ingmar R. Prucha, Department
st0293 from http://www.stata-journal.com/software/sj13-2
SJ13-2 st0293. A command for estimating... / A command for estimating
spatial-autoregressive / models with spatial-autoregressive / disturbances
and additional endogenous variables / by David M. Drukker, StataCorp,
College Station, TX / Ingmar R. Prucha, Department of Economics, /
st0294 from http://www.stata-journal.com/software/sj13-2
SJ13-2 st0294. A command for Laplace regression / A command for Laplace
regression / by Matteo Bottai, Unit of Biostatistics, / Institute of
Environmental Medicine, / Karolinska Institutet, Stockholm, Sweden /
Nicola Orsini, Unit of Biostatistics and Unit / of Nutritional
st0285 from http://www.stata-journal.com/software/sj13-1
SJ13-1 st0285. Regression anatomy, revealed / Regression anatomy, revealed
-- / Graphical inspection of linear multivariate / models / by Valerio
Filoso, Department of Economics, / University of Naples "Federico II", /
Naples, Italy / Support: filoso@unina.it / After installation, type help
st0273 from http://www.stata-journal.com/software/sj12-4
SJ12-4 st0273. A generalized missing-indicator approach ... / A
generalized missing-indicator approach to regression / with imputed
covariates / by Dardanoni Valentino, University of Palermo / De Luca
Giuseppe, ISFOL / Modica Salvatore, University of Palermo / Peracchi
st0278 from http://www.stata-journal.com/software/sj12-4
SJ12-4 st0278. Robinson's square root of N consistent ... / Robinson's
square root of N consistent / semiparametric regression estimator / by
Vincenzo Verardi, University of Namur (Centre / for Research in the
Economics of / Development), Namur, Belgium and / Universite Libre de
st0279 from http://www.stata-journal.com/software/sj12-4
SJ12-4 st0279. Generalized Poisson regression / Generalized Poisson
regression / by Tammy Harris, Department of Epidemiology and /
Biostatistics, University of South / Carolina / Zhao Yang, Quintiles, Inc.
/ James W. Hardin, Department of Epidemiology / and Biostatistics,
st0269 from http://www.stata-journal.com/software/sj12-3
SJ12-3 st0269. A generalized Hosmer-Lemeshow... / A generalized
Hosmer-Lemeshow goodness-of-fit test for / multinomial logistic regression
models / by Morten W. Fagerland, Unit of Biostatistics and / Epidemiology,
Oslo University Hospital, Norway / David W. Hosmer, Department of Public
st0272 from http://www.stata-journal.com/software/sj12-3
SJ12-3 st0272. Long-run covariance and its applications... / Long-run
covariance and its applications in cointegration / regression / by Qunyong
Wang, Institute of Statistics and Econometrics, / Nankai University / Na
Wu, Economics School, Tianjin University of Finance / and Economics /
st0231_1 from http://www.stata-journal.com/software/sj12-3
SJ12-3 st0231_1. Update: Logistic quantile regression... / Update:
Logistic quantile regression in Stata / by Nicola Orsini, Unit of
Nutritional Epidemiology / and Unit of Biostatistics, Institute of
Environmental / Medicine, Karolinska Insitutet, Stockholm, Sweden / Matteo
st0087_1 from http://www.stata-journal.com/software/sj12-2
SJ12-2 st0087_1. Update: Boosted regression (boosting):... / Update:
Boosted regression (boosting): An introductory / tutorial and a Stata
plugin / by Matthias Schonlau, RAND / Support: matt@rand.org / After
installation, type help boost
st0257 from http://www.stata-journal.com/software/sj12-2
SJ12-2 st0257. Threshold regression for time-to-event ... / Threshold
regression for time-to-event analysis: / The stthreg package / by Tao
Xiao, Ohio State University, Columbus, OH / G. A. Whitmore, McGill
University, Montreal, Canada / Xin He, University of Maryland ,College
st0242 from http://www.stata-journal.com/software/sj11-4
SJ11-4 st0242. Dynamic simulations of autoregressive... / Dynamic
simulations of autoregressive relationships / by Laron K. Williams,
University of Missouri, Columbia, MO / Guy D. Whitten, Texas A&M
University, College Station, TX / Support: williamslaro@missouri.edu,
st0231 from http://www.stata-journal.com/software/sj11-3
SJ11-3 st0231. Logistic quantile regression in Stata / Logistic quantile
regression in Stata / by Nicola Orsini, Unit of Nutritional Epidemiology /
and Unit of Biostatistics Institute of / Environmental Medicine,
Karolinska Insitutet, / Stockholm, Sweden / Matteo Bottai, Division of
st0156_1 from http://www.stata-journal.com/software/sj11-2
SJ11-2 st0156_1. Update: Multivariate random-effects... / Update:
Multivariate random-effects meta-regression / by Ian White / Support:
ian.white@mrc-bsu.cam.ac.uk / After installation, type help mvmeta and
mvmeta_make
st0213_1 from http://www.stata-journal.com/software/sj11-1
SJ11-1 st0213_1. Update: Variable selection in linear regression /
Update: Variable selection in linear regression / by Charles Lindsey,
StataCorp, College Station, TX / Simon Sheather, Texas A&M Statistics,
College Station, TX / Support: clindsey@stata.com / After installation,
st0219 from http://www.stata-journal.com/software/sj11-1
SJ11-1 st0219. Right-censored Poisson regression model / Right-censored
Poisson regression model / by Rafal Raciborski, StataCorp / Support:
rraciborski@stata.com / After installation, type help rcpoisson
st0213 from http://www.stata-journal.com/software/sj10-4
SJ10-4 st0213. Variable selection in linear regression / Variable
selection in linear regression / by Charles Lindsey, StataCorp, College
Station, TX / Simon Sheather, Texas A&M Statistics, College Station, TX /
Support: clindsey@stata.com / After installation, type help vselect
st0202 from http://www.stata-journal.com/software/sj10-3
SJ10-3 st0202. Regression analysis of censored data using... / Regression
analysis of censored data using / pseudo-observations / by Erik T. Parner,
University of Aarhus, Aarhus, Denmark / Per K. Andersen, University of
Copenhagen, Copenhagen, Denmark / Support: parner@biostat.au.dk / After
st0099_1 from http://www.stata-journal.com/software/sj10-2
SJ10-2 st0099_1. Update: Goodness-of-fit test for a... / Update:
Goodness-of-fit test for a logistic regression / model estimated using
survey sample data / by Kellie J. Archer, Ph.D., Department of
Biostatistics, / Virginia Commonwealth University / Michael I. Lichter,
st0173_1 from http://www.stata-journal.com/software/sj10-2
SJ10-2 st0173_1. Update: MM-robust regression / Update: MM-robust
regression / by Vincenzo Verardi, University of Namur and / Universite
Libre de Bruxelles, Namur, Belgium / Christophe Croux, K. U. Leuven,
Faculty of Business / and Economics, Leuven, Belgium / Support:
st0186 from http://www.stata-journal.com/software/sj10-1
SJ10-1 st0186. Creating synthetic discrete-response... / Creating
synthetic discrete-response regression models / by Joseph M. Hilbe,
Arizona State University and / Jet Propulsion Laboratory, CalTech /
Support: hilbe@asu.edu
st0173 from http://www.stata-journal.com/software/sj9-3
SJ9-3 st0173. MM-robust regression / MM-robust regression / by Vincenzo
Verardi, University of Namur and Universite / Libre de Bruxelles, Namur,
Belgium / Christophe Croux, K. U. Leuven, Faculty of Business and /
Economics, Leuven, Belgium / Support: vverardi@fundp.ac.be,
st0163 from http://www.stata-journal.com/software/sj9-2
SJ9-2 st0163. metandi: Meta-analysis of diagnostic... / metandi:
Meta-analysis of diagnostic accuracy using / hierarchical logistic
regression / by Roger Harbord, University of Bristol / Penny Whiting,
University of Bristol / Support: roger.harbord@bristol.ac.uk / After
sbe23_1 from http://www.stata-journal.com/software/sj8-4
SJ8-4 sbe23_1. Update: Meta-regression in Stata (revised) / Update:
Meta-regression in Stata (revised) / by Roger Harbord, Department of
Social Medicine, / University of Bristol, UK / Julian Higgins, MRC
Biostatistics Unit, Cambridge, UK / Support: roger.harbord@bristol.ac.uk
st0151 from http://www.stata-journal.com/software/sj8-4
SJ8-4 st0151. The Blinder-Oaxaca decomposition for linear... / The
Blinder-Oaxaca decomposition for linear regression / models / by Ben Jann,
ETH Zurich / Support: jannb@ethz.ch / After installation, type help
oaxaca
st0128 from http://www.stata-journal.com/software/sj7-3
SJ7-3 st0128. Robust standard errors for panel regressions... / Robust
standard errors for panel regressions with / cross-sectional dependence /
by Daniel Hoechle, University of Basel, Switzerland / Support:
daniel.hoechle@unibas.ch / After installation, type help xtscc and
st0085_1 from http://www.stata-journal.com/software/sj7-2
SJ7-2 st0085_1. Update: Making regression tables simplified / Update:
Making regression tables simplified / by Ben Jann, ETH Zurich / Support:
jann@soz.gess.ethz.ch / After installation, type help estout, esttab,
eststo, and estadd
st0120 from http://www.stata-journal.com/software/sj7-1
SJ7-1 st0120. Multivariable regression spline models / Multivariable
regression spline models / by Patrick Royston, UK Medical Research Council
/ Willi Sauerbrei, University Medical Center, Freiberg, Germany / Support:
patrick.royston@ctu.mrc.ac.uk / After installation, type help mvrs,
st0053_3 from http://www.stata-journal.com/software/sj6-4
SJ6-4 st0053_3. Update: From the help desk: Local polynomial... /
Update: From the help desk: Local polynomial regression / and Stata
plugins / by Roberto G. Gutierrez, StataCorp / Jean Marie Linhart,
StataCorp / Jeffrey S. Pitblado, StataCorp / Support:
st0105_1 from http://www.stata-journal.com/software/sj6-4
SJ6-4 st0105_1. Update: Multinomial treatment effects of... / Update:
Multinomial treatment effects of a negative binomial / regression model /
by Partha Deb, Hunter College, City University of New York / Pravin K.
Trivedi, Indiana University / Support: partha.deb@hunter.cuny.edu,
st0109 from http://www.stata-journal.com/software/sj6-3
SJ6-3 st0109. Linear partial regression / Linear partial regression / by
Michael Lokshin, The World Bank / Support: mlokshin@worldbank.org /
After installation, type help plreg
st0045_2 from http://www.stata-journal.com/software/sj6-2
SJ6-2 st0045_2. Update: Multivariate probit regression... / Multivariate
probit regression using simulated maximum / likelihood / by Lorenzo
Cappellari, Universit\xe0 Cattolica, Milano, Italy / Stephen Jenkins, ISER,
University of Essex, Colchester, UK / Support:
st0105 from http://www.stata-journal.com/software/sj6-2
SJ6-2 st0105. Multinomial treatment effects of a negative... /
Multinomial treatment effects of a negative binomial / regression model /
by Partha Deb, Hunter College, City University of New York / Pravin K.
Trivedi, Indiana University / Support: partha.deb@hunter.cuny.edu,
st0099 from http://www.stata-journal.com/software/sj6-1
SJ6-1 st0099. Goodness-of-fit test for a logistic regression... /
Goodness-of-fit test for a logistic regression model fitted / using survey
sample data / by Kellie J. Archer, Ph.D., Department of Biostatistics, /
Virginia Commonwealth University / Stanley Lemeshow, Ph.D., School of
st0094 from http://www.stata-journal.com/software/sj5-4
SJ5-4 st0094. Confidence intervals for predicted outcomes... / Confidence
intervals for predicted outcomes in regression / models for categorical
outcomes / by Jun Xu and J. Scott Long, Indiana University / Support:
spostsup@indiana.edu / After installation, type help prvalue and
sg139_1 from http://www.stata-journal.com/software/sj5-3
SJ5-3 sg139_1. Logistic regression when binary outcome is... / Logistic
regression when binary outcome is measured with / uncertainty / by Mario
Cleves, UAMS Department of Pediatrics, / Arkansas Center for Birth Defects
/ Alberto Tosetto, S. Bortolo Hospital, Vicenza, Italy / Support:
st0071_2 from http://www.stata-journal.com/software/sj5-3
SJ5-3 st0071_2. Maximum likelihood estimation of models... / Maximum
likelihood estimation of endogenous switching / regression models / by
Michael Lokshin, World Bank / Zurab Sajaia, World Bank / Support:
mlokshin@worldbank.org / After installation, type help movestay
st0085 from http://www.stata-journal.com/software/sj5-3
SJ5-3 st0085. Making regression tables from stored... / Making regression
tables from stored estimates / by Ben Jann, ETH Zurich / Support:
jann@soz.gess.ethz.ch / After installation, type help estout,
estoutdef, / and estadd
st0087 from http://www.stata-journal.com/software/sj5-3
SJ5-3 st0087. Boosted regression (boosting): An ... / Boosted regression
(boosting): An introductory tutorial / and a Stata plugin / by Matthias
Schonlau, RAND / Support: matt@rand.org / After installation, type help
boost
st0045_1 from http://www.stata-journal.com/software/sj5-2
SJ5-2 st0045_1. Multivariate probit regression using... / Multivariate
probit regression using simulated maximum likelihood: / update / by
Lorenzo Cappellari, Universita del Piemonte Orientale and / University of
Essex / Stephen P. Jenkins, University of Essex / Support:
st0053_2 from http://www.stata-journal.com/software/sj5-2
SJ5-2 st0053_2. From the help desk: Local polynomial... / From the help
desk: Local polynomial regression and Stata plugins / by Roberto G.
Gutierrez, Jean Marie Linhart, and Jeffrey S. Pitblado / StataCorp /
Support: rgutierrez@stata.com / After installation, type help locpoly
st0053_1 from http://www.stata-journal.com/software/sj5-1
SJ5-1 st0053_1. From the help desk: Local polynomial and... / From the
help desk: Local polynomial regression and Stata / by Roberto G.
Gutierrez, StataCorp / Jean Marie Linhart, StataCorp / Jeffrey S.
Pitblado, StataCorp / Support: rgutierrez@stata.com / After
st0071 from http://www.stata-journal.com/software/sj4-3
SJ4-3 st0071. Maximum likelihood estimation of endogenous ... / Maximum
likelihood estimation of endogenous switching / regression models / by
Michael Lokshin and Zurab Sajaia, The World Bank, US / Support:
mlokshin@worldbank.org, zsajaia@worldbank.org / After installation, type
st0004_2 from http://www.stata-journal.com/software/sj4-2
SJ4-2 st0004_2. Update to residual diagnostics for ... / Update to
residual diagnostics for cross-section time-series / regression models /
by C. F. Baum, Boston College / Support: baum@bc.edu / After
installation, see help xttest2 and xttest3
st0050 from http://www.stata-journal.com/software/sj3-4
SJ3-4 st0050. Regression-calibration method for fitting ... /
Regression-calibration method for fitting generalized linear / models with
additive measurement error / by James W. Hardin, University of South
Carolina / Henrik Schmiediche, Texas A&M University / Raymond J. Carroll,
st0053 from http://www.stata-journal.com/software/sj3-4
SJ3-4 st0053. Local polynomial regression and Stata plugins / Local
polynomial regression and Stata plugins / by Roberto G. Gutierrez,
StataCorp, LP / Jean Marie Linhart, StataCorp, LP / Jeffrey S. Pitblado,
StataCorp, LP / Support: rgutierrez@stata.com / After installation, type
st0045 from http://www.stata-journal.com/software/sj3-3
SJ3-3 st0045. Multivariate probit regression using ... / Multivariate
probit regression using simulated maximum / likelihood / by Lorenzo
Cappellari, Universita del Piemonte Orientale and / University of Essex /
Stephen P. Jenkins, University of Essex / Support: stephenj@essex.ac.uk
st0004_1 from http://www.stata-journal.com/software/sj3-2
SJ3-2 st0004_1. Update to residual diagnostics for ... / Update to
residual diagnostics for cross-sectional time-series / regression models /
by C. F. Baum, Boston College / Support: baum@bc.edu / After
installation, see help xttest2
st0024 from http://www.stata-journal.com/software/sj2-4
SJ2-4 st0024. Using Aalen's linear hazards model to ... / Using Aalen's
linear hazards model to investigate / time-varying effects in the
proportional hazards / regression model / by David W. Hosmer, University
of Massachusetts / Patrick Royston, UK Medical Research Council / Support:
st0008 from http://www.stata-journal.com/software/sj2-1
SJ2-1 st0008. Analysis of quantitative traits using regression ... /
Analysis of quantitative traits using regression and log-linear / modeling
when phase is unknown. / by Adrian Mander, MRC Biostatistics Unit,
Cambridge, UK / Support: adrian.mander@mrc-bsu.cam.ac.uk / After
st0004 from http://www.stata-journal.com/software/sj1-1
SJ1-1 st0004. Residual diagnostics for cross-sectional time series /
Residual diagnostics for cross-sectional time series regression models. /
by C. F. Baum, Boston College / Support: baum@bc.edu / After
installation, see help xttest2, xttest3
sg163 from http://www.stata.com/stb/stb61
STB-61 sg163. Stereotype Ordinal Regression / STB insert by Mark Lunt,
ARC Epidemiology Unit, / University of Manchester, UK / Support:
mdeasml2@fs1.ser.man.ac.uk / After installation, see help soreg
sg97_3 from http://www.stata.com/stb/stb59
STB-59 sg97_3. Update to formatting regression output / STB insert by
John Luke Gallup, developIT.org / Support:
john_gallup@alum.swarthmore.edu / After installation, see help outreg
sg156 from http://www.stata.com/stb/stb58
STB-58 sg156. Mean score method for missing covariate data in ... / Mean
score method for missing covariate data in logistic / regression models /
STB insert by Marie Reilly, Epidemiology & Public Health, Univ College, /
Cork, Ireland / Agus Salim, Department of Statistics, University College,
sg157 from http://www.stata.com/stb/stb58
STB-58 sg157. Predicted values calculated from linear or logistic ... /
Predicted values calculated from linear or logistic regression models /
STB insert by Joanne M. Garrett, University of North Carolina / Support:
garrettj@med.unc.edu / After installation, see help predcalc
sg97_2 from http://www.stata.com/stb/stb58
STB-58 sg97_2. Update to formatting regression output / STB insert by
John Luke Gallup, developIT.org / Support:
john_gallup@alum.swarthmore.edu / After installation, see help outreg
sg152 from http://www.stata.com/stb/stb57
STB-57 sg152. Listing and interpreting transformed coef. from ... /
Listing and interpreting transformed coefficients from certain /
regression models / STB insert by J. Scott Long, Indiana University /
Jeremy Freese, University of Wisconsin-Madison / Support:
sbe37 from http://www.stata.com/stb/stb56
STB-56 sbe37. Special restrictions in multinomial logistic regression /
STB insert by John Hendrickx, University of Nijmegen, Netherlands /
Support: j.hendrickx@bw.kun.nl / After installation, see help mclest
and help mclgen
sg145 from http://www.stata.com/stb/stb56
STB-56 sg145. Scalar measures of fit for regression models / STB insert
by J. Scott Long, Indiana University / Jeremy Freese, University of
Wisconsin-Madison / Support: jslong@indiana.edu / jfreese@ssc.wisc.edu
/ After installation, see help fitstat
sg148 from http://www.stata.com/stb/stb56
STB-56 sg148. Profile likelihood confidence intervals for explanatory /
variables in logistic regression / AUTHOR: Mark S. Pearce University of
Newcastle upon Tyne, UK / Support: m.s.pearce@ncl.ac.uk / After
installation, see help logprof
sg135 from http://www.stata.com/stb/stb55
STB-55 sg135. Test for autoregressive conditional heteroskedasticity ... /
Test for autoregressive conditional heteroskedasticity in regression /
error distribution. / STB insert by Christopher F. Baum, Boston College /
Vince Wiggins, Stata Corporation / Support: baum@bc.edu /
sg139 from http://www.stata.com/stb/stb55
STB-55 sg139. Logistic regression when binary outcome is measured ... /
Logistic regression when binary outcome is measured with uncertainty. /
STB insert by Mario Cleves, Stata Corporation / Alberto Tosetto, S.
Bortolo Hospital, Vicenza, Italy / Support: mcleves@stata.com /
sg130 from http://www.stata.com/stb/stb54
STB-54 sg130. Box-Cox regression models / STB insert by David M. Drukker,
Stata Corporation / Support: ddrukker@stata.com / After installation,
see help boxcox2
sg122 from http://www.stata.com/stb/stb52
STB-52 sg122: Truncated regression / STB insert by Ronna Cong, Stata
Corporation / Support: rcong@stata.com / After installation, see help
truncreg
dm66_2 from http://www.stata.com/stb/stb51
STB-51 dm66_2. Update of cut to Stata 6. / STB insert by / David Clayton,
MRC Biostatistical Research Unit, Cambridge; / Michael Hills (retired). /
Support: david.clayton@mrc-bsu.cam.ac.uk and mhills@regress.demon.co.uk
/ After installation, see help cutv5 (for Stata 5 version) or help
dm66_1 from http://www.stata.com/stb/stb50
STB-50 dm66_1. Stata 6: recode variables using grouped values. / STB
insert by / David Clayton, MRC Biostatistical Research Unit, Cambridge; /
Michael Hills (retired). / Support: david.clayton@mrc-bsu.cam.ac.uk and
mhills@regress.demon.co.uk / After installation, see help cut.
dm66 from http://www.stata.com/stb/stb49
STB-49 dm66. Recoding variables using grouped values. / STB insert by /
David Clayton, MRC Biostatistical Research Unit, Cambridge; / Michael
Hills (retired). / Support: david.clayton@mrc-bsu.cam.ac.uk and
mhills@regress.demon.co.uk / After installation, see help cut.
gr37 from http://www.stata.com/stb/stb49
STB-49 gr37. Cumulative distribution function plots. / STB insert by /
David Clayton, MRC Biostatistical Research Unit, Cambridge; / Michael
Hills (retired). / Support: david.clayton@mrc-bsu.cam.ac.uk and
mhills@regress.demon.co.uk / After installation, see help cdf.
sg99 from http://www.stata.com/stb/stb47
STB-47 sg99. Multiple regression with missing obs. for some variables. /
STB insert by Mead Over, World Bank. / Support: meadover@worldbank.org /
After installation, see help regmsng.
sg102 from http://www.stata.com/stb/stb47
STB-47 sg102. Zero-truncated Poisson and negative binomial regression. /
STB insert by Joseph Hilbe, Arizona State University. / Support:
hilbe@asu.edu / After installation, see help trpois0 and help
trnbin0.
sg94 from http://www.stata.com/stb/stb46
STB-46 sg94. Right, left, and uncensored Poisson regression. / STB insert
by / Joseph Hilbe, Arizona State University; / Dean H. Judson, University
of Nevada. / Support: hilbe@asu.edu and djudson@unr.edu / After
installation, see help cenpois.
sg96 from http://www.stata.com/stb/stb46
STB-46 sg96. Zero-inflated Poisson and neg. binomial regression models. /
STB insert by Jesper B. Sorensen, University of Chicago. / Support:
jesper.sorensen@gsbpop.uchicago.edu / After installation, see help
zipois.
sg97 from http://www.stata.com/stb/stb46
STB-46 sg97. Formatting regression output for published tables. / STB
insert by John Luke Gallup, developIT.org / Support:
john_gallup@alum.swarthmore.edu / After installation, see help outreg.
sg98 from http://www.stata.com/stb/stb46
STB-46 sg98. Poisson regression with a random effect. / STB insert by
David Clayton, MRC Biostatistical Unit, Cambridge. / Support:
david.clayton@mrc-bsu.cam.ac.uk / After installation, see help
rpoisson.
sts13 from http://www.stata.com/stb/stb46
STB-46 sts13. Time series regress. for counts allowing autocorrelation. /
STB insert by / Aurelio Tobias, Institut Municipal d'Investigacio Medica,
Spain; / Michael J. Campbell, University of Sheffield. / Support:
atobias@imim.es and m.j.campbell@sheffield.ac.uk / After installation,
sg93 from http://www.stata.com/stb/stb45
STB-45 sg93. Switching regressions. / STB insert by Frederic Zimmerman,
Stanford University. / Support: zimmer@leland.stanford.edu / After
installation, see help elapse and help switchr. / Note: The example
datasets for this insert have been compressed using a / ZIP-compatible
sg87 from http://www.stata.com/stb/stb44
STB-44 sg87. Windmeijer's goodness-of-fit test for logistic regression. /
STB insert by Jeroen Weesie, Utrecht University, Netherlands. / Support:
weesie@weesie.fsw.ruu.nl / After installation, see help lfitx2.
sbe21 from http://www.stata.com/stb/stb42
STB-42 sbe21. Adjusted pop. attrib. fractions from logistic regression. /
STB insert by Anthony R. Brady, / Public Health Laboratory Service
Statistics Unit, UK. / Support: tbrady@phls.co.uk / After installation,
see help aflogit.
sbe23 from http://www.stata.com/stb/stb42
STB-42 sbe23. Meta-analysis regression. / STB insert by Stephen Sharp,
London School of Hygiene and Tropical / Medicine. / Support:
stephen.sharp@lshtm.ac.uk / After installation, see help metareg.
sg53_2 from http://www.stata.com/stb/stb41
STB-41 sg53_2. Stata-like commands for complementary log-log regression.
/ STB insert by Joseph Hilbe, Arizona State University. / Support:
atjmh@asuvm.inre.asu.edu / After installation, see help bcloglog and
help cloglog.
ssa10_1 from http://www.stata.com/stb/stb41
STB-41 ssa10.1. Analysis of follow-up studies with Stata 5.0. / STB
insert by / David Clayton, MRC Biostatistical Research Unit, Cambridge; /
Michael Hills, London School of Hygiene and Tropical Medicine (retired). /
Support: david.clayton@mrc-bsu.cam.ac.uk and /
ssa10 from http://www.stata.com/stb/stb40
STB-40 ssa10. Analysis of follow-up studies with Stata 5.0. / STB insert
by / David Clayton, MRC Biostatistical Research Unit, Cambridge; / Michael
Hills, London School of Hygiene and Tropical Medicine (retired). /
Support: david.clayton@mrc-bsu.cam.ac.uk and mhills@regress.demon.co.uk
sbe17 from http://www.stata.com/stb/stb39
STB-39 sbe17. Discrete time proportional hazards regression. / STB insert
by Stephen P. Jenkins, / ESRC Research Centre on Micro-Social Change,
University of Essex, UK. / Support: stephenj@essex.ac.uk / After
installation, see help pgmhaz.
sg70 from http://www.stata.com/stb/stb38
STB-38 sg70. Interquantile and simultaneous-quantile regression. / STB
insert by William Gould, Stata Corporation. / Support:
tech-support@stata.com / After installation, see help sqreg and help
iqreg.
sg63 from http://www.stata.com/stb/stb35
STB-35 sg63. Logistic regression: Standardized coef. and partial corr. /
STB insert by Joseph Hilbe, Arizona State University. / Support:
atjmh@asuvm.inre.asu.edu / After installation, see help lstand.
sg53 from http://www.stata.com/stb/stb32
STB-32 sg53. Maximum likelihood complementary log-log regression. / STB
insert by Joseph Hilbe, Arizona State University. / Support:
atjmh@asuvm.inre.asu.edu / After installation, see help cloglog.
sg54 from http://www.stata.com/stb/stb32
STB-32 sg54. Extended probit regression. / STB insert by Joseph Hilbe,
Arizona State University. / Support: atjmh@asuvm.inre.asu.edu / After
installation, see help eprobit.
sts11 from http://www.stata.com/stb/stb32
STB-32 sts11. Hildreth-Lu regression. / STB insert by James W. Hardin,
Stata Corporation. / Support: stata@stata.com / After installation, see
help hlu.
snp10 from http://www.stata.com/stb/stb30
STB-30 snp10. Nonparametric regression: kernel, ASH-WARPing, and k-NN. /
STB insert by I.H. Salgado-Ugarte, M. Shimizu & T. Taniuchi, / Univ. of
Tokyo, Faculty of Agriculture, Department of Fisheries, / Yayoi 1-1-1,
Bunkyo-ku, Tokyo 113, Japan. / Tel. (011)-81-3-3812-2111 ext. 5281 / FAX
sg45 from http://www.stata.com/stb/stb28
STB-28 sg45. Maximum likelihood ridge regression. / STB insert by /
Robert L. Obenchain, / Eli Lilly and Company. / Support: 317-276-3150 /
After installation, see help rxrcrlq, help rxridge, help / rxrmaxl,
help rxrmkdta, help rxrrisk, and help rxrsimu.
sts10 from http://www.stata.com/stb/stb25
STB-25 sts10. Prais-Winsten regression. / STB insert by James Hardin,
Stata Corporation. / Support: stata@stata.com / After installation, see
help prais.
ssa6 from http://www.stata.com/stb/stb22
STB-22 ssa6. Util. for survival analysis with time-varying regressors. /
STB insert by Dr. Philippe Bocquier, CERPOD. / Support:
bocquier@orstom.orstom.fr / After installation, see help censor, help
firstocc, help slice, / and help tmerge.
sts4_1 from http://www.stata.com/stb/stb16
STB-16 sts4_1. A suite of programs for time-series regression. / STB
insert by Sean Becketti, Stata Technical Bulletin. / Support: FAX
913-888-6708 / After installation, see help datevars, help findsmpl,
help / tsfit, help tsmult, help period, help tsreg, and / help
sts4 from http://www.stata.com/stb/stb15
STB-15 sts4. A suite of programs for time-series regression. / STB insert
by Sean Becketti, Stata Technical Bulletin. / Support: FAX 913-888-6708 /
After installation, see help datevars, help findsmpl, help / tsfit,
help tsmult, help period, help tsreg, and / help regdiag. / Also
sg17 from http://www.stata.com/stb/stb13
STB-13 sg17. Regression standard errors in clustered samples. / STB
insert by William Rogers, CRC. / Support: FAX 310-393-7551 / After
installation, see help hreg2.
sqv8 from http://www.stata.com/stb/stb13
STB-13 sqv8. Interpreting multinomial logistic regression. / STB insert
by / Lawrence C. Hamilton, University of New Hampshire; / Carole L.
Seyfrit, Old Dominion University.
sqv6 from http://www.stata.com/stb/stb10
STB-10 sqv6. Smoothed partial residual plots for logistic regression. /
STB insert by Joseph Hilbe, Editor, STB. / Support: FAX 602-860-1446;
Voice 602-860-4331 / After installation, see help lpartr.
sbe7 from http://www.stata.com/stb/stb9
STB-9 sbe7. Hyperbolic regression analysis in biomedical applications. /
STB insert by Paul Geiger, USC School of Medicine. / Support:
pgeiger@vm.usc.edu / After installation, see help hbolic.
sg8_1 from http://www.stata.com/stb/stb9
STB-9 sg8_1. Huber exponential regression. / STB insert by William
Rogers, C.R.C. / Support: FAX 310-393-7551; voice 800-STATAPC / After
installation, see help hereg.
sg10 from http://www.stata.com/stb/stb9
STB-9 sg10. Confidence limits in bivariate linear regression. / STB
insert by Paul Geiger, USC School of Medicine. / Support:
pgeiger@vm.usc.edu / After installation, see help confx.
sg11_1 from http://www.stata.com/stb/stb9
STB-9 sg11_1. Quantile regression with bootstrapped standard errors. /
STB insert by William Gould, C.R.C. / Support: FAX 310-393-9893; voice
800-STATAPC / After installation, see help bsqreg.
sg1_3 from http://www.stata.com/stb/stb8
STB-8 sg1_3. Nonlinear regression command, bug fix. / STB insert by
Patrick Royston, Royal Postgraduate Medical School, London. / Support:
FAX (011)-44-81-740 3119 / After installation, see help nl.
sg1_2 from http://www.stata.com/stb/stb7
STB-7 sg1_2. Nonlinear regression command. / STB insert by Patrick
Royston, Royal Postgraduate Medical School, London. / Support: FAX
(011)-44-81-740 3119 / After installation, see help nl.
gr9 from http://www.stata.com/stb/stb5
STB-5 gr9. Partial residual graphs for linear regression. / STB insert by
Joseph Hilbe, Editor, STB. / Support: FAX (602)-860-1446 / After
installation, see help partres. / Note: In order to use the partres
lowess option, be certain to have / ksm.ado in the same directory or path
smv3 from http://www.stata.com/stb/stb5
STB-5 smv3. Regression based dichotomous discriminant analysis. / STB
insert by Joseph Hilbe, Editor, STB. / Support: FAX (602)-860-1446 /
After installation, see help discrim.
srd7 from http://www.stata.com/stb/stb5
STB-5 srd7. Adjusted summary statistics for logarithmic regressions. /
STB insert by Richard Goldstein, Qualitas, Brighton, MA. / Support:
goldst@harvarda.bitnet / After installation, see help logsumm. / Note:
The logdummy.ado program mentioned in srd7 in STB-5 is found in / the srd8
sqv1_3 from http://www.stata.com/stb/stb4
STB-4 sqv1_3. Enhanced logistic regression program. / STB insert by
Joseph Hilbe, Editor, STB. / Support: FAX (602)-860-1446 / After
installation, see help logiodd2.
sg1_1 from http://www.stata.com/stb/stb3
STB-3 sg1_1. Nonlinear regression (derivative free). / STB insert by
Francesco Danuso, Ph.D., Universita degli Studi di Udine, / Istituto di
Produzione Vegetale, Udine, Italy. / (English adaptation & revision:
Joseph Hilbe, Editor). / Support: FAX 011-39-432-558603 (Danuso) /
srd1 from http://www.stata.com/stb/stb2
STB-2 srd1. Robust regression. / STB insert by Lawrence W. Hamilton, Dept
of Sociology & Anthropology, / University of New Hampshire, Durham, NH
03824-3586. / Support: write / After installation, see help breg.
srd4 from http://www.stata.com/stb/stb2
STB-2 srd4. Test for general specification error in linear regression. /
STB insert by Richard Goldstein, Qualitas. / Support:
goldst@harvarda.bitnet / and 37 Kirkwood Road, Brighton MA 02135. / After
installation, see help pswdiff.
sbe1 from http://www.stata.com/stb/stb1
STB-1 sbe1. Poisson regression with rates. / STB insert by William
Rogers, CRC. / Support: CRC Supported.
sg1 from http://www.stata.com/stb/stb1
STB-1 sg1. Nonlinear regression (derivative free). / STB insert by
Francesco Danuso, Ph.D., / Universita degli Studi di Udine, / Istituto di
Produzione Vegetale, / Udine, Italy. / (English adaptation & revision:
Joseph Hilbe, Editor) / Support: FAX 011-39-432-558603 (Danuso) /
tost from https://alexisdinno.com/stata
tost. Two one-sided tests for equivalence. / Program by Alexis Dinno. /
Support: alexis.dinno@pdx.edu / Version: 3.1.9.2 (updated Feb 5, 2026) /
Distribution-Date: 05feb2026 / / This package includes {cmd:tostt} and
{cmd:tostti} which perform {it:t} tests / for mean equivalence,
recursyst from http://www.econometrics.it/stata
recursyst. Full Information Maximum Likelihood estimation of a
simultaneous three-equation model with mixed latent and observed variables
(version 1.0.1 - 3nov2016). / {cmd:recursyst} allows the FIML estimation
of a three-equation model with endogenous latent / variables as well as
sftfe from http://www.econometrics.it/stata
sftfe. Consistent estimation of fixed-effects stochastic frontier models
(version 1.2.9 19oct2022). / {cmd: sftfe} fits the following fixed-effects
stochastic frontier model: / y_it = alpha_i + beta*X_it + v_it {c 177}
u_it / where v_it is a normally distributed error term and u_it is a
dursel from https://myweb.uiowa.edu/fboehmke/stata
Dursel. A Stata utility for estimating duration models with sample
selection. / Version 2.0. / Distribution-Date: 04nov2009 / Frederick J.
Boehmke, University of Iowa. / / dursel allows the user to estimate
exponential, Weibull or lognormal / duration models accounting for
plotfds from https://myweb.uiowa.edu/fboehmke/stata
plotfds. Plot first differences after a regression command. / Version 1.1.
/ Distribution-Date: 16dec2008 / Frederick J. Boehmke, University of Iowa.
/ / plotfds allows the user to generate and plot first differences for /
various regression models by operating as a front-end for Clarify / (Tomz,
sudcd from https://myweb.uiowa.edu/fboehmke/stata
Sudcd. A Stata utility for estimating seemingly unrelated discrete-choice
duration models. / Version 1.1. / Distribution-Date: 10dec2009 / Frederick
J. Boehmke, University of Iowa. / / sudcd allows the user to estimate
exponential, Weibull or lognormal / seemingly unrelated discrete-choice
loghockey from http://personalpages.manchester.ac.uk/staff/mark.lunt
Piecewise linear ("Hockey-Stick") regression / / A set of programs that
perform piecewise linear regression, with a / single "breakpoint". Either
linear or logistic regression may be used. / Author: Mark Lunt, arc
Epidemiology Unit, University of Manchester / Support:
dr from http://personalpages.manchester.ac.uk/staff/mark.lunt
Doubly Robust Estimation / A program to perform doubly robust estimation
(estimating the effect / of a particular exposure, controlling for
confounders through both a / regression model and inverse probability of
exposure weights / Authors: Mark Lunt, Arthritis Research UK Epidemiology
idi from http://personalpages.manchester.ac.uk/staff/mark.lunt
Indices of improvement in discrimination / Programs to measure various
indices of improvement of a logistic / regression model when a new marker
is added / The first time you run idi or nri you will be asked if you /
are happy to send anonymous data to Google Analytics. For details / of the
recycle from http://www.schonlau.net/stata
adjusted multivariate proportions for logistic regression / Matthias
Schonlau / matt@rand.org / Distribution-Date: 201810920
mvrs from http://www.homepages.ucl.ac.uk/~ucakjpr/stata
mvrs. Package for univariate and multivariable regression spline modelling
/ Programs by Patrick Royston. / Distribution-Date: 20160226 / version:
2.0.1 (uvrs), 2.0.1 (mvrs), 1.2.3 (splinegen) / Please direct queries to
Patrick Royston (j.royston@ucl.ac.uk)
xpredict from http://www.homepages.ucl.ac.uk/~ucakjpr/stata
xpredict. Package for extended prediction for regression models / Program
by Patrick Royston. / Distribution-Date: 20120509 / version: 1.1.0 /
Please direct queries to Patrick Royston (pr@ctu.mrc.ac.uk)
atkplot from https://staskolenikov.net/stata
atkplot -- plot to assess regression residual normality / Author: Stas
Kolenikov, skolenik@unc.edu / This package plots half-normal plot for
regression residuals / with simulated confidence bands as suggested by
A.Atkinson.
calibr from https://staskolenikov.net/stata
calibr -- Stata module for inverse regression / Author: Stas Kolenikov,
skolenik@email.unc.edu / callibr performs the inverse regression and
calibration in / bivariate regression context. I.e., given the value of
the / observed dependent variable, we reconstruct the plausible / values
fsreg from https://staskolenikov.net/stata
fsreg -- Forward search regression / / Author: Stas Kolenikov,
skolenik@unc.edu / This package performs the forward search for the
outlier-free / subset of the data (Riani, Atkinson, 2000). The diagnostic
/ graphs produced by it show the effect of adding observations / on some
aboutreg from https://staskolenikov.net/stata
aboutreg -- Regression diagnostics tutorial / Author: Stas Kolenikov,
skolenik@recep.glasnet.ru / This tutorial was developed for the seminars
on applied / econometrics that the author was giving in Spring 2000 / at a
couple of Russian provincial universities in the / framework of the New
annfit from https://staskolenikov.net/stata
annfit -- Approximation by neural networks / Author: Stas Kolenikov,
skolenik@recep.glasnet.ru / This module performs a version of nonlinear
regression / involving a linear part and a neural network part. / Only
random search for the best approximating neural / network is implemented
regdplot from https://staskolenikov.net/stata
regdplot -- Regression diagnostics on one graph / Author: Stas Kolenikov,
skolenik@unc.edu / / This program displays the main regression
diagnostics / plots on one graph.
ardl from http://www.kripfganz.de/stata
'ARDL': Autoregressive distributed lag regression model / Sebastian
Kripfganz, www.kripfganz.de / Daniel C. Schneider, www.dan-schneider.net /
ardl fits a linear regression model with lags of the dependent / variable
and the independent variables as additional regressors. / Information
kinkyreg from http://www.kripfganz.de/stata
'KINKYREG': Kinky least squares estimation / Sebastian Kripfganz,
www.kripfganz.de / Jan F. Kiviet, sites.google.com/site/homepagejfk/ /
kinkyreg implements the kinky least squares estimator for instrument-free
/ inference under confined regressor endogeneity. An arbitrary number of /
xtdpdbc from http://www.kripfganz.de/stata
'XTDPDBC': Bias-corrected estimation of linear dynamic panel models /
Sebastian Kripfganz, www.kripfganz.de / xtdpdbc implements the
bias-corrected estimator of Breitung, Kripfganz, / and Hayakawa (2021) for
linear dynamic panel data models with fixed or / random effects.
xtseqreg from http://www.kripfganz.de/stata
'XTSEQREG': Sequential linear panel data estimation / Sebastian Kripfganz,
www.kripfganz.de / xtseqreg implements sequential estimators for linear
panel data models / with the analytical second-stage standard error
correction of Kripfganz / and Schwarz (2019). The command can be used to
spost13_ado from https://spost.su.domains
Distribution-date: 19Jul2020 / spost13_ado | SPost13 commands from Long
and Freese (2014) / Regression Models for Categorical Outcomes using
Stata, 3rd Edition. / Support www.indiana.edu/~jslsoc/spost.htm / Scott
Long (jslong@indiana.edu) & Jeremy Freese (jfreese@stanford.edu)
spost13_do from https://spost.su.domains
Distribution-date: 05Aug2014 / spost13_do | SPost13 examples from Long and
Freese, 2014, / Regression Models for Categorical Outcomes using Stata,
3rd Edition. / Support www.indiana.edu/~jslsoc/spost.htm / Scott Long
(jslong@indiana.edu) & Jeremy Freese (jfreese@stanford.edu)
margeff8 from http://web.uni-corvinus.hu/bartus/stata
margeff8. Average marginal effects for categorial regression models /
This version: 10 February 2006 (3rd update for Stata Journal submission) /
Author: Tamas Bartus (Corvinus University, Budapest)
sgmediation2 from https://tdmize.github.io/data
sgmediation2 - Sobel-Goodman tests of mediation for linear regression
models / Trenton D. Mize, Purdue University / Distribution-Date: 20240714
scoretest_cox from http://www.stata.com/users/icanette
{cmd:scoretest_cox}: Score test after {cmd:stcox} (requires Stata version
10) / {cmd:scoretest_cox} performs a score test on the significance of the
/ coefficients from a Cox regression fitted by using {cmd:stcox} / Program
by Isabel Canette, StataCorp LP. / {inp:icanette@stata.com} / 10 October
logitem from http://www.stata.com/users/mcleves
logitem. Logitistic regression when outcome is measured with uncertainty /
Program by Mario A. Cleves, Stata Corporation <mcleves@stata.com>. /
Statalist distribution, 31 August 1999. / logitem uses an EM algorithm
to estimates a maximum-likelihood logit / regression model when the
weibhet from http://www.stata.com/users/mcleves
weibhet. Weibull regression with gamma heterogeneity / Program by Mario
A. Cleves, Stata Corporation <mcleves@stata.com>. / weibhet performs
Weibull regression with gamma heterogeneity. / THIS PROGRAM HAS NOT BEEN
FULLY TESTED. / Use / . streg1, dist(weibull) hetero
treatreg from http://www.stata.com/users/rcong
treatreg. Treatment regression / Program by Ronna Cong, stata Corporation
<rcong@stata.com> / / treatreg estimates treatment effects models using
either Heckman's / two-step consistent estimator or full
maximum-likelihood.
truncreg from http://www.stata.com/users/rcong
truncreg. Truncated regression (updates to STB-52 sg122) / Program by
Ronna Cong, stata Corporation <rcong@stata.com> / STB 52 distribution,
November 1999. / / truncreg is used to estimate a regression model for
data from / a truncated normal distribution. See STB-52 sg122 for complete
boxcox2 from http://www.stata.com/users/ddrukker
boxcox2. Obtains MLE estimates of four Box-Cox Regression Models. /
Program by David Drukker, StataCorp <ddrukker@stata.com>. / Updated
statalist distribution, 20 March 2000. / This program obtains MLE
estimates of the coefficients, the / parameter(s) of the Box-Cox transform
stcstat from http://www.stata.com/users/wgould
stcstat. ROC curves after Cox regression / Program by William Gould,
Stata Corp <wgould@stata.com>. / Statalist distribution, 04 December 2001.
/ / {cmd:stcstat} calculates the area under the ROC curve based on the /
last model estimated by {help:stcox}.
sg45v6 from http://www.stata.com/users/wgould
STB-28 sg45v6. Maximum likelihood ridge regression. / STB insert by
Robert L. Obenchain, Eli Lilly and Company. / Updated to work with Stata 6
by William Gould, StataCorp. / Note by William Gould: This probram was
originally published in STB-28. / A change in Stata 6 broke the program
intreg2 from http://www.stata.com/users/wguan
intreg2. Performs interval regression with heteroskedasticity option. /
Program by Weihua Guan, StataCorp <wguan@stata.com>. / This program is an
expansion of -intreg-. It adds one more option to / specify the
conditional variance. The model then will contain / multiplicative
dfbeta2 from http://www.stata.com/users/wguan
dfbeta2. Calculates the DFBETA after OLS regression. / Program by Weihua
Guan, StataCorp <wguan@stata.com>. / This program is an expansion of
-dfbeta-. It allows the calculation of / the scaled DFBETA for the
constant term.
gendist from http://www.stata.com/users/rgutierrez
gendist: Utilities for random number generation. / Utilities for
generating data for use with Stata's {cmd:nbreg} (negative / binomial
regression) and {cmd:poisson} estimation commands. / This package also
contains commands for generating data from the / three-parameter gamma and
gam from http://www.stata.com/users/jhardin
gam. Generalized Additive Models. / Program by Patrick Royston and Gareth
Ambler. / / Module to estimate generalized additive regression models. / /
NOTE: This program may only be run on DOS/Windows machines / as it
requires a separate executable. / See help gam.
gologit from http://www.stata.com/users/jhardin
gologit. Generalized ordered logistic regression. / Vincent Kang Fu. / /
Module to estimate generalized ordered logistic models. / The gologit
command estimates regression models for ordinal / dependent variables. The
actual values taken on by the dependent / variables are irrelevant except
lstand from http://www.stata.com/users/jhardin
lstand. Logistic regression: Standardized coef. and partial corr. /
Program by Joseph Hilbe, Arizona State Univ. <jhilbe@aol.com> / / This
program provides standardized coefficients and partial / correlations
after a logistic regression estimation. / See help lstand.
negbin from http://www.stata.com/users/jhardin
negbin. Log negative binomial regression. / Program by Joseph Hilbe,
Arizona State Univ. <jhilbe@aol.com> / / Module to estimate log-negative
binomial regression models. / negbin estimates maximum likelihood log
negative binomial / regression models using the IRLS method for
pigreg from http://www.stata.com/users/jhardin
pigreg. Poisson inverse gaussian regression / Program by James Hardin,
Univ. South Carolina, <jhardin@sc.edu> and / Joseph Hilbe, Arizona State
Univ. / / Module to estimate Poisson inverse gaussian regression. / / See
help pigreg.
trpois0 from http://www.stata.com/users/jhardin
trpois0. Zero-truncated Poisson regression. / Program by Joseph Hilbe,
Arizona State Univ. <jhilbe@aol.com> / / Module to estimate zero-truncated
Poisson regression models / trpois0 estimates maximum likelihood
zero-truncated Poisson / regression models using Stata's ml method for
trnbin0 from http://www.stata.com/users/jhardin
trnbin0. Zero-truncated negative binomial regression. / Program by Joseph
Hilbe, Arizona State Univ. <jhilbe@aol.com> / / Module to estimate
zero-truncated neg. binomial regression / models. trnbin0 estimates
maximum likelihood zero-truncated / neg. binomial regression models using
williams from http://www.stata.com/users/jhardin
williams. Logistic regression using Williams' procedure. / Program by
Joseph Hilbe, Arizona State Univ. <jhilbe@aol.com> / Statalist
distribution, 25 January 1999. / / The William's procedure iteratively
reduces the chi2-based / dispersion to approximately 1. / See help
zigp from http://www.stata.com/users/jhardin
zigp. Zero-inflated generalized Poisson regression / Program by James
Hardin, Univ. South Carolina, <jhardin@sc.edu> and / Joseph Hilbe, Arizona
State Univ. / / Module to estimate zero-inflated generalized Poisson
regression models. / / See help zigp.
ulogit from http://www.stata.com/users/jhilbe
ulogit. Univariate LL tests for model identification / Program by Joseph
Hilbe, Arizona State Univ. <jhilbe@aol.com> / Statalist distribution, 27
January 1999. / Comparing the log-likelihood of a logistic regression
model containing only / the intercept with that of a model having a single
williams from http://www.stata.com/users/jhilbe
williams. Logistic regression using Williams' procedure. / Program by
Joseph Hilbe, Arizona State Univ. <jhilbe@aol.com> / Statalist
distribution, 25 January 1999. / The William's procedure iteratively
reduces the chi2-based / dispersion to approximately 1. / See help
deming from http://www.stata.com/users/ymarchenko
deming: Deming regression / This command performs Deming regression to
analyze method-comparison data / when the two methods are measured with
error. This approach results in / the best line minimizing the sum of
squares of the perpendicular distances. / Program by Yulia Marchenko,
mibeta from http://www.stata.com/users/ymarchenko
mibeta: Standardized coefficients for multiply-imputed data / This command
reports standardized coefficients and R-squared measures / for
multiply-imputed data analyzed by using linear regression. / Keywords:
multiple imputation, mi estimate, beta weights / Program by Yulia
drcurve from http://www.stata.com/users/jpitblado
drcurve: Plotting dose-response curves / Jeff Pitblado, StataCorp
<jpitblado@stata.com> / / drcurve plots dose-response curves using
logistic and probit regression. / Distribution-Date: 15may2007
myrereg from http://www.stata.com/users/jpitblado
myrereg: Random effects regression using the -gf2- evaluator / Jeff
Pitblado, StataCorp <jpitblado@stata.com> / / myrereg implements the
random effects regression model as described in the / StataPress book
about -ml-. In this implementation, I use a -gf2- Mata / function
r2_a from http://www.stata.com/users/jpitblado
r2_a: Adjusted R-square after regress / Jeff Pitblado, StataCorp
<jpitblado@stata.com>. / / r2_a computes adjusted R-square after
regress. This was written for / Stata 6, and is obsolete in Stata 7
since regress saves e(r2_a).
reg_ss from http://www.stata.com/users/jpitblado
reg_ss: Sum of Squares Tables in Linear Regression. / Jeff Pitblado,
StataCorp <jpitblado@stata.com> / / reg_ss generates Sequential and
Partial Sum of Squares Tables for Linear / Regression.
sim_arma from http://www.stata.com/users/jpitblado
sim_arma Simulate autoregressive moving average data (version 8) / Jeff
Pitblado, StataCorp <jpitblado@stata.com> / / sim_arma is a random
number generator for the autoregressive moving / average model. /
sim_arma was originally developed using Stata 7, but has since been /
atkplot from http://staskolenikov.net/stata
atkplot -- plot to assess regression residual normality / Author: Stas
Kolenikov, skolenik@unc.edu / This package plots half-normal plot for
regression residuals / with simulated confidence bands as suggested by
A.Atkinson.
calibr from http://staskolenikov.net/stata
calibr -- Stata module for inverse regression / Author: Stas Kolenikov,
skolenik@email.unc.edu / callibr performs the inverse regression and
calibration in / bivariate regression context. I.e., given the value of
the / observed dependent variable, we reconstruct the plausible / values
fsreg from http://staskolenikov.net/stata
fsreg -- Forward search regression / / Author: Stas Kolenikov,
skolenik@unc.edu / This package performs the forward search for the
outlier-free / subset of the data (Riani, Atkinson, 2000). The diagnostic
/ graphs produced by it show the effect of adding observations / on some
aboutreg from http://staskolenikov.net/stata
aboutreg -- Regression diagnostics tutorial / Author: Stas Kolenikov,
skolenik@recep.glasnet.ru / This tutorial was developed for the seminars
on applied / econometrics that the author was giving in Spring 2000 / at a
couple of Russian provincial universities in the / framework of the New
annfit from http://staskolenikov.net/stata
annfit -- Approximation by neural networks / Author: Stas Kolenikov,
skolenik@recep.glasnet.ru / This module performs a version of nonlinear
regression / involving a linear part and a neural network part. / Only
random search for the best approximating neural / network is implemented
regdplot from http://staskolenikov.net/stata
regdplot -- Regression diagnostics on one graph / Author: Stas Kolenikov,
skolenik@unc.edu / / This program displays the main regression
diagnostics / plots on one graph.
allsets from http://www.graunt.cat/stata
allsets. All Possible Subsets: linear, logistic, Cox & competing-risks
regression. / After installation, see help allsets. / (c)JM. Domenech, JB.
Navarro / Programmer: R. Sesma / Laboratori d'Estadistica Aplicada,
Universitat Autonoma de Barcelona. / Distribution-Date: 26jun2025 /
confound from http://www.graunt.cat/stata
confound. Modelling confounding in Linear, Logistic and Cox Regression. /
After installation, see help confound. / (c)JM. Domenech, JB. Navarro /
Programmer: R. Sesma / Laboratori d'Estadistica Aplicada, Universitat
Autonoma de Barcelona. / Distribution-Date: 09dec2024 / Version 1.2.1
a2reg from http://fmwww.bc.edu/RePEc/bocode/a
'A2REG': module to estimate models with two fixed effects / a2reg
estimates linear regressions with two way fixed effects, as / in Abowd and
Kramarz (1999). Fixed effects should not be / nested, but connected as
described in Abowd, Creecy, Kramarz / (2002). The deletion of missing
aaniv from http://fmwww.bc.edu/RePEc/bocode/a
'AANIV': module to compute unbiased IV regression / The conventional
instrumental variable (IV) or two-stage least / squares (2SLS) estimator
may be badly biased in overidentified / models with weak instruments.
While the 2SLS estimator performs / better in the exactly identified case,
aaplot from http://fmwww.bc.edu/RePEc/bocode/a
'AAPLOT': module for scatter plot with linear and/or quadratic fit,
automatically annotated / aaplot graphs a scatter plot for yvar versus
xvar with linear / and/or quadratic fit superimposed. The equation(s) and
R-square / statistics of the fits shown are also shown at the top of the /
aardl from http://fmwww.bc.edu/RePEc/bocode/a
'AARDL': module to perform Augmented ARDL Cointegration Analysis / aardl
implements the Augmented Autoregressive Distributed Lag / (A-ARDL)
cointegration test using the 3-test framework of Sam, / McNown & Goh
(2019). The package provides **eight model types** / that combine three
abar from http://fmwww.bc.edu/RePEc/bocode/a
'ABAR': module to perform Arellano-Bond test for autocorrelation / abar
performs the Arellano-Bond (1991) test for / autocorrelation. The test was
originally proposed for a / particular linear Generalized Method of
Moments dynamic panel / data estimator, but is quite general in its
acreg from http://fmwww.bc.edu/RePEc/bocode/a
'ACREG': module to perform Arbitrary Correlation Regression / acreg allows
users to obtain coefficient standard errors / allowing for an arbitrarily
flexible degree of correlation / structure; acreg stands for “arbitrary
correlation / regression”. Specifically, when estimating a regression in
adftest from http://fmwww.bc.edu/RePEc/bocode/a
'ADFTEST': module to perform ADF and Breusch-Godfrey tests / adftest
performs Dickey-Fuller unit root test and displays / the results along
with the Breusch-Godfrey autocorrelation / test results. The null
hypothesis of the Dickey-Fuller test / is that the variable is
adjksm from http://fmwww.bc.edu/RePEc/bocode/a
'ADJKSM': module to perform adjusted "ksm" for robust scatterplot
smoothing / adjksm calculates locally weighted regression scatterplot /
smoothing with an uniform band width along the x interval by a /
modification of the original ksm Stata ado-file. The robust / iterative
adjmean from http://fmwww.bc.edu/RePEc/bocode/a
'ADJMEAN': module to calculate variables' means adjusted for covariates /
adjmean calculates and optionally graphs adjusted means and / confidence
intervals from linear regression estimates for one or / two nominal X
variables, adjusted for covariates. If a second X / is specified, means
adjprop from http://fmwww.bc.edu/RePEc/bocode/a
'ADJPROP': module to calculate adjusted probabilities from logistic
regression estimates / adjprop calculates and optionally graphs adjusted
probabilities / (risks) and confidence intervals from logistic regression
/ estimates for one or two nominal X variables, adjusted for / covariates.
adjust from http://fmwww.bc.edu/RePEc/bocode/a
'ADJUST': module (corrected) to compute adjusted predictions and
probabilities after estimation / After an estimation command adjust
provides adjusted predictions / of xbeta (the means in a linear-regression
setting) or / probabilities (available after certain estimation commands).
aextlogit from http://fmwww.bc.edu/RePEc/bocode/a
'AEXTLOGIT': module to compute average elasticities for fixed effects
logit / aextlogit is a wrapper for xtlogit which estimates the fixed /
effects logit and reports estimates of the average (semi-) / elasticities
of Pr(y=1|x,u) with respect to the regressors, and / the corresponding
aic_model_selection from http://fmwww.bc.edu/RePEc/bocode/a
'AIC_MODEL_SELECTION': module to perform Forward Model Selection using AIC
or BIC / aic_model_selection performs a regression on a sequence of /
models adding one x-variable (in the order specified) at a / time, as
defined by a forward stepwise regression. / KW: model selection / KW: AIC
aivreg from http://fmwww.bc.edu/RePEc/bocode/a
'AIVREG': module to perform Anti-IV regression / aivreg implements the
anti-IV estimator outlined in Bell, / Billings, Calder-Wang, & Zhong
(2024). The method allows the / user to estimate implicit amenity prices
in the presence of an / unobservable confounder. / KW: regression / KW:
albatross from http://fmwww.bc.edu/RePEc/bocode/a
'ALBATROSS': module to create albatross plots / The albatross command
creates an albatross plot from studies / with number of participants, P
values and effect directions. The / plot shows a summary of studies when
meta-analysis is not / possible, with effect contours derived from the
allsynth from http://fmwww.bc.edu/RePEc/bocode/a
'ALLSYNTH': module to automate estimation of (i) bias-corrected synthetic
control gaps ("treatment effects") / allsynth is a wrapper for the synth
command which automates / the implementation of several additional
features. The primary / extensions are: (1) automated estimation of
almon from http://fmwww.bc.edu/RePEc/bocode/a
'ALMON': Module to Estimate Shirley Almon Generalized Polynomial
Distributed Lag Model / almon estimates Shirley Almon Polynomial
Distributed Lag Model / for many variables with different lag order,
endpoint / restrictions, and polynomial degree order via (ALS - ARCH - /
almon1 from http://fmwww.bc.edu/RePEc/bocode/a
'ALMON1': module to estimate Shirley Almon Polynomial Distributed Lag
Model / almon1 estimates Shirley Almon Polynomial Distributed Lag Model /
for many variables with the same lag order, endpoint / restrictions, and
polynomial degree order via (OLS - ALS - GLS - / ARCH) Regression models.
alogit from http://fmwww.bc.edu/RePEc/bocode/a
'ALOGIT': module to estimate (In)attentive logit regression from Abaluck
and Adams / alogit estimates the attentive logit models discussed in /
Abaluck and Adams (2017). An inattentive consumer chooses a good / as a
function of that good's characteristics and the probability / of paying
alsmle from http://fmwww.bc.edu/RePEc/bocode/a
'ALSMLE': module to perform Beach-Mackinnon AR(1) Autoregressive Maximum
Likelihood Estimation / alsmle performs Beach-Mackinnon First Order AR(1)
Autoregressive / Maximum Likelihood Estimation / KW: Regression / KW:
Maximum Likelihood Estimation / KW: Autoregressive / KW: Beach-Mackinnon /
anketest from http://fmwww.bc.edu/RePEc/bocode/a
'ANKETEST': module to perform diagnostic tests for spatial autocorrelation
in the residuals of OLS, SAR, IV, and IV-SAR models / anketest calculates
Moran's I and Lagrange Multiplier test / statistics and p-values to test
for spatial autocorrelation in / the residuals of Ordinary Least Squares
apc from http://fmwww.bc.edu/RePEc/bocode/a
'APC': module for estimating age-period-cohort effects / apc is a Stata
package for estimating age-period-cohort models. / apc_cglim estimates
generalized linear models in which a single / equality constraint on the
coefficients is used to solve the / age-period-cohort identification
appmlhdfe from http://fmwww.bc.edu/RePEc/bocode/a
'APPMLHDFE': module to estimate asymmetric Poisson regression with high
dimensional fixed effects / appmlhdfe estimates conditional expectiles
using Efron's (1992) / asymmetric Poisson maximum likelihood estimator;
the expectiles / are assumed to be an exponential function of a linear
archlm from http://fmwww.bc.edu/RePEc/bocode/a
'ARCHLM': module to calculate LM test for ARCH effects / archlm computes
Engle's LM test for ARCH (autoregressive / conditional heteroskedasticity)
effects in a regression residual / series for a specified number of lags
p. A list of lag orders / may be given; if none are given, one lag is
ardl from http://fmwww.bc.edu/RePEc/bocode/a
'ARDL': module to perform autoregressive distributed lag model estimation
/ ardl fits a linear regression model with lags of the dependent /
variable and the independent variables as additional regressors. /
Information criteria are used to find the optimal lag lengths if / those
arhomme from http://fmwww.bc.edu/RePEc/bocode/a
'ARHOMME': module to estimate Arellano and Bonhomme quantile selection
model / arhomme fits a conditional quantile regression in the / presence
of sample selection using the method of Arellano and / Bonhomme
(Econometrica, 2017). Standard errors are computed by / bootstrap or
armadiag from http://fmwww.bc.edu/RePEc/bocode/a
'ARMADIAG': module to compute post-estimation residual diagnostics for
time series / armadiag is a post-estimation diagnostic tool for use after
arch, / arima or regress. The residuals (standardized residuals with /
arch) are plotted together with autocorrelations, partial /
arrowplot from http://fmwww.bc.edu/RePEc/bocode/a
'ARROWPLOT': module to produce combined plot for graphing inter-group and
intra-group trends / arrowplot creates graphs showing inter-group and
intra-group / variation by overlaying arrows for intra-group (regression)
/ trends on an inter-group scatter plot. The graphical output is /
asdoc from http://fmwww.bc.edu/RePEc/bocode/a
'ASDOC': module to create high-quality tables in MS Word from Stata output
/ asdoc sends Stata output to Word / RTF format. asdoc creates /
high-quality, publication-ready tables from various Stata / commands such
as summarize, correlate, pwcorr, tab1, tab2, / tabulate1, tabulate2,
asreg from http://fmwww.bc.edu/RePEc/bocode/a
'ASREG': module to estimate rolling window regressions, Fama-MacBeth and
by(group) regressions / asreg can fit three types of regression models;
(1) a model of / depvar on indepvars using linear regression in a user's
defined / rolling window or recursive window (2) cross-sectional /
atkplot from http://fmwww.bc.edu/RePEc/bocode/a
'ATKPLOT': module to generate Atkinson residual normality plots / atkplot
graphs the half-normal plots with the confidence bands / for regression
residuals as suggested by Atkinson (1985) and / described in Smith and
Young (2001). This is a graphical tool to / assess the normality of the
attregtest from http://fmwww.bc.edu/RePEc/bocode/a
'ATTREGTEST': module to implement the regression-based attrition tests
proposed in Ghanem et al. (2024) / attregtest implements the two
regression-based attrition tests / proposed in Ghanem et al. (J. Hum.Res.
2024). / The first test is based on the testable implication of the
avciplot from http://fmwww.bc.edu/RePEc/bocode/a
'AVCIPLOT': module to produce added-variable plot with confidence
intervals / avciplot creates an added-variable plot (a.k.a. /
partial-regression leverage plot, partial regression plot, or / adjusted
partial residual plot) after regress. It differs from / avplot by adding
avg_effect from http://fmwww.bc.edu/RePEc/bocode/a
'AVG_EFFECT': module to calculate mean (standardized) effect size across
multiple outcomes / avg_effect follows Kling et al. (2004) and
Clingingsmith et al. / (Q J Econ, 2009) in calculating average
(standardized) effect / size using the seemingly-unrelated regression
avplot3 from http://fmwww.bc.edu/RePEc/bocode/a
'AVPLOT3': module to generate partial regression plots for subsamples /
avplot3 generates "partial regression plots" from an analysis of /
covariance model, where a category variable has been included in /
dummy-variable form among the regressors along with a constant / term
avplots4 from http://fmwww.bc.edu/RePEc/bocode/a
'AVPLOTS4': module to graph added-variable plots for specified regressors
in a single image / avplots4 is a variant on official Stata's avplots. It
allows a / list of variables to be specified. / KW: added-variable plot /
KW: graph / KW: regression / Requires: Stata version 6.0 /
b1x2 from http://fmwww.bc.edu/RePEc/bocode/b
'B1X2': module to account for changes when X2 is added to a base model
with X1 / b1x2 runs a "base" regression, runs a "full" regression with an
/ additional regressor, and computes both the difference in the /
coefficent estimates for x1all (including the constant) and a / consistent
batplot from http://fmwww.bc.edu/RePEc/bocode/b
'BATPLOT': module to produce Bland-Altman plots accounting for trend / The
normal Bland-Altman plot is between the difference of paired / variables
versus their average. This version uses a regression / between the
difference and the average and then alters the / limits of agreement
bayeshmc from http://fmwww.bc.edu/RePEc/bocode/b
'BAYESHMC': module to fit Bayesian regression models in Stata using the
No-U-Turn Sampler (NUTS) via CmdStan / bayeshmc fits Bayesian regression
models in Stata using the / No-U-Turn Sampler (NUTS) via CmdStan. It
supports 45 model / families — single-level (regress, logit, probit,
bcoeff from http://fmwww.bc.edu/RePEc/bocode/b
'BCOEFF': module to save regression coefficients to new variable / bcoeff
saves in a new variable regression coefficients (more / generally, the b
coefficient from a regression-like model) for / each of several groups of
observations. (bcoeff supersedes / deltaco by Zhiqiang Wang.) Note added
bcoeffs from http://fmwww.bc.edu/RePEc/bocode/b
'BCOEFFS': module to save regression coefficients to new variable / This
routine extends -bcoeff- of Wang and Cox to save all / estimated
coefficients to a new variable. Its function is similar / to that of
-statsby-. / KW: regression / KW: coefficients / Requires: Stata version
bctobit from http://fmwww.bc.edu/RePEc/bocode/b
'BCTOBIT': module to produce a test of the tobit specification / bctobit
computes the LM-statistic for testing the tobit / specification, against
the alternative of a model that is / non-linear in the regressors and
contains an error term that can / be heteroskedastic and non-normally
bdiff from http://fmwww.bc.edu/RePEc/bocode/b
'BDIFF': module to compute Bootstrap and Permutation tests for difference
in coefficients between two groups / bdiff perform several tests (Fisher's
Permutation test; / Seemingly Unrelated Regression test, see suest) to
determine / the significance of observed differences in coefficient
bdiffv from http://fmwww.bc.edu/RePEc/bocode/b
'BDIFFV': module to extend bdiff, visualizing differences (Bootstrap and
Permutaion tests) between two groups / Yujun, Lian (arlionn) released a
package called bdiff on / November 24, 2020, which can perform
between-group difference / analysis. However, existing research can no
betacoef from http://fmwww.bc.edu/RePEc/bocode/b
'BETACOEF': module to calculate beta coefficients from regression /
betacoef computes the beta coefficients from the previous / regression
(estimated via regress, ivreg, or ivreg2) and returns / them in r(beta).
The regression may use weights (except pw). As / only the original
bgtest from http://fmwww.bc.edu/RePEc/bocode/b
'BGTEST': module to calculate Breusch-Godfrey test for serial correlation
/ bgtest computes the Breusch (1978)-Godfrey (1978) Lagrange / multiplier
test for nonindependence in the error distribution. / For a specified
number of lags p, the test's null of independent / errors has alternatives
biastest from http://fmwww.bc.edu/RePEc/bocode/b
'BIASTEST': module to test parameter equality across different models /
The biastest command in Stata is a powerful and user-friendly / tool
designed to compare the coefficients of different regression / models,
enabling researchers to assess the robustness and / consistency of their
bicdrop1 from http://fmwww.bc.edu/RePEc/bocode/b
'BICDROP1': module to estimate the probability a model is more likely
without each explanatory variable / bicdrop1 is a post-estimation command
that uses the Bayesian / Information Criterion (BIC) to estimate the
probability that the / model would be more likely after dropping one of
binscatter from http://fmwww.bc.edu/RePEc/bocode/b
'BINSCATTER': module to generate binned scatterplots / binscatter
generates binned scatterplots, and is optimized for / speed in large
datasets. Binned scatterplots provide a / non-parametric way of
visualizing the relationship between two / variables. With a large number
binscatterhist from http://fmwww.bc.edu/RePEc/bocode/b
'BINSCATTERHIST': module to produce binned scatterplot with marginal
histograms / binscatterhist generates binned scatterplots, with the option
/ to plot the variables underlying distribution. Binned / scatterplots
provide a non-parametric way of visualizing the / relationship between two
bioprobit from http://fmwww.bc.edu/RePEc/bocode/b
'BIOPROBIT': module for bivariate ordered probit regression / bioprobit
fits maximum-likelihood two-equation ordered probit / models of ordinal
variables depvar1 and depvar2 on the / independent variables indepvars1
and indepvars2. The actual / values taken on by dependent variables are
bitobit from http://fmwww.bc.edu/RePEc/bocode/b
'BITOBIT': module to perform bivariate Tobit regression / bitobit fits a
two equation seemingly-unrelated model of the y1 / variable on the x1
variables and the y2 variable on the x2 / variables, where the censoring
status is determined by the / censor1 and censor2 variables. / KW: Tobit
bivpoisson from http://fmwww.bc.edu/RePEc/bocode/b
'BIVPOISSON': module to perform seemingly unrelated count regression /
bivpoisson implements the count-valued seemingly unrelated / regression
(count SUR) estimator proposed in Terza and Zhang / (2021). This paper
shows that bivpoisson affords greater / precision and accuracy than Linear
bivpoisson_ate from http://fmwww.bc.edu/RePEc/bocode/b
'BIVPOISSON_ATE': module to estimate Average Treatment Effects in
Seemingly Unrelated Count Regression / When we encounter correlated
count-valued outcomes y1 in / {0,1,...,M} and y2 in {0,1,...,M}, the
identification and / estimation of average treatment effects (ATEs) need
bking from http://fmwww.bc.edu/RePEc/bocode/b
'BKING': module to implement Baxter-King filter for timeseries data /
bking implements the band-pass filter proposed by Baxter and King / (Rev
Econ Stat, 1999) for the transformation of timeseries data / to preserve
business cycle frequencies. They demonstrate that it / has desirable
blandaltman from http://fmwww.bc.edu/RePEc/bocode/b
'BLANDALTMAN': module to create Bland-Altman plots featuring differences
or %differences or ratios, with options to add a variety of lines and
intervals / blandaltman produces Bland-Altman plots featuring (a) /
difference, (b) percentage difference or (c) ratio on the y-axis, / and
boost from http://fmwww.bc.edu/RePEc/bocode/b
'BOOST': module to perform boosted regression / boost implements the MART
boosting algorithm described in / Hastie et al. (2001). boost
accommodates Gaussian (normal), / logistic, Poisson and multinomial
regression. The algorithm is / implemented as a C++ plugin and requires
boottest from http://fmwww.bc.edu/RePEc/bocode/b
'BOOTTEST': module to provide fast execution of the wild bootstrap with
null imposed / boottest is a post-estimation command that offers fast
execution / of the wild bootstrap (Wu 1986) with null imposed, as
recommended / by Cameron, Gelbach, and Miller (2008) for estimates with /
boxtid from http://fmwww.bc.edu/RePEc/bocode/b
'BOXTID': module to fit Box-Tidwell and exponential regression models /
boxtid is a generalization of fracpoly in which continuous / rather than
fractional powers of the continuous covariates are / estimated. boxtid
fits Box & Tidwell's (Technometrics, 1962) / power transformation model to
bronch from http://fmwww.bc.edu/RePEc/bocode/b
'BRONCH': module to describe bronchiolitis severity / A new command has
been developed implementing a previously / validated tool for describing
bronchiolitis severity. / Bronchiolitis is one of the most common causes
of hospital / admission for infants and it is widely studied. This command
bspline from http://fmwww.bc.edu/RePEc/bocode/b
'BSPLINE': modules to compute B-splines parameterized by their values at
reference points / bspline and frencurv, each of which generates a basis
of splines / in an X-variable, for use in the varlist of a regression
command / (such as regress or glm) for fitting a spline in the X-variable.
bta2score from http://fmwww.bc.edu/RePEc/bocode/b
'BTA2SCORE': module to generate beta to score / A beta to score creates a
rounded-simplest scoring system for a / specific purpose as a rule of
thumb, calculation in the / mental arithmetic by the user in an emergency
condition or rapid / decision-making from post-estimation output derived
btobit from http://fmwww.bc.edu/RePEc/bocode/b
'BTOBIT': module to produce a test of the tobit specification / bctobit
computes the LM-statistic for testing the tobit / specification, against
the alternative of a model that is / non-linear in the regressors and
contains an error term that can / be heteroskedastic and non-normally
buckley from http://fmwww.bc.edu/RePEc/bocode/b
'BUCKLEY': module to implement Buckley-James method for analysing censored
data / buckley uses the Buckley-James method (Buckley and James 1979) to /
estimate the regression coefficients and generate the expected / value of
the censored outcome. depvar is the dependent variable / whose value is
byvar from http://fmwww.bc.edu/RePEc/bocode/b
'BYVAR': module to repeat a command by variable / byvar repeats stata_cmd
for each distinct combination of / values in varlist; varlist may contain
string variables. / Option e(elist) saves the e-class estimates e() named
in / elist which arise from stata_cmd. r(rlist) saves the R-class /
calibr from http://fmwww.bc.edu/RePEc/bocode/c
'CALIBR': module for inverse regression and calibration / calibr performs
the inverse regression and calibration in / bivariate regression context.
I.e., given the value of the / observed dependent variable, we reconstruct
the plausible values / of the regressor. Some three methods are used, so
cart from http://fmwww.bc.edu/RePEc/bocode/c
'CART': module to perform Classification And Regression Tree analysis /
This program performs a CART analysis for failure time data. It / uses the
martingale residuals of a Cox model to calculate / (approximate) chisquare
values for all possible cutpoints on all / the CART covariates. / KW:
catdev from http://fmwww.bc.edu/RePEc/bocode/c
'CATDEV': modules for interpretation of categorical dependent variable
models / There are several methods that can be used to effectively /
interpret the results of regression models for categorical / dependent
variables. Each of these methods requires the analyst / to complete post
ccv from http://fmwww.bc.edu/RePEc/bocode/c
'CCV': module to implement the causal cluster variance estimator / This
package implements the causal cluster variance (CCV) / estimator described
in Abadie et al., ("When Should You Adjust / Standard Errors for
Clustering?", QJE, 2023). The CCV estimator / allows for the calculation
ccweight from http://fmwww.bc.edu/RePEc/bocode/c
'CCWEIGHT': module to generate inverse sampling probability weights /
ccweight takes, as input, a varlist whose distinct values / correspond to
case groups, and a status variable (1 for cases, 0 / for controls) in the
option status. It creates, as output, a new / variable, suitable for use
cdfquantreg from http://fmwww.bc.edu/RePEc/bocode/c
'CDFQUANTREG': module for estimating generalized linear models for
doubly-bounded random variables with cdf-quantile distributions /
cfquantreg estimates generalized linear models with cdf-quantile /
distributions for doubly-bounded random variables. It assumes / that the
cdfquantreg01 from http://fmwww.bc.edu/RePEc/bocode/c
'CDFQUANTREG01': module for estimating generalized linear models for
doubly-bounded random variables with finite-tailed cdf-quantile
distributions / cdfquantreg01 estimates generalized linear models with /
finite-tailed cdf-quantile distributions for doubly-bounded / random
cdist from http://fmwww.bc.edu/RePEc/bocode/c
'CDIST': module for counterfactual distribution estimation and
decomposition of group differences / cdist estimates counterfactual
distributions using methods / suggested by Chernozhukov et al.
(Econometrica 81:2205–2268, / 2013). The unconditional (counterfactual)
cdreg from http://fmwww.bc.edu/RePEc/bocode/c
'CDREG': module to estimate Linear Regression under Measurement Error
using Auxiliary Information / cdreg consistently estimates linear
regression models including / a variable that is only observed with error
using parameter / estimates of the conditional density of the true value
cenpois from http://fmwww.bc.edu/RePEc/bocode/c
'CENPOIS': module to estimate censored maximum likelihood Poisson
regression models / cenpois estimates censored maximum likelihood Poisson
regression / models using Stata's ml method for estimation. Cases may be /
uncensored, left censored, or right censored. It includes the / cluster,
censornb from http://fmwww.bc.edu/RePEc/bocode/c
'CENSORNB': module to estimate censored negative binomial regression as
survival model / censornb fits a maximum likelihood censored negative
binomial / regression of depvar on indepvars, where depvar is a /
non-negative count variable. The censor option is required. If no /
cepois from http://fmwww.bc.edu/RePEc/bocode/c
'CEPOIS': module to estimate censored maximum likelihood Poisson
regression models / cepois estimates censored maximum likelihood Poisson
regression / models using Stata's ml method for estimation. Cases may be /
uncensored, left censored, or right censored. It includes the / cluster,
cfbinout from http://fmwww.bc.edu/RePEc/bocode/c
'CFBINOUT': module to perform Control Function Estimation of Binary
Outcome Models / cfbinout implements control function (two-stage residuals
/ inclusion) estimation of binary outcome models, specifically / logit,
probit, and cloglog, as suggested in Wooldridge (2015). / That is, in a
checkrob from http://fmwww.bc.edu/RePEc/bocode/c
'CHECKROB': module to perform robustness check of alternative
specifications / checkrob estimates a set of regressions where the
dependent / variable is regressed (with whatever method is specified in /
estimation command) on core variables - which are included in / all
chowreg from http://fmwww.bc.edu/RePEc/bocode/c
'CHOWREG': module to compute Structural Change Regressions and Chow Test /
chowreg Estimates Structural Change Regressions and Computes / Chow Test /
KW: regression / KW: OLS / KW: Structural Change / KW: Chow Test / KW:
Lagrange Multiplier Test / KW: Likelihood Ratio Test / KW: Wald Test /
chrdreg from http://fmwww.bc.edu/RePEc/bocode/c
'CHRDREG': module to estimate high-dimensional regressions based on
cluster-robust double/debiased machine learning / crhdreg estimates
high-dimensional regressions and / high-dimensional IV regressions with
one-way or two-way / cluster-robust standard errors based on Chiang, Kato,
cisd from http://fmwww.bc.edu/RePEc/bocode/c
'CISD': module to compute confidence intervals for standard deviations /
cisd performs estimation with CI of the residual standard / deviation
after regress or anova. cisd1 performs estimation with / CI of the
standard deviation based on a normal sample. / KW: confidence intervals /
cjive from http://fmwww.bc.edu/RePEc/bocode/c
'CJIVE': module to perform Cluster Jackknife Instrumental Variables
Estimation (CJIVE) / CJIVE estimates coefficients on potentially
endogenous / regressors via leave-one-cluster-out jackknife instrumental /
variables. This estimator is appropriate when there are many / instruments
clarify from http://fmwww.bc.edu/RePEc/bocode/c
'CLARIFY': module for Interpreting and Presenting Statistical Results /
Clarify is a program that uses Monte Carlo simulation to convert / the raw
output of statistical procedures into results that are of / direct
interest to researchers, without changing statistical / assumptions or
clus_nway from http://fmwww.bc.edu/RePEc/bocode/c
'CLUS_NWAY': module to perform Multi-way Clustering for Various Model
Specifications / clus_nway performs n-way clustering for
variance-covariance / matrix estimation for any model specification for
which Stata / allows 1 way clustering. This approach is based on Cameron,
cmogram from http://fmwww.bc.edu/RePEc/bocode/c
'CMOGRAM': module to plot histogram-style conditional mean or median
graphs / cmogram graphs the means, medians, frequencies, or proportions /
of one variable, conditional on another. Output can be further /
conditioned on a series of control variables, in which case it is / the
cmp from http://fmwww.bc.edu/RePEc/bocode/c
'CMP': module to implement conditional (recursive) mixed process estimator
/ cmp estimates multi-equation, mixed process models, potentially / with
hierarchical random effects. "Mixed process" means that / different
equations can have different kinds of dependent / variables. The choices
cnbreg from http://fmwww.bc.edu/RePEc/bocode/c
'CNBREG': module to estimate negative binomial regression - canonical
parameterization / cnbreg fits a maximum-likelihood negative binomial
regression / model, with canonical parameterization, of depvar on
indepvars, / where depvar is a non-negative count variable. cnbreg
cndnmb3 from http://fmwww.bc.edu/RePEc/bocode/c
'CNDNMB3': module to calculate condition number of regressor matrix /
cndnmb3 calculates the maximal condition number of a matrix of /
regressors. This statistic (the ratio of largest to smallest /
eigenvalue) is an unbounded measure of collinearity, or /
cnsrsig from http://fmwww.bc.edu/RePEc/bocode/c
'CNSRSIG': module to evaluate validity of restrictions on a regression /
Stata's cnsreg command facilitates the estimation of a linear / regression
subject to linear restrictions, or constraints in / Stata syntax, on its
coefficients. The restricted regression is / nested within its
coefout from http://fmwww.bc.edu/RePEc/bocode/c
'COEFOUT': module to keeps the beta-coefficients for specified variables
and labelnames from a regression. / Post regression, coefout saves the
beta coefficients for all / specified variables and, or labelnames(given
for any interaction / terms). The coefout has options to save the
coefplot from http://fmwww.bc.edu/RePEc/bocode/c
'COEFPLOT': module to plot regression coefficients and other results /
coefplot plots results from estimation commands or Stata / matrices.
Results from multiple models or matrices can be / combined in a single
graph. The default behavior of coefplot is / to draw markers for
coldiag from http://fmwww.bc.edu/RePEc/bocode/c
'COLDIAG': module to perform BWK regression collinearity diagnostics /
Coldiag is an implementation of the regression collinearity / diagnostic
procedures found in Belsley, Kuh, and Welsch (1980). / These procedures
examine the "conditioning" of the matrix of / independent variables.
coldiag2 from http://fmwww.bc.edu/RePEc/bocode/c
'COLDIAG2': module to evaluate collinearity in linear regression /
coldiag2 is an implementation of the regression collinearity / diagnostic
procedures found in Belsley, Kuh, and Welsch (1980). / These procedures
examine the "conditioning" of the matrix of / independent variables. This
compreg from http://fmwww.bc.edu/RePEc/bocode/c
'COMPREG': module to estimate a compositional regression with isometric
log-ratio (ILR) transformation of the components / compreg estimates a
compositional regression with isometric / log-ratio (ILR) transformation
of the components. / Compositional regression with isometric log-ratio
cooksd2 from http://fmwww.bc.edu/RePEc/bocode/c
'COOKSD2': module to compute Cook's distance after regress or xtreg /
cooksd2 generates Cook's (1977) distance measures after / regress or
xtreg, which summarize the effect of deleting an / observation, or an
entire subject, on the estimated regression / coefficients. The procedure
cpoisson from http://fmwww.bc.edu/RePEc/bocode/c
'CPOISSON': module to estimate censored Poisson regression / cpoisson fits
a censored Poisson maximum-likelihood regression / of depvar on indepvars,
where depvar is a non-negative count / variable. The censor option is
required. If no observations are / censored, a censor variable with all
cpoissone from http://fmwww.bc.edu/RePEc/bocode/c
'CPOISSONE': module to estimate censored Poisson regression (econometric
parameterization) / cpoissone fits a censored Poisson maximum-likelihood
regression / of depvar on indepvars, where depvar is a non-negative count
/ variable. The censor option is required. If no observations are /
cprplot2 from http://fmwww.bc.edu/RePEc/bocode/c
'CPRPLOT2': module to graph component-plus-residual plots for transformed
regressors / cprplot2 is a variation of official Stata's cprplot and is
used / for graphing component-plus-residual plots (a.k.a. partial /
residual plots). Additional features (compared to cprplot): (1) / cprplot2
cqiv from http://fmwww.bc.edu/RePEc/bocode/c
'CQIV': module to perform censored quantile instrumental variables
regression / cqiv conducts censored quantile instrumental variable (CQIV)
/ estimation. This command can implement both censored and / uncensored
quantile IV estimation either under exogeneity or / endogeneity. The
crash from http://fmwww.bc.edu/RePEc/bocode/c
'CRASH': module to calculate stock crash risk measures / crash calculates
two measures of stock crash risk - / Negative Coefficient of Skewness
(NCSKEW) and Down-to-Up / Volatility (DUVOL) - following the methodology
described in Chen, / Hong, and Stein (2001) and Kim, Li, and Zhang
crhdreg from http://fmwww.bc.edu/RePEc/bocode/c
'CRHDREG': module to estimate high-dimensional regressions based on
cluster-robust double/debiased machine learning / crhdreg estimates
high-dimensional regressions and / high-dimensional IV regressions with
one-way or two-way / cluster-robust standard errors based on Chiang, Kato,
crtest from http://fmwww.bc.edu/RePEc/bocode/c
'CRTEST': module to perform Cramer-Ridder Test for pooling states in a
Multinomial logit / crtest performs the Cramer-Ridder test for pooling
states in the / Multinomial logit model. This test assume a multinomial
logit / model with (S+1) states and two states that are candidates for /
crtrees from http://fmwww.bc.edu/RePEc/bocode/c
'CRTREES': module to compute Classification and Regression Trees
algorithms / crtrees performs Classification and Regression Trees (see /
Breiman et al. 1984). The procedure consists of three / algorithms:
tree-growing, tree-pruning, and finding the honest / tree. / KW:
cureregr from http://fmwww.bc.edu/RePEc/bocode/c
'CUREREGR': module to estimate parametric cure regression / cureregr fits
a parametric cure model in either the non-mixture / or mixture class.
cureregr requires that the data be stset prior / to use. The program works
with simple entry and exit in one / record per observation. It also works
cureregr8 from http://fmwww.bc.edu/RePEc/bocode/c
'CUREREGR8': module to estimate parametric cure regression (version 8.2) /
cureregr8 fits a parametric cure model in either the / non-mixture or
mixture class. cureregr8 requires that the data be / stset prior to use.
The program works with simple entry and exit / in one record per
curvefit from http://fmwww.bc.edu/RePEc/bocode/c
'CURVEFIT': module to produces curve estimation regression statistics and
related plots between two variables for alternative curve estimation
regression models / The Curve Estimation procedure produces curve
estimation / regression statistics and related plots between two variables
cusum6 from http://fmwww.bc.edu/RePEc/bocode/c
'CUSUM6': module to compute cusum, cusum^2 stability tests / cusum6 is an
updated version of Sean Becketti's cusum routine, / part of the Becketti
Time Series Library originally published in / STB-24, but not updated for
Stata 6.0 in the STB software / distribution. The routine calculates the
cusum9 from http://fmwww.bc.edu/RePEc/bocode/c
'CUSUM9': module to compute cusum, cusum^2 stability tests / cusum9 is an
updated version of Sean Becketti's cusum routine, / part of the Becketti
Time Series Library originally published in / STB-24. The routine
calculates the recursive residuals from a / time series regression in
cv from http://fmwww.bc.edu/RePEc/bocode/c
'CV': module to compute coefficient of variation after regress /
Coefficient of variation (CV) is the ratio of the standard / deviation of
residuals (Root MSE) to the sample mean of the / dependent variable
(Y-bar). The coefficient is then multiplied / by 100 to express it in
cv_kfold from http://fmwww.bc.edu/RePEc/bocode/c
'CV_KFOLD': module to implement k-fold cross-validation procedures /
cv_kfold is a post estimation command that implements a k-fold /
cross-validation procedure after regress, logit, probit, mlogit, /
poisson, and nbreg. The program allows selecting the / number of folds
cv_regress from http://fmwww.bc.edu/RePEc/bocode/c
'CV_REGRESS': module to estimate the leave-one-out error for linear
regression models / cv_regress uses the shortcut that relies on the
leverage / statistics to estimate the leave-one-out error, which is /
typically used in the estimation of Cross-Validation Statistics. / For
cvauroc from http://fmwww.bc.edu/RePEc/bocode/c
'CVAUROC': module to compute Cross-validated Area Under the Curve for ROC
Analysis after Predictive Modelling for Binary Outcomes / Receiver
operating characteristic (ROC) analysis is used for / comparing predictive
models, both in model selection and model / evaluation. This method is
cwmglm from http://fmwww.bc.edu/RePEc/bocode/c
'CWMGLM': module to estimate Cluster Weighted Models (CWM) / cwmglm is a
flexible package that allows to estimate Cluster / Weighted Models (finite
mixture of regression with random / covariates) using the EM algorithm. In
this program, are also / included parsimonious models of Gaussian
decomp from http://fmwww.bc.edu/RePEc/bocode/d
'DECOMP': module to compute decompositions of earnings gaps / decomp
computes Blinder-Oaxaca wage decompositions. It compares / the results
from two regressions, using intermediate commands / (himod and lomod), and
produces a table of output containing the / decompositions. These
decompose from http://fmwww.bc.edu/RePEc/bocode/d
'DECOMPOSE': module to compute decompositions of wage differentials /
Given the results from two regressions (one for each of two / groups),
decompose computes several decompositions of the outcome / variable
differential. The decompositions shows how much of the / gap is due to
descsave from http://fmwww.bc.edu/RePEc/bocode/d
'DESCSAVE': module to export data set and machine-readable codebook /
descsave is an extension of describe, creating up to 2 output / data sets.
These are a Stata data file with 1 observation per / variable and data on
the descriptive attributes of each variable / (name, storage type, format
dfao from http://fmwww.bc.edu/RePEc/bocode/d
'DFAO': module to perform Dickey-Fuller unit root test in the presence of
additive outliers / dfao is an extension of the dfuller routine in Stata.
It performs / the D-F unit root test when the data have additive outliers,
or / temporary one-time shocks. Such outliers give rise to moving /
dfgls from http://fmwww.bc.edu/RePEc/bocode/d
'DFGLS': module to compute Dickey-Fuller/GLS unit root test / dfgls
performs the Elliott-Rothenberg-Stock (ERS, 1996) efficient / test for an
autoregressive unit root. This test is similar to an / (augmented)
Dickey-Fuller "t" test, as performed by dfuller, but / has the best
dfsummary from http://fmwww.bc.edu/RePEc/bocode/d
'DFSUMMARY': module to compute the (Augmented) Dickey-Fuller unit-root
test and reports a summary table for different lags / dfsummary performs
the augmented Dickey-Fuller test that a / variable follows a unit-root
process. The null hypothesis is / that the variable contains a unit root,
diagma from http://fmwww.bc.edu/RePEc/bocode/d
'DIAGMA': module for the split component synthesis method of diagnostic
meta-analysis / diagma generates a summary ROC curve and uses the split /
component synthesis (SCS) method for diagnostic meta-analysis. / The SCS
method synthesises the diagnostic odds ratio (DOR) across / studies using
diagreg from http://fmwww.bc.edu/RePEc/bocode/d
'DIAGREG': module to compute Model Selection Diagnostic Criteria / diagreg
computes Model Selection Diagnostic Criteria / KW: regression / KW: OLS /
KW: Model Selection / Requires: Stata version 10.1 / Distribution-Date:
20140322 / Author: Emad Abd Elmessih Shehata, Agricultural Economics
diagreg2 from http://fmwww.bc.edu/RePEc/bocode/d
'DIAGREG2': module to compute 2SLS-IV ModeL Selection Diagnostic Criteria
/ diagreg2 computes 2SLS-IV ModeL Selection Diagnostic Criteria / KW:
regression / KW: IV / KW: 2SLS / KW: Model Selection / Requires: Stata
version 10 / Distribution-Date: 20140323 / Author: Emad Abd Elmessih
did2s from http://fmwww.bc.edu/RePEc/bocode/d
'DID2S': module to estimate a TWFE model using the two-stage
difference-in-differences approach / did2s implements Two-Stage
Difference-in-Differences by / Gardner (2021). A TWFE model for outcomes
is given by / unit/group fixed effects, time fixed effects, treatment
did_had from http://fmwww.bc.edu/RePEc/bocode/d
'DID_HAD': module to estimate the effect of a treatment on an outcome in a
heterogeneous adoption design with no stayers but some quasi stayers /
did_had estimates the effect of a treatment on an outcome in / a
heterogeneous adoption design (HAD) with no stayers but some / quasi
difd from http://fmwww.bc.edu/RePEc/bocode/d
'DIFD': module to evaluate test items for differential item functioning
(DIF) / DIF detection is a first step in assessing bias in test items. /
difd detects DIF in test items between groups, conditional on / the trait
that the test is measuring, using logistic / regression. The criteria for
dlagif from http://fmwww.bc.edu/RePEc/bocode/d
'DLAGIF': Module to Estimate Irving Fisher Arithmetic Distributed Lag
Model / dlagif estimates Irving Fisher Arithmetic Distributed Lag Model /
for many variables with different lag order, and polynomial / degree order
via (ALS - ARCH - Box-Cox - GLS - GMM - OLS - QREG - / Ridge) Regression
dlogit2 from http://fmwww.bc.edu/RePEc/bocode/d
'DLOGIT2': modules to compute marginal effects for logit, probit, and
mlogit / The commands dlogit2, dprobit2, and dmlogit2 compute marginal /
effects for, respectively, logistic regression, probit / regression, and
multinomial logistic regression. / Author: Bill Sribney, Stata
dltable from http://fmwww.bc.edu/RePEc/bocode/d
'DLTABLE': module to produce regression tables for Randomized Controlled
Trials Using Double LASSO / dltable creates regressions and tables (with
the subcommand / using) for experimental studies using double LASSO
estimation / (Belloni et al., 2014) It is the sister command of rctable. /
dmexogxt from http://fmwww.bc.edu/RePEc/bocode/d
'DMEXOGXT': module to test consistency of OLS vs XT-IV estimates /
dmexogxt computes a test of exogeneity for a panel regression / estimated
via instrumental variables, the null hypothesis for / which states that an
ordinary least squares (OLS) estimator of / the same equation would yield
domin from http://fmwww.bc.edu/RePEc/bocode/d
'DOMIN': module to conduct dominance analysis / domin conducts dominance
analysis (Budescu, 1993; Psychological / Bulletin) which computes general,
conditional statistics, / as well as complete dominance designations for a
user / supplied regression model. All dominance analysis / statistics are
drcate from http://fmwww.bc.edu/RePEc/bocode/d
'DRCATE': module to estimate and plot conditional average treatment effect
functions with uniform confidence bands using a doubly robust method /
drcate is a stata module to implement procedures to estimate / and plot
conditional average treatment effect functions with / uniform confidence
drdid from http://fmwww.bc.edu/RePEc/bocode/d
'DRDID': module for the estimation of Doubly Robust
Difference-in-Difference models / DRDID implements Sant'Anna and Zhao
(2020) proposed estimators / for the Average Treatment Effect on the
Treated (ATT) in / Difference-in-Differences (DID) setups where the
dstat from http://fmwww.bc.edu/RePEc/bocode/d
'DSTAT': module to compute summary statistics and distribution functions
including standard errors and optional covariate balancing / dstat unites
a variety of methods to describe (univariate) / statistical distributions.
Covered are density estimation, / histograms, cumulative distribution
durbinh from http://fmwww.bc.edu/RePEc/bocode/d
'DURBINH': module to calculate Durbin's h test for serial correlation / In
the presence of lagged dependent variables, the Durbin-Watson / statistic
and Box-Pierce Q statistics are not appropriate tests / for serial
correlation in the errors. Durbin's h statistic may / be used in this
dynamic_est from http://fmwww.bc.edu/RePEc/bocode/d
'DYNAMIC_EST': module to visualize dynamic effects in TWFE models / This
routine simplifies the visualization of dynamic effects / in two-way fixed
effects (TWFE) models. It is ideal for use in / event study or
Difference-in-Differences (DID) analyses. This / command allows users to
dynardl from http://fmwww.bc.edu/RePEc/bocode/d
'DYNARDL': module to dynamically simulate autoregressive distributed lag
(ARDL) models / dynardl is a program to produce dynamic simulations of /
autoregressive distributed lag (ARDL) models. See Philips /
(Am.J.Pol.Sci.,2018) for a discussion of this approach, / especially in
dynsimpie from http://fmwww.bc.edu/RePEc/bocode/d
'DYNSIMPIE': module to examine dynamic compositional dependent variables /
dynsimpie is a program to dynamically examine compositional / dependent
variables, first detailed in Philips, Rutherford, and / Whitten (2015a)
and used in Philips, Rutherford, and Whitten / (2015b). Their modeling
dynsimple from http://fmwww.bc.edu/RePEc/bocode/d
'DYNSIMPLE': module to examine dynamic compositional dependent variables /
dynsimpie is a program to dynamically examine compositional / dependent
variables, first detailed in Philips, Rutherford, and / Whitten (2015a)
and used in Philips, Rutherford, and Whitten / (2015b). Their modeling
eacf from http://fmwww.bc.edu/RePEc/bocode/e
'EACF': module to compute Extended Sample Autocorrelation Function / eacf
computes the Extended Sample Autocorrelation Function. This / approach was
put forward by Tsay, Ruey S. and George C. Tiao in / their paper 1984 JASA
"Consistent Estimates of Autoregressive / Parameters and Extended Sample
eba from http://fmwww.bc.edu/RePEc/bocode/e
'EBA': module to perform extreme bound analysis / eba Performs the Extreme
Bound Analysis on the regressor "var2". / For given a dependent variable
"var1", and a set of regressors / "var2", Z and X. The program runs
n!/(k!(n-k)!) OLS regressions / by taking combinations of k Z variables
ebalfit from http://fmwww.bc.edu/RePEc/bocode/e
'EBALFIT': module to perform entropy balancing / -ebalfit- is an
estimation command to perform entropy / balancing. Entropy balancing can
be expressed as a / regression-like model with one coefficient for each
balancing / constraint. -ebalfit- estimates such a model including the /
ebreg from http://fmwww.bc.edu/RePEc/bocode/e
'EBREG': module to compute Robust Empirical Bayes Confidence Intervals /
This Stata package implements robust empirical Bayes confidence /
intervals from Armstrong, Kolesár, and Plagborg-Møller (2021) / by
shrinking preliminary estimates toward a regression line. / KW: empirical
ecic from http://fmwww.bc.edu/RePEc/bocode/e
'ECIC': module to perform estimation and inference for changes in changes
at extreme quantiles / This command estimates quantile treatment effects
(QTE) at / extreme quantiles via changes in changes (CIC) based on Sasaki
/ and Wang (2022). The designed setting requires that all the / units are
effects from http://fmwww.bc.edu/RePEc/bocode/e
'EFFECTS': module to provide a graphical interface for estimation commands
/ effects provides a graphical user interface in which one may / specify
exposure, stratifying and confounding variables, and to / combine this
information with Stata estimation commands such as / regress, logistic,
elasticregress from http://fmwww.bc.edu/RePEc/bocode/e
'ELASTICREGRESS': module to perform elastic net regression, lasso
regression, ridge regression / elasticregress calculates an elastic
net-regularized / regression: an estimator of a linear model in which
larger / parameters are discouraged. This estimator nests the LASSO / and
emc from http://fmwww.bc.edu/RePEc/bocode/e
'EMC': module providing prefix command estimating contrasts for effect
modifier values / The prefix command emc takes a regression command as an
/ argument. From the regression command argument emc uses the / first
variable as an outcome variable, the second as a / dichotomous contrast
encoder from http://fmwww.bc.edu/RePEc/bocode/e
'ENCODER': module to encode strings into numerics with the option to
replace / encoder is identical to encode, but also includes options to (i)
/ replace an existing variable instead of generating a new / variable, and
(ii) set the first labeled value to start at 0, / rather than at 1. The
epitable from http://fmwww.bc.edu/RePEc/bocode/e
'EPITABLE': module to more easily create table 2 and table 3 for
epidemiological studies / epitable2 creates a composite table using
Stata’s collect / commands. The composite table contains regression
coefficients, / 95% confidence intervals, and trend p-values after running
epresent from http://fmwww.bc.edu/RePEc/bocode/e
'EPRESENT': module to present non-linear relationships in regression
models with log or logit-link / epresent reports exp(beta) for non-linear
associations between / a previously transformed or untransformed exposure
(specified in / transformedexposure) and an outcome (specified by depvar)
equation from http://fmwww.bc.edu/RePEc/bocode/e
'EQUATION': module to Output The Equation of a Regression / equation
displays the most recently estimated equation. / KW: estimation / KW:
regression / KW: display / Requires: Stata version 9 / Distribution-Date:
20201123 / Author: Liu Wei, The School of Sociology and Population
esetran from http://fmwww.bc.edu/RePEc/bocode/e
'ESETRAN': module to transform estimates and standard errors in parmest
resultssets / esetran is designed for use in parmest resultssets, which
have / one observation per estimated parameter and data on parameter /
estimates. It inputs 2 user-specified variables, containing the /
esizereg from http://fmwww.bc.edu/RePEc/bocode/e
'ESIZEREG': module for computing the effect size based on a linear
regression coefficient / esizereg is a postestimation command that
calculates Cohen's d / (Cohen 1988) and Hedges's g (Hedges 1981) effect
size for both / the unadjusted and adjusted mean difference of a
estout from http://fmwww.bc.edu/RePEc/bocode/e
'ESTOUT': module to make regression tables / estout produces a table of
regression results from one or / several models for use with spreadsheets,
LaTeX, HTML, or a / word-processor table. eststo stores a quick copy of
the active / estimation results for later tabulation. esttab is a wrapper
estout1 from http://fmwww.bc.edu/RePEc/bocode/e
'ESTOUT1': module to export estimation results from estimates table /
-estout1- is a wrapper for -estimates table- and produces a / table of
regression results for use with spreadsheets, TeX, / HTML, or a
word-processor table. In addition, -estout1- / overcomes some of the
estparm from http://fmwww.bc.edu/RePEc/bocode/e
'ESTPARM': module to save results from a parmest resultsset and test
equality / estparm is an inverse of parmest. It inputs 2 or 3 / variables
in the varlist, containing parameter estimates, / standard errors, and
(optionally) degrees of freedom. It / saves a set of estimation results
evalue_estat from http://fmwww.bc.edu/RePEc/bocode/e
'EVALUE_ESTAT': module for conducting postestimation sensitivity analyses
of unmeasured confounding in observational studies / evalue_estat is a
postestimation command that performs / sensitivity analyses for unmeasured
confounding in observational / studies using the methodology proposed by
eventbaseline from http://fmwww.bc.edu/RePEc/bocode/e
'EVENTBASELINE': module to correct event study after xthdidregress /
eventbaseline transforms the coefficients estimated by / xthdidregress
into a correct event study relative to a / baseline. The reported
coefficients are the average treatment / effects on the treated (ATT) for
eventcoefplot from http://fmwww.bc.edu/RePEc/bocode/e
'EVENTCOEFPLOT': module to produce advanced event-study graphical analysis
/ eventcoefplot runs regressions and generates graphs for / event-study
analysis, with extensive options for multiple / specifications comparison,
and specification and sample / robustness checks. In the context of
eventdd from http://fmwww.bc.edu/RePEc/bocode/e
'EVENTDD': module to panel event study models and generate event study
plots / eventdd estimates a panel event study corresponding to a /
difference-in-difference style model where a series of lag and / lead
coefficients and confidence intervals are estimated and / plotted. These
eventstudyinteract from http://fmwww.bc.edu/RePEc/bocode/e
'EVENTSTUDYINTERACT': module to implement the interaction weighted
estimator for an event study / To estimate the dynamic effects of an
absorbing treatment, / researchers often use two-way fixed effects (TWFE)
regressions / that include leads and lags of the treatment (event study /
eventstudyweights from http://fmwww.bc.edu/RePEc/bocode/e
'EVENTSTUDYWEIGHTS': module to estimate the implied weights on the
cohort-specific average treatment effects on the treated (CATTs) (event
study specifications) / eventstudyweights estimate weights underlying
two-way fixed / effects regressions with relative time indicators, It is /
ewreg from http://fmwww.bc.edu/RePEc/bocode/e
'EWREG': module to estimate errors-in-variable model with mismeasured
regressor / ewreg runs a Errors-In-Variables regression, with one /
mismeasured regressor and several perfectly measured regressors. / It uses
the High-Order-Moments method of Erickson & Whited (2000, / Journal of
exceloutput from http://fmwww.bc.edu/RePEc/bocode/e
'EXCELOUTPUT': module to output regression results directly to specific
cells in excel file / exceloutput is invoked after estimation. It places
regression / coefficients in the selected cell and standard error in the
cell / beneath along with stars for p-values along with other options. /
far5 from http://fmwww.bc.edu/RePEc/bocode/f
'FAR5': module to compute floating absolute risk for Cox and conditional
logit regression / far5 computes floating absolute risk for Cox and
conditional / logit regression. / Author: Abdel G. Babiker, University
College London Medical School / Support: email A.Babiker@ctu.mrc.ac.uk /
favplots from http://fmwww.bc.edu/RePEc/bocode/f
'FAVPLOTS': module for formatted added-variable plot(s) / favplot graphs
an added-variable plot (a.k.a. partial-regression / leverage plot, partial
regression plot, or adjusted partial / residual plot) after regress.
favplots graphs all the / added-variable plots in a single image. These
fbardl from http://fmwww.bc.edu/RePEc/bocode/f
'FBARDL': module to perform Fourier Bootstrap Autoregressive Distributed
Lag Model estimation / fbardl estimates a Fourier ARDL model and performs
/ cointegration testing. / KW: ARDL / KW: autoregressive / KW:
distributed lag / KW: Fourier / Requires: Stata version 17 /
fbnardl from http://fmwww.bc.edu/RePEc/bocode/f
'FBNARDL': module to perform Fourier Bootstrap Nonlinear Autoregressive
Distributed Lag estimation / fbnardl estimates a Fourier Nonlinear ARDL
model that combines / three methodological advances: (1) the NARDL
framework of Shin, / Yu & Greenwood-Nimmo (2014) for asymmetric
fencode from http://fmwww.bc.edu/RePEc/bocode/f
'FENCODE': module to provide frequency-based encoding of string and
numeric variables / fencode provides frequency-based encoding of string
and numeric / variables. Unlike encode which assigns codes alphabetically,
/ fencode assigns numeric codes ordered by frequency (descending or /
fese from http://fmwww.bc.edu/RePEc/bocode/f
'FESE': module to calculate standard errors for fixed effects / fese
implements a fixed-effects regression using areg and saves / the estimated
fixed effects and their standard errors as new / variables on the data.
Note that areg produces identical results / to {help xtreg} with the fe
fgt_ci from http://fmwww.bc.edu/RePEc/bocode/f
'FGT_CI': module to calculate and decompose Foster–Greer–Thorbecke
(and standard) concentration indices / This command combines two of the
most widely used measures in / the inequality and poverty literatures: the
concentration / index (CI) and the Foster–Greer–Thorbecke (FGT)
fgtest from http://fmwww.bc.edu/RePEc/bocode/f
'FGTEST': module to Compute Farrar-Glauber Multicollinearity Chi2, F, t
Tests / fgtest Computes Farrar-Glauber Multicollinearity Chi2, F, t /
Tests / KW: regression / KW: Multicollinearity / KW: Farrar-Glauber test /
Requires: Stata version 10 / Distribution-Date: 20120208 / Author: Emad
firthlogit from http://fmwww.bc.edu/RePEc/bocode/f
'FIRTHLOGIT': module to calculate bias reduction in logistic regression /
The module implements a penalized maximum likelihood estimation / method
proposed by David Firth (University of Warwick) for / reducing bias in
generalized linear models. In this module, the / method is applied to
fitint from http://fmwww.bc.edu/RePEc/bocode/f
'FITINT': module to fit generalized linear model and test two-way
interactions / The creation and testing of interaction terms in regression
/ models can be very cumbersome, even in Stata 8. We propose a / simple
wrapping command, -fitint-, that fits any generalised / linear model and
fitstat from http://fmwww.bc.edu/RePEc/bocode/f
'FITSTAT': module to compute fit statistics for single equation regression
models / fitstat is a post-estimation command that computes a variety of /
measures of fit for many kinds of regression models. It works / after the
following: clogit, cnreg, cloglog, intreg, logistic, / logit, mlogit,
fixedrho from http://fmwww.bc.edu/RePEc/bocode/f
'FIXEDRHO': module to estimate treatment and selection models with fixed
rho / fixedrho provides commands for estimating endogenous treatment / and
sample-selection models that enable fixing the value of the / correlation
between the unobservables. / KW: treatment / KW: sample selection / KW:
flexdid from http://fmwww.bc.edu/RePEc/bocode/f
'FLEXDID': module for flexible estimation of difference-in-differences
regression with staggered implementation / flexdid estimates average
treatment effects on the treated / (ATETs) in difference-in-differences
designs with staggered / implementation of treatment using a flexible
fmm from http://fmwww.bc.edu/RePEc/bocode/f
'FMM': module to estimate finite mixture models / fmm fits a finite
mixture regression model using maximum / likelihood estimation. The model
is a J-component finite mixture / of densities, with the density within a
class (j) allowed to / vary in location and scale. Optionally, the mixing
forest from http://fmwww.bc.edu/RePEc/bocode/f
'FOREST': module to visualize results from multiple regressions on a
single independent variable / forest visualizes results from multiple
regressions on a single / independent variable. The resulting "forest"
chart shows the / effect of a single treatment variable of interest on a
fqardl from http://fmwww.bc.edu/RePEc/bocode/f
'FQARDL': module to perform Fourier Quantile Autoregressive Distributed
Lag Model estimation / fqardl estimates a Fourier ARDL model and performs
/ cointegration testing. It extends the standard QARDL / framework by
incorporating Fourier trigonometric terms to capture / smooth structural
fqreg from http://fmwww.bc.edu/RePEc/bocode/f
'FQREG': module to estimate quantile regression for non-negative data with
a mass-point at zero and an upper bound / fqreg estimates quantile
regression for non-negative data with a / mass-point at zero and an upper
bound, using the specification / and method described in Machado, Santos
fracirf from http://fmwww.bc.edu/RePEc/bocode/f
'FRACIRF': module to compute impulse response function for
fractionally-integrated timeseries / fracirf computes the infinite moving
average representation (or / impulse response function) of a
fractionally-integrated / timeseries, given a value of the fractional
fractileplot from http://fmwww.bc.edu/RePEc/bocode/f
'FRACTILEPLOT': module for smoothing with respect to distribution function
predictors / fractileplot computes and graphs smooths of a response on all
/ of a set of predictors simultaneously; that is, each smooth is /
adjusted for the others. Each predictor is treated on the scale / of its
frm from http://fmwww.bc.edu/RePEc/bocode/f
'FRM': module to estimate and test fractional regression models / This
package includes six Stata modules for estimating and / testing fractional
regression models (Ramalho, Ramalho and / Murteira, 2011, Alternative
estimating and testing empirical / strategies for fractional regression
frontierhtail from http://fmwww.bc.edu/RePEc/bocode/f
'FRONTIERHTAIL': module to estimate stochastic production frontier models
for heavy tail data / frontierhtail implements stochastic production
frontier / regression for heavy tail data. As pointed out by Nguyen
(2010), / economic and financial data frequently evidence fat tails. /
fsreg from http://fmwww.bc.edu/RePEc/bocode/f
'FSREG': module for forward search regression / This package performs the
forward search for the outlier-free / subset of the data (Riani, Atkinson,
2000). The diagnostic graphs / produced by it show the effect of adding
observations on some / regression results and on the parameters of the
ftest from http://fmwww.bc.edu/RePEc/bocode/f
'FTEST': module comparing two nested models using an F-test / ftest
compares two nested models estimated using regress and / performs an
F-test for the null hypothesis that the constraint / implict in the
restricted model holds. For example if a variable / is left out of the
gb2reg from http://fmwww.bc.edu/RePEc/bocode/g
'GB2REG': module to perform Regression with a GB2 Error Term / gb2reg fits
a model of the log of depvar on indepvars using / maximum likelihood with
an error term distributed as a gb2. / The parameter delta varies with the
independent variables. The / other parameters can also vary with the
gdecomp from http://fmwww.bc.edu/RePEc/bocode/g
'GDECOMP': module to compute decomposition of outcome differentials after
nonlinear models / gdecomp implements a generalized Blinder-Oaxaca
decomposition / which applies to categorical and count outcomes (and
parallel to / this, to nonlinear regression models). First, the /
geninteract from http://fmwww.bc.edu/RePEc/bocode/g
'GENINTERACT': module to generate N-way interaction terms / This program
generates N-way interaction terms for a set of / variables. While this
program works for any numerical variable / list, it is particularly useful
for polynomials. It has been / shown that neural networks (NNs) are
genqreg from http://fmwww.bc.edu/RePEc/bocode/g
'GENQREG': module to perform Generalized Quantile Regression / genqreg can
be used to fit the generalized quantile regression / estimator developed
in Powell (2016). The generalized quantile / estimator addresses a
fundamental problem posed by traditional / quantile estimators: inclusion
genspec from http://fmwww.bc.edu/RePEc/bocode/g
'GENSPEC': module to implement a General-to-Specific modelling algorithm /
genspec is an algorithm for general-to-specific model prediction / in
Stata. It is designed to search a large number of explanatory /
variables, and from these explanatory variables select the 'best' / model
getregstats from http://fmwww.bc.edu/RePEc/bocode/g
'GETREGSTATS': module for computing all values in a regression table when
only the coefficient and one other statistic is available / getregstats
computes all the statistics reported in a regression / table when the user
specifies the coefficient and one other / statistic. This is useful in
gets from http://fmwww.bc.edu/RePEc/bocode/g
'GETS': module to implement a General-to-Specific modelling algorithm /
gets is an algorithm for general-to-specific model prediction in / Stata.
It is designed to search a large number of explanatory / variables, and
from these explanatory variables select the 'best' / model based upon
ggtax from http://fmwww.bc.edu/RePEc/bocode/g
'GGTAX': module to identify the most suitable GG family model / ggtax is a
postestimation command that creates a graph for an / easy interpretation
of the shape and scale parameters of a / parametric survival regression
with gamma distribution. When / ggtax is ran after streg varlist,
ggtaxonomy from http://fmwww.bc.edu/RePEc/bocode/g
'GGTAXONOMY': module to identify the most suitable GG family model /
ggtaxonomy is a postestimation command that creates a graph for / an easy
interpretation of the shape and scale parameters of / a parametric
survival regression with gamma distribution. / When ggtax is ran after
ghxt from http://fmwww.bc.edu/RePEc/bocode/g
'GHXT': module to compute Panel Groupwise Heteroscedasticity Tests / ghxt
computes Panel Groupwise Heteroscedasticity Tests / KW: panel / KW:
heteroskedasticity / KW: regression / KW: Lagrange Multiplier LM Test /
KW: Likelihood Ratio LR Test / KW: Wald Test / Requires: Stata version 10
ginireg from http://fmwww.bc.edu/RePEc/bocode/g
'GINIREG': module for Gini regression / The ginireg package supports the
estimation of Gini regressions. / The Gini regression has its origin in
Corrado Gini's (1912) / introduction of the Gini Mean Difference (GMD) as
an alternative / to the variance. The population GMD is defined as GMD = /
gintreg from http://fmwww.bc.edu/RePEc/bocode/g
'GINTREG': module to perform Generalized Interval Regression / gintreg
fits a model of depvar on indepvars using maximum / likelihood where the
dependent variable can be point data, / interval data, right-censored
data, or left-censored data. This / is a generalization of the built in
givgauss2 from http://fmwww.bc.edu/RePEc/bocode/g
'GIVGAUSS2': module to estimate generalized two-parameter inverse Gaussian
regression / givgauss2 fits a maximum-likelihood generalized 2-parameter /
log-inverse Gaussian regression model of depvar on indepvars, / where
depvar is a non-negative count variable. The program may be / used to
glgamma2 from http://fmwww.bc.edu/RePEc/bocode/g
'GLGAMMA2': module to estimate generalized two-parameter log-gamma
regression / glgamma2 fits a maximum-likelihood generalized 2-parameter /
log-gamma regression model of depvar on indepvars, where depvar / is a
non-negative count variable. The program may be used to / model
gllamm from http://fmwww.bc.edu/RePEc/bocode/g
'GLLAMM': program to fit generalised linear latent and mixed models /
gllamm fits generalized linear latent and mixed models. These / models
include Multilevel generalized linear regression models / (extensions of
the simple random intercept models that may be / fitted in Stata using
glst from http://fmwww.bc.edu/RePEc/bocode/g
'GLST': module for trend estimation of summarized dose-response data /
glst estimates trend across different exposure levels for either / single
or multiple summarized case-control, incidence-rate, and / cumulative
incidence data. This approach is based on / constructing an approximate
gmemultinomial from http://fmwww.bc.edu/RePEc/bocode/g
'GMEMULTINOMIAL': module to fit multinomial models using generalized
maximum entropy / gmemultinomial fits multinomial models using generalized
maximum / entropy. Given finite samples, gmemultinomial is more efficient
/ than its maximum entropy and maximum likelihood counterparts / because
gnbstrat from http://fmwww.bc.edu/RePEc/bocode/g
'GNBSTRAT': module to estimate Generalized Negative Binomial with
Endogenous Stratification / gnbstrat fits a maximum-likelihood generalized
negative binomial / with endogenous stratification regression model of
depvar on / indepvars, where depvar is a nonnegative count variable > 0.
gnpoisson from http://fmwww.bc.edu/RePEc/bocode/g
'GNPOISSON': module to estimate generalized Poisson regression / gnpoisson
fits a maximum-likelihood generalized Poisson / regression model of depvar
on indepvars, where depvar is a / non-negative count variable. / KW:
Poisson regression / KW: count data / KW: generalized Poisson / Requires:
gologit from http://fmwww.bc.edu/RePEc/bocode/g
'GOLOGIT': module to estimate generalized ordered logit models / The
gologit command estimates regression models for ordinal / dependent
variables. The actual values taken on by the dependent / variable are
irrelevant except that larger values are assumed to / correspond to
gologit2 from http://fmwww.bc.edu/RePEc/bocode/g
'GOLOGIT2': module to estimate generalized logistic regression models for
ordinal dependent variables / gologit2 estimates generalized ordered logit
models for ordinal / dependent variables. A major strength of gologit2 is
that it can / also estimate three special cases of the generalized model:
gologit29 from http://fmwww.bc.edu/RePEc/bocode/g
'GOLOGIT29': module to estimate generalized logistic regression models for
ordinal dependent variables / gologit29 estimates generalized ordered
logit models for ordinal / dependent variables. Users running Stata 11.2
or better should / use gologit2 (q.v.). / KW: logistic / KW: logistic
goprobit from http://fmwww.bc.edu/RePEc/bocode/g
'GOPROBIT': module to estimate generalized ordered probit models /
goprobit is a user-written procedure to estimate generalized / ordered
probit models in Stata. The actual values taken on by / the dependent
variable are irrelevant except that larger values / are assumed to
goprobit2 from http://fmwww.bc.edu/RePEc/bocode/g
'GOPROBIT2': module to perform generalized ordered probit regression /
goprobit2 is a community-contributed module which fits ordered / response
regression models of ordinal variable depvar on the / independent
variables indepvars. It extends oprobit by / offering a variety of link
gphudak from http://fmwww.bc.edu/RePEc/bocode/g
'GPHUDAK': module to estimate long memory in a timeseries / gphudak
computes the Geweke/Porter-Hudak (GPH, 1983) estimate of / the long memory
(fractional integration) parameter, d, of a / timeseries. The GPH method
uses nonparametric methods--a spectral / regression estimator-- to
gpreg from http://fmwww.bc.edu/RePEc/bocode/g
'GPREG': module to estimate regressions with two dimensional fixed effects
/ Estimation of regressions with two dimensions of fixed effects, / e.g.
worker and firm fixed effects, student and teacher, or / patient and
doctor fixed effects. This program uses the / Guimaraes & Portugal
grand2 from http://fmwww.bc.edu/RePEc/bocode/g
'GRAND2': module to compute an estimate of the grand mean/intercept and
differences / For use after fit to present a set of indicator/dummy
variables / in the form of a "grand mean" and differences from the "grand
/ mean". The specified list of variables (indicator_variable_list) / must
grcompare from http://fmwww.bc.edu/RePEc/bocode/g
'GRCOMPARE': module to make group comparisons in binary regression models
/ This is a Stata module to make group comparisons in binary / regression
models using alternative measures, including gradip: / average difference
in predicted probabilities over a range; / grdiame:difference in group
grlogit from http://fmwww.bc.edu/RePEc/bocode/g
'GRLOGIT': module to plot logit of a variable by categories of another
variable / grlogit plots the logit of one variable against categories of /
another variable. This may be of some use in the beginning of / logistic
regression modelling. You could use this program to / confirm visually
group2hdfe from http://fmwww.bc.edu/RePEc/bocode/g
'GROUP2HDFE': module to compute number of restrictions in a linear
regression model with two high-dimensional fixed effects / This command
calculates the number of restrictions needed to / ensure identifiability
of the fixed effects in a linear / regression model with two high
group3hdfe from http://fmwww.bc.edu/RePEc/bocode/g
'GROUP3HDFE': module to compute number of restrictions in a linear
regression model with three high-dimensional fixed effects / This command
calculates the number of restrictions needed to / ensure identifiability
of the fixed effects in a linear / regression model with three high
groupcl from http://fmwww.bc.edu/RePEc/bocode/g
'GROUPCL': module to estimate grouped conditional logit models / In many
applications of conditional logit models the choice set / and the
characteristics of that set are identical for groups / of decision makers.
In that case it is possible to obtain a more / computationally efficient
grqreg from http://fmwww.bc.edu/RePEc/bocode/g
'GRQREG': module to graph the coefficients of a quantile regression /
grqreg graphs the coefficients of a quantile regression. / KW: quantile
regression / KW: graphs / Requires: Stata version 8.2 / Author: Joao
Pedro Azevedo, University of Newcastle-upon-Tyne, UK / Support: email
grstest2 from http://fmwww.bc.edu/RePEc/bocode/g
'GRSTEST2': module to implement the Gibbons, Ross, Shanken (1989) test /
The module calculates the Gibbons, Ross, Shanken (1989) F-test / for the
joint null hypothesis that N estimated intercepts from N / time-series
regressions are equal to zero. The test is frequently / employed to assess
grxtqreg from http://fmwww.bc.edu/RePEc/bocode/g
'GRXTQREG': module to graph the coefficients of a Quantile Regression for
Panel Data / grxtqreg graphs the coefficients of the quantile regression
for / panel data. It also has the option to graph the confidence /
interval, the general FE (fixed effect) coefficient and / the FE
gs2sls from http://fmwww.bc.edu/RePEc/bocode/g
'GS2SLS': module to estimate Generalized Spatial Two Stage Least Squares
Cross Sections Regression / gs2sls estimates Generalized Spatial Two Stage
Least Squares / Cross Sections Regression / KW: spatial regression / KW:
two stage least squares / KW: 2SLS / KW: cross section / KW: regression /
gs2slsar from http://fmwww.bc.edu/RePEc/bocode/g
'GS2SLSAR': module to estimate Generalized Spatial Autoregressive Two
Stage Least Squares Regression / gs2sls estimates Generalized Spatial
Autoregressive Two Stage / Least Squares Regression / KW: spatial
regression / KW: two stage least squares / KW: 2SLS / KW: autoregression /
gs2slsarxt from http://fmwww.bc.edu/RePEc/bocode/g
'GS2SLSARXT': module to estimate Generalized Spatial Panel Autoregressive
Two Stage Least Squares Cross Sections Regression / gs2sls estimates
Generalized Spatial Panel Two Stage Least / Squares Regression / KW:
spatial regression / KW: two stage least squares / KW: 2SLS / KW: cross
gs2slsxt from http://fmwww.bc.edu/RePEc/bocode/g
'GS2SLSXT': module to estimate Generalized Spatial Panel Autoregressive
Two-Stage Least Squares Regression / gs2slsxt estimates Generalized
Spatial Panel Autoregressive / Two-Stage Least Squares Regression / KW:
spatial / KW: panel / KW: regression / KW: Between Effects / KW:
gs3sls from http://fmwww.bc.edu/RePEc/bocode/g
'GS3SLS': module to estimate Generalized Spatial Three Stage Least Squares
(3SLS) / gs3sls estimates Generalized Spatial Three Stage Least Squares /
(3SLS) / KW: spatial / KW: panel / KW: regression / KW: GS2SLS / KW:
GS3SLS / KW: Generalized Spatial 2SLS Model / KW: Generalized Spatial 3SLS
gs3slsar from http://fmwww.bc.edu/RePEc/bocode/g
'GS3SLSAR': module to estimate Generalized Spatial Autoregressive Three
Stage Least Squares (3SLS) Cross Sections Regression / gs3sls estimates
Generalized Spatial Autoregressive Three Stage / Least Squares (3SLS)
Cross Sections Regression and calculates / Spatial Autocorrelation, Non
gsreg from http://fmwww.bc.edu/RePEc/bocode/g
'GSREG': module to perform Global Search Regression / gsreg is an
automatic model selection command for time series, / cross-section and
panel data regressions. By default (otherwise, / users have many options
to modify this simplest / specification), gsreg performs alternative OLS
gtsheckman from http://fmwww.bc.edu/RePEc/bocode/g
'GTSHECKMAN': module to compute a generalized two-step Heckman selection
model / gtsheckman fits regression models with selection by using /
Heckman's two-step consistent estimator. It is similar to the / two step
consistent heckman estimator, but allows for / heteroskedasticity in the
gvselect from http://fmwww.bc.edu/RePEc/bocode/g
'GVSELECT': module to perform best subsets variable selection / gvselect
performs best subsets variable selection. The / Furnival-Wilson
(Technometrics, 1974) leaps-and-bounds algorithm / is applied using the
log likelihoods of candidate models, / allowing variable selection to be
gweakivtest from http://fmwww.bc.edu/RePEc/bocode/g
'GWEAKIVTEST': module providing robust test for weak instruments for 2SLS
with multiple endogenous regressors / gweakivtest implements the robust
weak instruments test of / Lewis and Mertens (2025). It is a
postestimation command for / ivreg2 and ivregress. gweakivtest tests the
haif from http://fmwww.bc.edu/RePEc/bocode/h
'HAIF': module to compute Homoskedastic Adjustment Inflation Factors for
model selection / haif calculates homoskedastic adjustment inflation
factors / (HAIFs) for core variables in the corevarlist, caused by /
adjustment by the additional variables specified by addvars(). / HAIFs are
hcnbreg from http://fmwww.bc.edu/RePEc/bocode/h
'HCNBREG': module to estimate Heterogeneous Canonical Negative Binomial
Regression / The canonical parameterization of the negative binomial
derives / directly from the exponential form of the negative binomial /
probability distribution function. Unlike the NB-2 and NB-1 /
hdfe from http://fmwww.bc.edu/RePEc/bocode/h
'HDFE': module to partial out variables with respect to a set of fixed
effects / hdfe will partial out a varlist with respect to a set of fixed /
effects. It will either overwrite the dataset in memory, or / generate new
variables. hdfe is the underlying procedure for the / reghdfe module,
hetsar from http://fmwww.bc.edu/RePEc/bocode/h
'HETSAR': module to estimate spatial autoregressive models with
heterogeneous coefficients / hetsar fits spatial autoregressive panel data
models with / heterogeneous coefficients. The estimation is performed via
quasi / maximum-likelihood. hetsar allows the automatic estimation of /
hettreatreg from http://fmwww.bc.edu/RePEc/bocode/h
'HETTREATREG': module to compute diagnostics for linear regression when
treatment effects are heterogeneous / hettreatreg represents OLS estimates
of the effect of a / binary treatment as a weighted average of the average
/ treatment effect on the treated (ATT) and the average treatment / effect
hgclg from http://fmwww.bc.edu/RePEc/bocode/h
'HGCLG': module to estimate geometric-complementary log log hurdle
regression / hgclg fits a geometric-cloglog maximum-likelihood hurdle
model / of depvar on indepvars, where depvar is a non-negative count /
variable. / KW: hurdle / KW: geometric / KW: cloglog / Requires: Stata
hglogit from http://fmwww.bc.edu/RePEc/bocode/h
'HGLOGIT': module to estimate geometric-logit hurdle regression / hglogit
fits a geometric-logit maximum-likelihood hurdle model / of depvar on
indepvars, where depvar is a non-negative count / variable. / KW: hurdle
/ KW: geometric / KW: logit / Requires: Stata version 9.1 /
hireg from http://fmwww.bc.edu/RePEc/bocode/h
'HIREG': module for hierarchial regression / The hireg command conducts
hierarchical regressions. Users / enter blocks of independent variables
which are added to the / model in successive steps. R-squared change is
reported at each / step along with a summary table at the end. All options
hlm from http://fmwww.bc.edu/RePEc/bocode/h
'HLM': module to invoke and run HLM v6 software from within Stata / This
set of commands enables users to invoke and run the HLM v.6 / software
from within Stata (v. 8.2).\xa0 HLM v. 6 must be installed / on the computer,
and the directory where the HLM software is / located must be specified in
hnbclg from http://fmwww.bc.edu/RePEc/bocode/h
'HNBCLG': module to estimate negative binomial-complementary log log
hurdle regression / hnbclg fits a negative binomial-cloglog
maximum-likelihood hurdle / model of depvar on indepvars, where depvar is
a non-negative / count variable. / KW: hurdle / KW: negative binomial /
hnblogit from http://fmwww.bc.edu/RePEc/bocode/h
'HNBLOGIT': module to estimate negative binomial-logit hurdle regression /
hnblogit fits a negative binomial-logit maximum-likelihood / hurdle model
of depvar on indepvars, where depvar is a / non-negative count variable.
/ KW: hurdle / KW: negative binomial / KW: logit / Requires: Stata version
hnbreg1 from http://fmwww.bc.edu/RePEc/bocode/h
'HNBREG1': module to estimate Heterogeneous linear negative binomial
regression (NB-1) / hnbreg1 fits a maximum-likelihood linear negative
binomial / regression model (NB-1), with a heterogeneous (Stata: /
-generalized-) parameterization of depvar on indepvars, where / depvar is
hpc from http://fmwww.bc.edu/RePEc/bocode/h
'HPC': module to perform specification test to discriminate between models
for non-negative data with many zeros / hpc computes the HPC test (Santos
Silva, Tenreyro, and / Windmeijer, Journal of Econometric Methods, 2015)
for the case / where the conditional expectation of a nonnegative variable
hpclg from http://fmwww.bc.edu/RePEc/bocode/h
'HPCLG': module to estimate Poisson-complementary log log hurdle
regression / hgclg fits a Poisson-cloglog maximum-likelihood hurdle model
of / depvar on indepvars, where depvar is a non-negative count / variable.
/ KW: hurdle / KW: Poisson / KW: cloglog / Requires: Stata version 9.1 /
hplogit from http://fmwww.bc.edu/RePEc/bocode/h
'HPLOGIT': module to estimate Poisson-logit hurdle regression / hplogit
fits a Poisson-logit maximum-likelihood hurdle model of / depvar on
indepvars, where depvar is a non-negative count / variable. / KW: hurdle
/ KW: Poisson / KW: logit / Requires: Stata version 91 /
hshaz from http://fmwww.bc.edu/RePEc/bocode/h
'HSHAZ': module to estimate discrete time (grouped data) proportional
hazards models / -hshaz- estimates, using ML, two discrete time (grouped
data) / proportional hazards regression models, one of which incorporates
/ a discrete mixture distribution to summarize unobserved / individual
icc2 from http://fmwww.bc.edu/RePEc/bocode/i
'ICC2': module to compute Intraclass correlation coefficients based on
crossed mixed regression / Textbooks often calculate the ICC using sums of
squares on a / subject-by-measurement matrix with non-missing cells. The
idea / of the ICC is to compare the wanted variation explained by a /
ietoolkit from http://fmwww.bc.edu/RePEc/bocode/i
'IETOOLKIT': module providing commands specially developed for Impact
Evaluations / ietookit provides a set of commands that address different /
aspects of data management and data analysis in relation to / Impact
Evaluations. The list of commands will be extended / continuously, and
igeintb from http://fmwww.bc.edu/RePEc/bocode/i
'IGEINTB': module to estimate intergenerational income elasticities (IGEs)
with multiple sets of instruments / igeintb estimates IGEs of children's
income with respect to / parental income. To estimate the lower bound,
igeintb uses an / estimator assumed to be affected by attenuation bias
igenerate from http://fmwww.bc.edu/RePEc/bocode/i
'IGENERATE': module to apply a variety of coding schemes, including
weighted effect coded interactions / igenerate generates new indicator
variables from categorical / predictors, including weighted effect coded
interactions. Note / that Stata’s built-in command contrast does this
igeset from http://fmwww.bc.edu/RePEc/bocode/i
'IGESET': module to estimate intergenerational income elasticities (IGEs)
with a single set of instruments / igeset estimates IGEs of children's
income with respect to / parental income. To estimate the lower bound,
igeset uses an / estimator assumed to be affected by attenuation bias
igesetci from http://fmwww.bc.edu/RePEc/bocode/i
'IGESETCI': module to compute confidence intervals for partially
identified intergenerational income elasticities (IGEs) / igesetci is a
post-estimation command that computes confidence / intervals for a
partially identified parameter (rather than for / the identified set) in
iia from http://fmwww.bc.edu/RePEc/bocode/i
'IIA': module to test the iia assumption in conditional logistic
regression (version 5) / Estimates McFadden's discrete choice model (with
clogit) and / subsequently performs Hausman tests for the assumption of /
'independence of irrelevant alternatives' (IIA) for each of the /
ineqrbd from http://fmwww.bc.edu/RePEc/bocode/i
'INEQRBD': module to calculate regression-based inequality decomposition /
ineqrbd performs regression-based decomposition of the / inequality in
depvar into the contributions accounted for by each / of the rhsvars. The
formulae used are those proposed by Fields / (2003) which, in turn, are
inmor from http://fmwww.bc.edu/RePEc/bocode/i
'INMOR': module to compute marginal odds ratios after model estimation /
lnmor is a post-estimation command to compute (adjusted) / marginal odds
ratios after logit or probit using G-computation. / By default, lnmor
obtains marginal ORs by applying fractional / logit to averaged
inteff3 from http://fmwww.bc.edu/RePEc/bocode/i
'INTEFF3': module to compute partial effects in a probit or logit model
with a triple dummy variable interaction term / inteff3 computes partial
effects in a probit or logit model with / a triple dummy variable
interaction term. These models may be / applied when the effect of a
interactplot from http://fmwww.bc.edu/RePEc/bocode/i
'INTERACTPLOT': module to generate plots for interaction terms of
multiplicative regressions / interactplot is a tool for generating plots
of predicted values / or marginal effects for polynomials or interaction
terms after / a multiplicative regression. The program detects /
interflex from http://fmwww.bc.edu/RePEc/bocode/i
'INTERFLEX': module to estimate multiplicative interaction models with
diagnostics and visualization / interflex performs diagnostics and
visualizations of / multiplicative interaction models. Besides
conventional linear / interaction models, it provides two additional
intreg2 from http://fmwww.bc.edu/RePEc/bocode/i
'INTREG2': module to perform interval regression with multiplicative
heteroskedasticity / This program is an expansion of -intreg-. It adds one
more option / to specify the conditional variance. The model then will
contain / multiplicative heteroskedasticity. / KW: interval regression /
ipdforest from http://fmwww.bc.edu/RePEc/bocode/i
'IPDFOREST': module to produce forest plot for individual patient data IPD
meta-analysis (one stage) / ipdforest is a post-estimation command which
uses the saved / estimates of an xtmixed or xtmelogit command for
multi-level / linear or logistic regression respectively. It will only
ipdpower from http://fmwww.bc.edu/RePEc/bocode/i
'IPDPOWER': module to perform simulation based power calculations for
mixed effects modelling / ipdpower is a simulations-based command that
calculates power / for complex mixed effects two-level data structures.
The command / was developed having individual patient data meta-analyses
ipwbreg from http://fmwww.bc.edu/RePEc/bocode/i
'IPWBREG': module to compute inverse propensity weights from Bernoulli
regression / ipwbreg fits a Bernoulli regression model for a binary /
dependent variable in a list of independent variables, and then / outputs
a list of inverse propensity weight variables. These / propensity weight
ipwlogit from http://fmwww.bc.edu/RePEc/bocode/i
'IPWLOGIT': module to fit marginal logistic regression by inverse
probability weighting / : ipwlogit fits marginal logistic regression of a
binary / dependent variable on a treatment variable, possibly adjusting /
for control variables by inverse probability weighting (IPW). The /
irax from http://fmwww.bc.edu/RePEc/bocode/i
'IRAX': module to perform isotonic regression analysis / A package for
implementing isotonic regression to ensure / monotonicity in the
y-variable when the x-variable is ordered. / Isotonic regression analysis
fits a step function, constrained to / be either monotonically
itpscore from http://fmwww.bc.edu/RePEc/bocode/i
'ITPSCORE': module to implement Iterative Propensity Score Logistic
Regression Model Search Procedure / itpscore performs the iterative
propensity score logistic / regression model search procedure described by
Imbens and Rubin / (2015). Given a binary outcome measure and a list of
itsa from http://fmwww.bc.edu/RePEc/bocode/i
'ITSA': module to perform interrupted time series analysis for single and
multiple groups / itsa estimates the effect of an intervention when the
outcome / variable is ordered as a time series and a number of
observations / are available in both preintervention and postintervention
itsadgp from http://fmwww.bc.edu/RePEc/bocode/i
'ITSADGP': module to generate artificial data for interrupted time-series
analysis / itsadgp generates artificial interrupted time series data using
/ the specifications defined by the user -- including / autoregressive
terms (lags). In the simplest case, the data / generated by itsadgp could
itspower from http://fmwww.bc.edu/RePEc/bocode/i
'ITSPOWER': module for simulation based power calculations for linear
interrupted time series (ITS) designs / itspower is a simulations-based
command that calculates power / for linear interrupted time series (ITS)
designs. The command / proceeds in two steps. First, it generates the
ivactest from http://fmwww.bc.edu/RePEc/bocode/i
'IVACTEST': module to perform Cumby-Huizinga test for autocorrelation
after IV/OLS estimation / ivactest performs the general specification test
of serial / correlation proposed by Cumby and Huizinga (1992) after OLS or
/ instrumental variables (IV) estimation. In their words, the null /
ivcloglog from http://fmwww.bc.edu/RePEc/bocode/i
'IVCLOGLOG': module to estimate a complementary log-log model with
endogenous covariates, instrumented via the control function approach
(i.e., 2SRI) / -ivcloglog- is essentially the same thing as -ivprobit,
twostep- / but for the -cloglog- model. -ivcloglog- estimates a /
ivendog from http://fmwww.bc.edu/RePEc/bocode/i
'IVENDOG': module to calculate Durbin-Wu-Hausman endogeneity test after
ivreg / ivendog computes a test for endogeneity in a regression estimated
/ via instrumental variables (IV), the null hypothesis for which / states
that an ordinary least squares (OLS) estimator of the / same equation
ivgauss2 from http://fmwww.bc.edu/RePEc/bocode/i
'IVGAUSS2': module to estimate two-parameter log-inverse Gaussian
regression / ivgauss2 fits a maximum-likelihood 2-parameter log-inverse /
Gaussian regression model of depvar on indepvars, where depvar / is a
non-negative count variable. The program may be used to / model
ivgmm0 from http://fmwww.bc.edu/RePEc/bocode/i
'IVGMM0': module to perform instrumental variables via GMM / ivgmm0
estimates a linear regression model containing endogenous / regressors via
a generalized method of moments instrumental / variables estimator
(GMM-IV) that allows for heteroskedasticity / of unknown form, with a
ivgravity from http://fmwww.bc.edu/RePEc/bocode/i
'IVGRAVITY': module containing method-of-moment IV estimators of
exponential-regression models with two-way fixed effects from a
cross-section of data on dyadic interactions and endogenous covariates /
ivgravity computes Jochmans and Verardi (2019) generalisation to / the IV
ivhettest from http://fmwww.bc.edu/RePEc/bocode/i
'IVHETTEST': module to perform Pagan-Hall and related heteroskedasticity
tests after IV / ivhettest performs various flavors of Pagan and Hall's
(1983) / tests of heteroskedasticity for instrumental variables (IV) /
estimation. It will also perform the related standard /
ivmediate from http://fmwww.bc.edu/RePEc/bocode/i
'IVMEDIATE': module to perform Causal mediation analysis in
instrumental-variables regressions / ivmediate implements the causal
mediation analysis framework for / linear IV models introduced by Dippel
et al. (2019). It estimates / three effects: i) the total effect of a
ivpermute from http://fmwww.bc.edu/RePEc/bocode/i
'IVPERMUTE': module to estimate nearly collinear robust instrumental
variables regression / ivpermute estimates 2SLS coefficients using
formulas based upon / the partitioned regression. Estimates using the
partitioned / regression are more robust to near collinearity among the /
ivpois from http://fmwww.bc.edu/RePEc/bocode/i
'IVPOIS': module to estimate an instrumental variables Poisson regression
via GMM / ivpois implements a Generalized Method of Moments (GMM)
estimator / of Poisson regression and allows endogenous variables to be /
instrumented by excluded instruments, hence the acronym for / Instrumental
ivprob-ivtobit from http://fmwww.bc.edu/RePEc/bocode/i
'IVPROB-IVTOBIT': modules to estimate instrumental variables probit and
tobit / These programs implement Amemiya Generalized Least Squares (AGLS)
/ estimators for probit and tobit with endogenous regressors. / Newey
(J.Metr. 1987, eq. 5.6) provides the formulas used. The / endogenous
ivprob-ivtobit6 from http://fmwww.bc.edu/RePEc/bocode/i
'IVPROB-IVTOBIT6': modules to estimate instrumental variables probit and
tobit / These programs implement Amemiya Generalized Least Squares (AGLS)
/ estimators for probit and tobit with endogenous regressors. / Newey
(J.Metr. 1987, eq. 5.6) provides the formulas used. The / endogenous
ivqreg2 from http://fmwww.bc.edu/RePEc/bocode/i
'IVQREG2': module to provide structural quantile function estimation /
ivqreg2 estimates the structural quantile functions defined by /
Chernozhukov and Hansen (J. Econometrics, 2008) using the method / of
Machado and Santos Silva (J. Econometrics, 2018). If no / instruments are
ivreg2 from http://fmwww.bc.edu/RePEc/bocode/i
'IVREG2': module for extended instrumental variables/2SLS and GMM
estimation / ivreg2 provides extensions to Stata's official ivregress and
/ newey. Its main capabilities: two-step feasible GMM estimation; /
continuously updated GMM estimation (CUE); LIML and k-class / estimation;
ivreg210 from http://fmwww.bc.edu/RePEc/bocode/i
'IVREG210': module for extended instrumental variables/2SLS and GMM
estimation (v10) / ivreg210 provides extensions to Stata's official
ivregress and / newey. Its main capabilities: two-step feasible GMM
estimation; / continuously updated GMM estimation (CUE); LIML and k-class
ivreg28 from http://fmwww.bc.edu/RePEc/bocode/i
'IVREG28': module for extended instrumental variables/2SLS and GMM
estimation (v8) / ivreg28 provides extensions to Stata's official ivreg
and newey. / ivreg28 supports the same command syntax as official ivreg
and / supports (almost) all of its options. The main extensions: /
ivreg29 from http://fmwww.bc.edu/RePEc/bocode/i
'IVREG29': module for extended instrumental variables/2SLS and GMM
estimation (v9) / ivreg2 provides extensions to Stata's official ivreg and
newey. / ivreg2 supports the same command syntax as official ivreg and /
supports (almost) all of its options. The main extensions: / two-step
ivreg2h from http://fmwww.bc.edu/RePEc/bocode/i
'IVREG2H': module to perform instrumental variables estimation using
heteroskedasticity-based instruments / ivreg2h estimates an instrumental
variables regression model / providing the option to generate instruments
using Lewbel's / (J.Bus.Ec.Stat., 2012) method. This technique allows the
ivreg2hdfe from http://fmwww.bc.edu/RePEc/bocode/i
'IVREG2HDFE': module to estimate an Instrumental Variable Linear
Regression Model with two High Dimensional Fixed Effects / This command
builds on the command reg2hdfe and ivreg2 for / estimation of a linear
instrumental variables regression model / with two high dimensional fixed
ivreg_ss from http://fmwww.bc.edu/RePEc/bocode/i
'IVREG_SS': module to compute confidence intervals, standard errors, and
p-values in an IV regression in which the excluded instrumental variable
has a shift-share structure / This package computes confidence intervals,
standard errors, and / p-values in an IV regression in which the excluded
ivreghdfe from http://fmwww.bc.edu/RePEc/bocode/i
'IVREGHDFE': module for extended instrumental variable regressions with
multiple levels of fixed effects / ivreghdfe is essentially ivreg2 with an
additional absorb() / option from reghdfe. / KW: regression / KW:
instrumental variables / KW: fixed effects / KW: high dimension fixed
ivregress2 from http://fmwww.bc.edu/RePEc/bocode/i
'IVREGRESS2': module to export first and second-stage results similar to
ivregress / ivregress2 provides a fast and easy way to export both the /
first-stage and the second-stage results similar to ivregress, on / which
it is based. / KW: ivregress / KW: 2sls / KW: liml / KW: gmm / KW:
ivreset from http://fmwww.bc.edu/RePEc/bocode/i
'IVRESET': module to perform Ramsey/Pesaran-Taylor/Pagan-Hall RESET
specification test after IV/GMM/OLS estimation / ivreset performs various
flavors of Ramsey's regression error / specification test (RESET) as
adapted by Pesaran and Taylor / (1999) and Pagan and Hall (1983) for
ivtreatreg from http://fmwww.bc.edu/RePEc/bocode/i
'IVTREATREG': module to estimate binary treatment models with
idiosyncratic average effect / ivtreatreg estimates five different
(binary) treatment models / with and without idiosyncratic (or
heterogeneous) average effect. / Depending on the model specified,
ivvif from http://fmwww.bc.edu/RePEc/bocode/i
'IVVIF': module to report variance inflation factors after IV / ivvif
extends Stata's official vif/estat vif command, which / reports variance
inflation factors. It differs in two ways. As / well as working after
regress, it can run after instrumented / regressions done with ivreg or
jwdid from http://fmwww.bc.edu/RePEc/bocode/j
'JWDID': module to estimate Difference-in-Difference models using Mundlak
approach / JWDID is a command that implements the estimation approach /
proposed by Wooldridge (2021), based on the Mundlak approach. / The main
idea of JWDID is that consistent estimations for ATT's / can be obtained
kernreg1 from http://fmwww.bc.edu/RePEc/bocode/k
'KERNREG1': module to compute kernel regression (Nadaraya-Watson
estimator) / kernreg1 is an updated and improved version of kernreg,
published / in STB-30 as package snp9. kernreg1 calculates the /
Nadaraya-Watson nonparametric regression. Differences between / kernreg
kernreg2 from http://fmwww.bc.edu/RePEc/bocode/k
'KERNREG2': module to compute kernel regression (Nadaraya-Watson
estimator) / kernreg2 is an updated and improved version of kernreg,
published / in STB-30 as package snp9. kernreg2 calculates the /
Nadaraya-Watson nonparametric regression. By default, kernreg2 / draws
kfoldclass from http://fmwww.bc.edu/RePEc/bocode/k
'KFOLDCLASS': module for generating classification statistics of k-fold
cross-validation for binary outcomes / kfoldclass performs k-fold
cross-validation for regression and / machine learning models with a
binary outcome and then produces / classification measures to assist in
khb from http://fmwww.bc.edu/RePEc/bocode/k
'KHB': module to decompose total effects into direct and indirect via
KHB-method / decomposes the total effect of a variable into direct and /
indirect effects using the KHB-method developed by Karlson, Holm, / and
Breen (2011). The method is developed for binary and logit / probit
kinkyreg from http://fmwww.bc.edu/RePEc/bocode/k
'KINKYREG': module to perform kinky least squares estimation and inference
/ kinkyreg implements the kinky least squares (KLS) estimator / proposed
by Kiviet (J. Econometrics, 2020). The estimates are / graphically
compared to the instrumental variables (IV) / / two-stage least squares
kitchensink from http://fmwww.bc.edu/RePEc/bocode/k
'KITCHENSINK': module to return the model with the highest number of
statistically significant predictors / The command kitchensink promotes
bad practice amongst the / scientific community by returning the
regression model with the / highest number of statistically significant
kmatch from http://fmwww.bc.edu/RePEc/bocode/k
'KMATCH': module module for multivariate-distance and propensity-score
matching, including entropy balancing, inverse probability weighting,
(coarsened) exact matching, and regression adjustment / kmatch matches
treated and untreated observations with respect / to covariates and, if
koenker from http://fmwww.bc.edu/RePEc/bocode/k
'KOENKER': module to perform Koenker/White detailed test for
heteroskedasticity / koenker implements the Koenker/White N*R2 test for /
heteroskedasticity after regress. estat hettest with the mtest / option
is based on the Breusch-Pagan/Cook-Weisberg LM test, which / has a null
krls from http://fmwww.bc.edu/RePEc/bocode/k
'KRLS': module to perform Kernel–Based Regularized Least Squares / krls
implements Kernel-Based Regularized Least Squares (KRLS), a / machine
learning method described in Hainmueller and Hazlett / (2013) that allows
users to solve regression and classification / problems without manual
kssur from http://fmwww.bc.edu/RePEc/bocode/k
'KSSUR': module to calculate Kapetanios, Shin & Snell unit root test
statistic along with critical values and p-values / kssur computes
Kapetanios, Shin & Snell KSS (J. Metr., 2003) / OLS-detrending based unit
root tests against the alternative / of a globally stationary exponential
ksur from http://fmwww.bc.edu/RePEc/bocode/k
'KSUR': module to calculate Kapetanios & Shin unit root test statistic
along with finite-sample critical values and p-values / ksur computes
Kapetanios & Shin KS (Ec.Let., 2008) / GLS-detrending based unit root
tests against the alternative / of a globally stationary exponential
kwstat from http://fmwww.bc.edu/RePEc/bocode/k
'KWSTAT': module to compute kernel weighted sample statistics / kwstat
computes sample statistics of a variable y in function of / another
variable x. The approach is inspired by the kernel / regression
(Nadaraya-Watson estimator) which computes the / conditional mean of y in
laplacereg from http://fmwww.bc.edu/RePEc/bocode/l
'LAPLACEREG': module to perform Laplace regression for censored data /
laplacereg estimates Laplace regression models for / percentiles of a
response variable with possibly censored / data. Typical applications are
in time-to-event or survival / analysis. For example, Laplace regression
lars from http://fmwww.bc.edu/RePEc/bocode/l
'LARS': module to perform least angle regression / Least Angle Regression
is a model-building algorithm that / considers parsimony as well as
prediction accuracy. This / method is covered in detail by the paper
Efron, Hastie, Johnstone / and Tibshirani (2004), published in The Annals
lassopack from http://fmwww.bc.edu/RePEc/bocode/l
'LASSOPACK': module for lasso, square-root lasso, elastic net, ridge,
adaptive lasso estimation and cross-validation / LASSOPACK is a suite of
programs for penalized regression / methods suitable for the
high-dimensional setting where the / number of predictors p may be large
lddtest from http://fmwww.bc.edu/RePEc/bocode/l
'LDDTEST': module to perform logarithmic density discontinuity equivalence
testing for regression discontinuity designs / lddtest performs
equivalence testing on logarithmic density / discontinuities of running
variables at the cutoff in regression / discontinuity designs, as
ldecomp from http://fmwww.bc.edu/RePEc/bocode/l
'LDECOMP': module decomposing the total effects in a logistic regression
into direct and indirect effects / ldecomp decomposes the total effects of
a categorical variable / in logistic regresion into direct and indirect
effects using a / method method by Erikson et al. (2005) and a
leanout from http://fmwww.bc.edu/RePEc/bocode/l
'LEANOUT': module to produce lean output formatting for estimation results
/ Regression output from Stata's regress command provides much / output
that is often not needed on every output screen; in / addition, it does
not allow for seeing results with the / appropriate number of digits;
levpredict from http://fmwww.bc.edu/RePEc/bocode/l
'LEVPREDICT': module to compute log-linear level predictions reducing
retransformation bias / levpredict is a post-estimation command for use
after a / log-linear regression model has been estimated. It generates /
predictions of the levels of the dependent variable for the / estimation
lgamma2 from http://fmwww.bc.edu/RePEc/bocode/l
'LGAMMA2': module to estimate two-parameter log-gamma regression / lgamma2
fits a maximum-likelihood 2-parameter log-gamma / regression model of
depvar on indepvars, where depvar is a / non-negative count variable. The
program may be used to model / under-dispersed Poisson count data.
lincheck from http://fmwww.bc.edu/RePEc/bocode/l
'LINCHECK': module to graphically assess the linearity of a continuous
covariate in a regression model / lincheck provides a quick-and-dirty
check of whether a continuous / covariate in a general linearized model
(GLM) is linear in the / link function. lincheck makes a new categorical
lintrend from http://fmwww.bc.edu/RePEc/bocode/l
'LINTREND': module to graph observed proportions or means for a continuous
or ordinal X variable / lintrend examines the "linearity" assumption for
an ordinal or / interval X variable against category means of a continuous
/ outcome or the logodds of a binary outcome; default prints means / or
listmiss from http://fmwww.bc.edu/RePEc/bocode/l
'LISTMISS': module to analyse missing values related to an estimation
command / listmiss is a post-estimation command that reports the number of
/ missing values for each independent variable. For each / independent
variable a flag is created to indicate when the / variable is missing. The
listreg from http://fmwww.bc.edu/RePEc/bocode/l
'LISTREG': module for the analysis of list experiments using linear
regression / listreg fits a linear model to data from a list experiment /
(a.k.a. item count technique) or to data collected by the / item-sum
technique. Single-list and double-list designs are / supported. / KW:
lmabg from http://fmwww.bc.edu/RePEc/bocode/l
'LMABG': Module to compute OLS Autocorrelation Breusch-Godfrey Test at
Higher Order AR(p) / lmabg computes OLS Autocorrelation Breusch-Godfrey
Test at / Higher Order AR(p) / KW: regression / KW: autocorrelation tests
/ KW: non-normality / KW: Breusch-Godfrey Test / Requires: Stata version
lmabg2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMABG2': Module to Compute 2SLS-IV Autocorrelation Breusch-Godfrey Test
at Higher Order AR(p) / lmabg2 computes 2SLS-IV Autocorrelation
Breusch-Godfrey Test at / Higher Order AR(p) / KW: regression / KW:
autocorrelation tests / KW: non-normality / KW: Breusch-Godfrey Test /
lmabgnl from http://fmwww.bc.edu/RePEc/bocode/l
'LMABGNL': module to compute NLS Autocorrelation Breusch-Godfrey Test at
Higher Order AR(p) / lmabgnl computes Non Linear Least Squares
Autocorrelation / Breusch-Godfrey Test at Higher Order AR(p) after nl
command / KW: Autocorrelation / KW: Regression / KW: NLS / KW: Non Linear
lmabgxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMABGXT': module to compute Panel Data Autocorrelation Breusch-Godfrey
Test / lmabgxt computes Panel Data Autocorrelation Breusch-Godfrey Test d
/ KW: Regression / KW: Panel Data / KW: Cross Section-Time Series / KW:
Autocorrelation / KW: Breusch-Godfrey Test / Requires: Stata version 11.2
lmabp from http://fmwww.bc.edu/RePEc/bocode/l
'LMABP': module to compute Box-Pierce Autocorrelation LM Test at Higher
Order AR(p) / lmabp computes Box-Pierce Autocorrelation LM Test at Higher
/ Order AR(p) after OLS Regression / KW: Autocorrelation / KW: regression
/ KW: OLS / KW: Box-Pierce Autocorrelation LM Test / Requires: Stata
lmabp2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMABP2': module to compute 2SLS-IV Box-Pierce Autocorrelation LM Test at
Higher Order AR(p) / lmabp2 computes 2SLS-IV Box-Pierce Autocorrelation LM
Test at / Higher Order AR(p) after OLS Regression / KW: Autocorrelation /
KW: regression / KW: OLS / KW: Box-Pierce Autocorrelation LM Test /
lmabpg from http://fmwww.bc.edu/RePEc/bocode/l
'LMABPG': module to compute OLS Autocorrelation Breusch-Pagan-Godfrey Test
at Higher Order AR(p) / lmabpg computes OLS Autocorrelation
Breusch-Pagan-Godfrey Test / at Higher Order AR(p) / KW: Regression / KW:
OLS / KW: Autocorrelation / KW: Breusch-Pagan-Godfrey Test / Requires:
lmabpg2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMABPG2': Module to Compute 2SLS-IV Autocorrelation Breusch-Pagan-Godfrey
Test at Higher Order AR(p) / lmabpg2 computes 2SLS-IV Autocorrelation
Breusch-Pagan-Godfrey / Test at Higher Order AR(p) / KW: Regression / KW:
2SLS / KW: LIML / KW: MELO / KW: GMM / KW: K-CLASS / KW: Two-Stage Least
lmabpgnl from http://fmwww.bc.edu/RePEc/bocode/l
'LMABPGNL': Module to Compute NLS Autocorrelation Breusch-Pagan-Godfrey
Test at Higher Order AR(p) / lmabpgnl computes Non Linear Least Squares
Autocorrelation / Breusch-Pagan-Godfrey Test at Higher Order AR(p) after
nl command d / KW: Regression / KW: NLS / KW: Non Linear Least Squares /
lmabpgxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMABPGXT': module to compute Panel Data Autocorrelation
Breusch-Pagan-Godfrey Test / lmabpgxt computes Panel Data Autocorrelation
/ Breusch-Pagan-Godfrey Test / KW: Regression / KW: Panel Data / KW: Cross
Sections-Time Series / KW: Autocorrelation / KW: Breusch-Pagan-Godfrey
lmabpxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMABPXT': module to compute Panel Data Autocorrelation Box-Pierce Test /
lmabpxt computes Panel Data Autocorrelation Box-Pierce Test / KW:
Regression / KW: Panel Data / KW: Cross Sections-Time Series / KW:
Autocorrelation / KW: Box-Pierce Test / Requires: Stata version 11.2 /
lmabxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMABXT': module to compute Panel Autocorrelation Baltagi Test / lmabxt
computes Panel Autocorrelation Baltagi Test / KW: Regression / KW: Panel
/ KW: Cross Section-Time Series / KW: Autocorrelation / KW: Baltagi Test /
Requires: Stata version 11 / Distribution-Date: 20130416 / Author: Emad
lmadurh from http://fmwww.bc.edu/RePEc/bocode/l
'LMADURH': module to compute Durbin h, Harvey LM, Wald LM Autocorrelation
Tests / lmadurh computes Durbin h, Harvey LM, Wald LM Autocorrelation /
Tests after OLS - ALS Regression / KW: Durbin h / KW: Harvey / KW: Wald /
KW: autocorrelation / KW: OLS / Requires: Stata version 10.1 /
lmadurh2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMADURH2': module to compute 2SLS-IV Autocorrelation Dynamic Durbin h,
Harvey LM, and Wald Tests / lmadurh2 computes 2SLS-IV Autocorrelation
Dynamic Durbin h, / Harvey LM, and Wald Tests / KW: Autocorrelation / KW:
Regression / KW: 2SLS / KW: LIML / KW: GMM / KW: Durbin h test / KW:
lmadurhxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMADURHXT': module to Compute Panel Data Autocorrelation Dynamic Durbin h
and Harvey LM Tests / lmadurhxt computes Panel Data Autocorrelation
Dynamic Durbin h / and Harvey LM Tests / KW: Regression / KW: Panel / KW:
Cross Section-Time Series / KW: Autocorrelation / KW: Durbin h Test / KW:
lmadurm from http://fmwww.bc.edu/RePEc/bocode/l
'LMADURM': module to compute OLS Autocorrelation Dynamic Durbin m Test at
Higher Order AR(p) / lmadurm computes OLS Autocorrelation Dynamic Durbin m
Test at / Higher Order AR(p) / KW: Regression / KW: OLS / KW:
Autocorrelation / KW: Durbin m Test / Requires: Stata version 11.2 /
lmadurm2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMADURM2': module to compute 2SLS-IV Autocorrelation Dynamic Durbin m
Test at Higher Order AR(p) / lmadurm2 computes 2SLS-IV Autocorrelation
Dynamic Durbin m Test / at Higher Order AR(p) / KW: Regression / KW: 2SLS
/ KW: LIML / KW: MELO / KW: GMM / KW: K-CLASS / KW: Two-Stage Least
lmadurmxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMADURMXT': module to compute Panel Data Autocorrelation Dynamic Durbin m
Test / lmadurxt computes Panel Data Autocorrelation Dynamic Durbin m /
Test / KW: Regression / KW: Panel / KW: Cross Section-Time Series / KW:
Autocorrelation / KW: Durbin m Panel Test / Requires: Stata version 11 /
lmadw from http://fmwww.bc.edu/RePEc/bocode/l
'LMADW': module to compute Durbin-Watson Autocorrelation Test / lmavon
computes Durbin-Watson Autocorrelation Test after OLS / Regression / KW:
Autocorrelation / KW: regression / KW: OLS / KW: Durbin-Watson test /
Requires: Stata version 10.1 / Distribution-Date: 20111029 / Author: Emad
lmadw2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMADW2': module to compute 2SLS-IV Autocorrelation Durbin-Watson Test at
Higher Order AR(p) / lmadw2 computes 2SLS-IV Autocorrelation Durbin-Watson
Test at / Higher Order AR(p) / KW: Autocorrelation / KW: Regression / KW:
2SLS / KW: LIML / KW: GMM / KW: Durbin-Watson test / Requires: Stata
lmadwxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMADWXT': module to compute Panel Data Autocorrelation Durbin-Watson Test
/ lmadwxt computes Panel Data Autocorrelation Durbin-Watson Test / KW:
Regression / KW: Panel Data / KW: Cross Section-Time Series / KW:
Autocorrelation / KW: Durbin-Watson Test / Requires: Stata version 11.2 /
lmalb from http://fmwww.bc.edu/RePEc/bocode/l
'LMALB': module to compute Ljung-Box Autocorrelation LM Test at Higher
Order AR(p) / lmalb computes Ljung-Box Autocorrelation LM Test at Higher
Order / AR(p) after OLS regression / KW: autocorrelation / KW: Ljung-Box /
KW: OLS / KW: regression / Requires: Stata version 10.1 /
lmalb2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMALB2': module to compute 2SLS-IV Autocorrelation Ljung-Box Test at
Higher Order AR(p) / lmalb2 computes 2SLS-IV Autocorrelation Ljung-Box
Test at Higher / Order AR(p) / KW: Autocorrelation / KW: Regression / KW:
2SLS / KW: LIML / KW: GMM / KW: Ljung-Box test / KW: Box-Pierce test /
lmanlsur from http://fmwww.bc.edu/RePEc/bocode/l
'LMANLSUR': module to perform Overall System NL-SUR Autocorrelation Tests
/ Stata Module to Compute Overall System NL-SUR Autocorrelation / Tests
after nlsur Regressions / KW: Overall System NL-SUR Autocorrelation Tests
/ KW: Harvey / KW: Durbin-Watson / KW: Guilkey / Requires: Stata version
lmasem from http://fmwww.bc.edu/RePEc/bocode/l
'LMASEM': module to perform Overall System Structural Equation Modeling
(SEM) Autocorrelation Tests / lmasem Computes Overall System
Autocorrelation Tests, after / Structural Equation Modeling (SEM)
Regressions / KW: SEM / KW: Structural Equation Modeling / KW: Overall
lmavon from http://fmwww.bc.edu/RePEc/bocode/l
'LMAVON': module to compute Von Neumann Ratio Autocorrelation Test at
Higher Order AR(p) / lmavon computes Von Neumann Ratio Autocorrelation
Test at Higher / Order AR(p) after OLS Regression / KW: Autocorrelation /
KW: regression / KW: OLS / KW: Von Neumann Ratio Test / Requires: Stata
lmavon2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMAVON2': Module to Compute 2SLS-IV Autocorrelation Von Neumann Ratio
Test at Higher Order AR(p) / lmavon2 computes 2SLS-IV Autocorrelation Von
Neumann Ratio Test / at Higher Order AR(p) / KW: Regression / KW: 2SLS /
KW: KW: LIML / KW: MELO / KW: GMM / KW: K-CLASS / KW: Two-Stage Least
lmawxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMAWXT': Module to Compute Panel Data Autocorrelation Wooldridge Test /
lmawxt computes Panel Data Autocorrelation Wooldridge Test / KW:
Regression / KW: Panel / KW: Cross Sections-Time Series / KW:
Autocorrelation / KW: Wooldridge Test / Requires: Stata version 11.2 /
lmaz from http://fmwww.bc.edu/RePEc/bocode/l
'LMAZ': module to compute OLS Autocorrelation Z Test at Higher Order AR(p)
/ lmaz computes OLS Autocorrelation Z Test at Higher Order AR(p) / KW:
Regression / KW: OLS / KW: Autocorrelation / KW: Z Test / Requires: Stata
version 11 / Distribution-Date: 20130416 / Author: Emad Abd Elmessih
lmaznl from http://fmwww.bc.edu/RePEc/bocode/l
'LMAZNL': Module to Compute NLS Autocorrelation Z Test at Higher Order
AR(p) / lmaznl computes NLS Autocorrelation Z Test at Higher Order AR(p) d
/ KW: Regression / KW: NLS / KW: Non Linear Least Squares / KW:
Autocorrelation / KW: Z Test / Requires: Stata version 11.2 /
lmcovnlsur from http://fmwww.bc.edu/RePEc/bocode/l
'LMCOVNLSUR': module to perform Breusch-Pagan Lagrange Multiplier Diagonal
Covariance Matrix Test after (NL-SUR) Regressions / Stata Module to
Compute Breusch-Pagan Lagrange Multiplier / Diagonal Covariance Matrix
Test after (NL-SUR) Regressions / KW: Diagonal Covariance Matrix Test /
lmcovreg3 from http://fmwww.bc.edu/RePEc/bocode/l
'LMCOVREG3': module to Compute Breusch-Pagan Lagrange Multiplier Diagonal
Covariance Matrix Test after (3SLS-SURE) Regressions / lmcovreg3 Computes
Breusch-Pagan Lagrange Multiplier Diagonal / Covariance Matrix Test after
(3SLS-SURE) Regressions / KW: Breusch-Pagan test / KW: diagonal covariance
lmcovsem from http://fmwww.bc.edu/RePEc/bocode/l
'LMCOVSEM': module to perform Overall System Structural Equation Modeling
(SEM) Breusch-Pagan Lagrange Multiplier Diagonal Covariance Matrix Test /
lmcovsem Computes Overall System Breusch-Pagan Lagrange / Multiplier
Diagonal Covariance Matrix Test, after Structural / Equation Modeling
lmfreg from http://fmwww.bc.edu/RePEc/bocode/l
'LMFREG': module to Compute OLS Linear vs Log-Linear Functional Form Tests
/ lmfreg computes OLS Linear vs Log-Linear Functional Form Tests / KW:
regression / KW: OLS / KW: Box-Cox Test / KW: Bera-McAleer BM Test / KW:
Davidson-Mackinnon PE Test / Requires: Stata version 11 /
lmfreg2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMFREG2': module to compute 2SLS-IV Linear vs Log-Linear Functional Form
Tests / lmadw2 computes 2SLS-IV Linear vs Log-Linear Functional Form /
Tests / KW: Regression / KW: 2SLS / KW: LIML / KW: GMM / KW: Antilog R2 /
KW: Box-Cox test / KW: Bera-McAleer BM Test / KW: Davidson-Mackinnon PE
lmharch2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMHARCH2': Module to Compute 2SLS-IV Heteroscedasticity Engle (ARCH) Test
/ lmharch2 computes 2SLS-IV Heteroscedasticity Engle (ARCH) Test / KW:
Regression / KW: 2SLS / KW: LIML / KW: MELO / KW: GMM / KW: K-CLASS / KW:
Two-Stage Least Squares (2SLS) / KW: Limited-Information Maximum
lmharchnl from http://fmwww.bc.edu/RePEc/bocode/l
'LMHARCHNL': Module to Compute NLS Heteroscedasticity Engle (ARCH) Test /
lmharchnl computes NLS Heteroscedasticity Engle (ARCH) Test / KW:
Regression / KW: NLS / KW: Non Linear Least Squares / KW:
Heteroscedasticity / KW: Engle ARCH Test / Requires: Stata version 11.2 /
lmharchxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMHARCHXT': Module to Compute Panel Data Heteroscedasticity Engle (ARCH)
Test / lmharchxt computes Panel Data Heteroscedasticity Engle (ARCH) /
Test / KW: Regression / KW: Panel / KW: Cross Sections-Time Series / KW:
Heteroscedasticity / KW: Engle ARCH Test / Requires: Stata version 11.2 /
lmhcw from http://fmwww.bc.edu/RePEc/bocode/l
'LMHCW': Module to Compute OLS Heteroscedasticity Cook-Weisberg Test /
lmhcw computes OLS Heteroscedasticity Cook-Weisberg Test / KW: regression
/ KW: Heteroscedasticity Tests / KW: Cook-Weisberg Test / KW: King Test /
Requires: Stata version 11 / Distribution-Date: 20140514 / Author: Emad
lmhcw2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMHCW2': Module to Compute 2SLS-IV Heteroscedasticity Cook-Weisberg Test
/ lmhcw2 computes 2SLS-IV Heteroscedasticity Cook-Weisberg Test / KW:
Regression / KW: 2SLS / KW: LIML / KW: MELO / KW: GMM / KW: K-CLASS / KW:
Two-Stage Least Squares (2SLS) / KW: Limited-Information Maximum
lmhcwxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMHCWXT': Module to Compute Panel Data Heteroscedasticity Cook-Weisberg
Test / lmhcwxt computes Panel Data Heteroscedasticity Cook-Weisberg / Test
/ KW: Regression / KW: Panel / KW: Cross Sections-Time Series / KW:
Heteroscedasticity / KW: Cook-Weisberg Test / KW: King Test / Requires:
lmhgl from http://fmwww.bc.edu/RePEc/bocode/l
'LMHGL': module to Compute Glejser Lagrange Multiplier Heteroscedasticity
Test for Residuals after OLS Regression / lmhgl Computes Glejser Lagrange
Multiplier Heteroscedasticity / Test for Residuals after OLS Regression /
KW: Heteroscedasticity / KW: regression / KW: Lagrange multiplier / KW:
lmhgl2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMHGL2': Module to Compute 2SLS-IV Heteroscedasticity Glejser Test /
lmhgl2 computes 2SLS-IV Heteroscedasticity Glejser Test / KW: Regression /
KW: 2SLS / KW: LIML / KW: MELO / KW: GMM / KW: K-CLASS / KW: Two-Stage
Least Squares (2SLS) / KW: Limited-Information Maximum Likelihood (LIML) /
lmhglnl from http://fmwww.bc.edu/RePEc/bocode/l
'LMHGLNL': module to compute NLS Heteroscedasticity Glejser Test / lmhglnl
computes NLS Heteroscedasticity Glejser Test / KW: Regression / KW: NLS /
KW: Non Linear Least Squares / KW: Heteroscedasticity Test / KW:
Glejser Test / Requires: Stata version 11.2 / Distribution-Date: 20150315
lmhharv from http://fmwww.bc.edu/RePEc/bocode/l
'LMHHARV': module to Compute Harvey Lagrange Multiplier Heteroscedasticity
Test for Residuals after OLS Regression / lmhharv Computes Harvey Lagrange
Multiplier Heteroscedasticity / Test for Residuals after OLS Regression /
KW: Heteroscedasticity / KW: regression / KW: Lagrange multiplier / KW:
lmhharv2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMHHARV2': Module to Compute 2SLS-IV Heteroscedasticity Harvey Test /
lmhharv2 computes 2SLS-IV Heteroscedasticity Harvey Test / KW: Regression
/ KW: 2SLS / KW: LIML / KW: MELO / KW: GMM / KW: K-CLASS / KW: Two-Stage
Least Squares (2SLS) / KW: Limited-Information Maximum Likelihood (LIML) /
lmhhp from http://fmwww.bc.edu/RePEc/bocode/l
'LMHHP': Module to Compute OLS Heteroscedasticity Hall-Pagan Test / lmhhp
computes OLS Heteroscedasticity Hall-Pagan Test / KW: regression / KW:
Heteroscedasticity Tests / KW: Hall-Pagan Test / Requires: Stata version
11 / Distribution-Date: 20140514 / Author: Emad Abd Elmessih Shehata,
lmhhp2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMHHP2': Module to Compute 2SLS-IV Heteroscedasticity Hall-Pagan Test /
lmhhp computes 2SLS-IV Heteroscedasticity Hall-Pagan Test / KW: regression
/ KW: Heteroscedasticity Tests / KW: Hall-Pagan Test / Requires: Stata
version 11 / Distribution-Date: 20140808 / Author: Emad Abd Elmessih
lmhhpnl from http://fmwww.bc.edu/RePEc/bocode/l
'LMHHPNL': module to compute NLS Heteroscedasticity Hall-Pagan Test /
lmhhpnl computes NLS Heteroscedasticity Hall-Pagan Test / KW: Regression /
KW: NLS / KW: Non Linear Least Squares / KW: Heteroscedasticity / KW:
Hall-Pagan Test / Requires: Stata version 11.2 / Distribution-Date:
lmhhpxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMHHPXT': module to compute Panel Data Heteroscedasticity Hall-Pagan Test
/ lmhhpxt Computes Panel Data Heteroscedasticity Hall-Pagan Test / KW:
Regression / KW: Panel / KW: Cross Section-Time Series / KW:
Heteroscedasticity / KW: Hall-Pagan Test / Requires: Stata version 11 /
lmhmss2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMHMSS2': Module to Compute 2SLS-IV Heteroscedasticity
Machado-Santos-Silva Test / lmhmss2 computes 2SLS-IV Heteroscedasticity
Machado-Santos-Silva / Test / KW: regression / KW: Heteroscedasticity
Tests / KW: Machado-Santos-Silva Test / Requires: Stata version 11 /
lmhreg3 from http://fmwww.bc.edu/RePEc/bocode/l
'LMHREG3': module to compute Overall System Heteroscedasticity Tests after
(3SLS-SURE) Regressions / lmhreg3 computes Overall System
Heteroscedasticity Tests after / (3SLS-SURE) Regressions / KW: 3SLS / KW:
SURE / KW: regression / KW: Heteroscedasticity / KW: Engle LM ARCH Test /
lmhsem from http://fmwww.bc.edu/RePEc/bocode/l
'LMHSEM': module to perform Overall System Structural Equation Modeling
(SEM) Heteroscedasticity Tests / lmhsem Computes Overall System
Heteroscedasticity Tests, after / Structural Equation Modeling (SEM)
Regressions / KW: SEM / KW: Structural Equation Modeling / KW: Overall
lmhwald from http://fmwww.bc.edu/RePEc/bocode/l
'LMHWALD': module to compute OLS Heteroscedasticity Wald Test / lmhwald
computes OLS Heteroscedasticity Wald Test / KW: Regression / KW: OLS / KW:
Heteroscedasticity / KW: Wald Test / Requires: Stata version 11 /
Distribution-Date: 20130416 / Author: Emad Abd Elmessih Shehata,
lmhwaldxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMHWALDXT': module to compute Panel Data Heteroscedasticity Wald Test /
lmhwaldxt Computes Panel Data Heteroscedasticity Wald Test / KW:
Regression / KW: Panel / KW: Cross Section-Time Series / KW:
Heteroscedasticity / KW: Wald Test / Requires: Stata version 11 /
lmnad from http://fmwww.bc.edu/RePEc/bocode/l
'LMNAD': Module to Compute OLS Non Normality Anderson-Darling Test / lmnad
computes OLS Non Normality Anderson-Darling Test / KW: regression / KW:
Heteroscedasticity Tests / KW: non-normality / KW: Anderson-Darling Test /
Requires: Stata version 11 / Distribution-Date: 20140514 / Author: Emad
lmnad2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMNAD2': Module to Compute 2SLS-IV Non Normality Anderson-Darling Test /
lmnad2 computes 2SLS-IV Non Normality Anderson-Darling Test / KW:
Regression / KW: 2SLS / KW: LIML / KW: MELO / KW: GMM / KW: K-CLASS / KW:
Two-Stage Least Squares (2SLS) / KW: Limited-Information Maximum
lmnadnl from http://fmwww.bc.edu/RePEc/bocode/l
'LMNADNL': Module to Compute NLS Non Normality Anderson-Darling Test /
lmnadnl computes NLS Non Normality Anderson-Darling Test / KW: Regression
/ KW: NLS / KW: Non Normality / KW: Anderson-Darling Test / Requires:
Stata version 11.2 / Distribution-Date: 20151016 / Author: Emad Abd
lmnadxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMNADXT': module to compute Panel Data Non Normality Anderson-Darling
Test / lmnadxt computes Panel Data Non Normality Anderson-Darling Test /
KW: Regression / KW: Panel / KW: Cross Section-Time Series / KW: Non
Normality / KW: Anderson-Darling Z Test / Requires: Stata version 11 /
lmndh from http://fmwww.bc.edu/RePEc/bocode/l
'LMNDH': Module to Compute OLS Non Normality Doornik-Hansen Test / lmndh
computes OLS Non Normality Doornik-Hansen Test / KW: regression / KW:
Heteroscedasticity Tests / KW: non-normality / KW: Doornik-Hansen Test /
Requires: Stata version 11 / Distribution-Date: 20140514 / Author: Emad
lmndp from http://fmwww.bc.edu/RePEc/bocode/l
'LMNDP': module to Compute OLS Non Normality D'Agostino-Pearson Test /
lmndp Module to Compute OLS Non Normality D'Agostino-Pearson / Test / KW:
Regression / KW: OLS / KW: Non Normality / KW: D'Agostino-Pearson Test /
Requires: Stata version 11 / Distribution-Date: 20131119 / Author: Emad
lmndp2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMNDP2': module to compute 2SLS-IV Non Normality D'Agostino-Pearson Test
/ lmndp2 computes 2SLS-IV Non Normality D'Agostino-Pearson Test / KW:
Regression / KW: 2SLS / KW: LIML / KW: MELO / KW: GMM / KW: K-CLASS /
KW: Two-Stage Least Squares (2SLS) / KW: Limited-Information Maximum
lmngr from http://fmwww.bc.edu/RePEc/bocode/l
'LMNGR': module to compute Jarque-Bera Non Normality Lagrange Multiplier
Runs Test for Residuals after OLS Regression / lmadurh computes
Jarque-Bera Non Normality Lagrange Multiplier / Runs Test for Residuals
after OLS Regression / KW: Jarque-Bera / KW: normality / KW: Lagrange
lmngry from http://fmwww.bc.edu/RePEc/bocode/l
'LMNGRY': module to compute Geary Non Normality Lagrange Multiplier Runs
Test / lmngry computes Geary Non Normality Lagrange Multiplier Runs / Test
for Residuals after OLS Regression / KW: normality / KW: regression / KW:
OLS / KW: Lagrange Multiplier / KW: Geary LM Runs Test / Requires: Stata
lmngry2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMNGRY2': Module to Compute 2SLS-IV Non Normality Geary Runs Test /
lmngry2 computes 2SLS-IV Non Normality Geary Runs Test / KW: Regression /
KW: 2SLS / KW: LIML / KW: MELO / KW: GMM / KW: K-CLASS / KW: Two-Stage
Least Squares (2SLS) / KW: Limited-Information Maximum Likelihood (LIML) /
lmngryxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMNGRYXT': module to compute Panel Data Non Normality Geary Runs Test /
lmngryxt computes Panel Data Non Normality Geary Runs Test / KW:
Regression / KW: Panel Data / KW: Cross Sections-Time Series / KW: Non
Normality / KW: Geary LM Test / KW: Runs Test / Requires: Stata version
lmnjb from http://fmwww.bc.edu/RePEc/bocode/l
'LMNJB': module to compute Lagrange Multiplier LM Jarque-Bera Normality
Test / lmnjb computes Lagrange Multiplier Jarque-Bera normality test / for
OLS residuals after regression. / KW: normality / KW: regression / KW:
OLS / KW: Lagrange Multiplier / Requires: Stata version 10 /
lmnjbxt from http://fmwww.bc.edu/RePEc/bocode/l
'LMNJBXT': Module to Compute Panel Data Non Normality Jarque-Bera Test /
lmnjbxt computes Panel Data Non Normality Jarque-Bera Test / KW:
Regression / KW: Panel / KW: Cross Sections-Time Series / KW: Non
Normality / KW: Jarque-Bera Test / Requires: Stata version 11.2 /
lmnreg3 from http://fmwww.bc.edu/RePEc/bocode/l
'LMNREG3': module to compute Overall System Non Normality Tests after
(3SLS-SURE) Regressions / lmnreg3 computes Overall System Non Normality
Tests after / (3SLS-SURE) Regressions / KW: 3SLS / KW: SURE / KW: Non
Normality / KW: Breusch-Pagan LM Test / KW: Likelihood Ratio LR Test / KW:
lmnsem from http://fmwww.bc.edu/RePEc/bocode/l
'LMNSEM': module to perform Overall System Structural Equation Modeling
(SEM) Non Normality Tests / lmhsem Computes Overall System Non Normality
Tests, after / Structural Equation Modeling (SEM) Regressions / KW: SEM /
KW: Structural Equation Modeling / KW: Overall System Non Normality Tests
lmnwhite from http://fmwww.bc.edu/RePEc/bocode/l
'LMNWHITE': module to Compute White Non Normality Lagrange Multiplier Test
after OLS Regression / lmnwhite Computes White Non Normality Lagrange
Multiplier Test / after OLS Regression / KW: normality / KW: regression /
KW: Lagrange multiplier / KW: White / Requires: Stata version 10 /
lmnwhite2 from http://fmwww.bc.edu/RePEc/bocode/l
'LMNWHITE2': Module to Compute 2SLS-IV White IM Non Normality Test /
lmnwhite2 computes 2SLS-IV White LM Non Normality Test / KW: Regression /
KW: 2SLS / KW: LIML / KW: MELO / KW: GMM / KW: K-CLASS / KW: Two-Stage
Least Squares (2SLS) / KW: Limited-Information Maximum Likelihood (LIML) /
lmnwhitext from http://fmwww.bc.edu/RePEc/bocode/l
'LMNWHITEXT': module to compute Panel Data Non Normality White Test /
lmabxt computes Panel Data Non Normality White Test / KW: Regression /
KW: Panel / KW: Cross Section-Time Series / KW: Non Normality / KW: White
IM Test / Requires: Stata version 11 / Distribution-Date: 20130811 /
lms from http://fmwww.bc.edu/RePEc/bocode/l
'LMS': module to perform least median squares regression fit / lms fits a
least median squares regression of varlist on depvar. / Least Median
Squares is a robust fitting approach which / attempts to minimize the
median squared residual of the / regression (equivalent to minimizing the
lmsrd from http://fmwww.bc.edu/RePEc/bocode/l
'LMSRD': module to compute Spurious Regression Diagnostic after OLS
Regression / lmsrd Computes Spurious Regression Diagnostic after OLS /
Regression / KW: spurious regression / KW: Engle / KW: Granger / KW: OLS /
Requires: Stata version 10.0 / Distribution-Date: 20120618 / Author: Emad
lnmor from http://fmwww.bc.edu/RePEc/bocode/l
'LNMOR': module to compute marginal odds ratios after model estimation /
lnmor is a post-estimation command to compute (adjusted) / marginal odds
ratios after logit or probit using G-computation. / By default, lnmor
obtains marginal ORs by applying fractional / logit to averaged
localp from http://fmwww.bc.edu/RePEc/bocode/l
'LOCALP': module for kernel-weighted local polynomial smoothing / localp
is a customised version of lpoly, smoothing yvar as a / function of xvar.
Defaults include kernel(biweight), degree(1), / bwidth() given by rounding
0.2 of the range of xvar down to a / nice number, at(xvar), ms(Oh) and
locpr from http://fmwww.bc.edu/RePEc/bocode/l
'LOCPR': module for semi-parametric estimation / locpr semi-parametrically
estimates a probability or proportion / as a function of one other
variable and graphs the result. / Specifically, it estimates a local
linear regression using lpoly / and approximates the endpoints of the
locproj from http://fmwww.bc.edu/RePEc/bocode/l
'LOCPROJ': module to estimate Local Projections / locproj estimates linear
and nonlinear Impulse Response / Functions (IRF) based on the local
projection methodology first / proposed by Jordà (2005). The procedure
allows for the easy / implementation of several options used in the
logitcprplot from http://fmwww.bc.edu/RePEc/bocode/l
'LOGITCPRPLOT': module to graph component-plus-residual plot for logistic
regression / logitcprplot can be used after logistic regression for
graphing / a component-plus-residual plot (a.k.a. partial residual plot)
for / a given predictor, including a lowess, local polynomial, /
logitgraph from http://fmwww.bc.edu/RePEc/bocode/l
'LOGITGRAPH': module to produce Graph of the Probabilities from a Logistic
Regression Model / logitgraph is a chart that horizontally displays the /
probabilities and their confidence intervals for all / combinations of
categories of each independent variable in a / logistic regression model.
logithetm from http://fmwww.bc.edu/RePEc/bocode/l
'LOGITHETM': module to estimate Logit Multiplicative Heteroscedasticity
Regression / logithetm fits MLE for Logit Multiplicative
Heteroscedasticity / Regression / KW: logit / KW: heteroskedasticity /
Requires: Stata version 10 / Distribution-Date: 20111027 / Author: Emad
logitjack from http://fmwww.bc.edu/RePEc/bocode/l
'LOGITJACK': module to provide cluster robust inference for logit models /
logitjack is a stand-alone command that calculates a / linearized version
of the wild cluster bootstrap for logit / models. It will calculate a CV1
variance estimate, with / p-values calculated using the t(G-1)
logittorisk from http://fmwww.bc.edu/RePEc/bocode/l
'LOGITTORISK': module for conversion of logistic regression output to
differences and ratios of risk / logittorisk computes the
exposure/intervention group risk (r1) / and a table of differences and
ratios of risk from the baseline / odds (constant) and odds ratio (from
logpred from http://fmwww.bc.edu/RePEc/bocode/l
'LOGPRED': module to calculate logistic regression probabilities / logpred
calculates and prints probabilities and 95% confidence / intervals from
logistic regression estimates for a continuous X / variable, adjusted for
covariates. Default prints probabilities / and confidence intervals;
logtest from http://fmwww.bc.edu/RePEc/bocode/l
'LOGTEST': module to test significance of a predictor in logistic models /
There exist a few ways (e.g. Wald test) of testing the / statistical
significance of a predictor in logistic models. The / likelihood ratio
(LR) test used for comparing two models is / considered as a better
looclass from http://fmwww.bc.edu/RePEc/bocode/l
'LOOCLASS': module for generating classification statistics of
Leave-One-Out cross-validation for binary outcomes / looclass performs
leave-one-out cross-validation for regression / and machine learning
models with a binary outcome and then / produces classification measures
lpdensity from http://fmwww.bc.edu/RePEc/bocode/l
'LPDENSITY': module to perform Local Polynomial Density Estimation and
Inference / lpdensity implements the local polynomial regression based /
density (and derivatives) estimator proposed in Cattaneo, / Jansson and Ma
(2020). Robust bias-corrected inference, both / pointwise (confidence
lpdid from http://fmwww.bc.edu/RePEc/bocode/l
'LPDID': module implementing Local Projections Difference-in-Differences
(LP-DiD) estimator / LPDID performs the Local Projections
Difference-in-Differences / estimator (LP-DiD) proposed by Dube, Girardi,
Jordà and Taylor / (2023). LP-DiD is a convenient and flexible
lppinv from http://fmwww.bc.edu/RePEc/bocode/l
'LPPINV': module providing a non-iterated general implementation of the
LPLS estimator for cOLS, TM, and custom cases / The program implements the
LPLS (linear programming through / least squares) estimator with the help
of the Moore-Penrose / inverse (pseudoinverse), calculated using singular
lprplot from http://fmwww.bc.edu/RePEc/bocode/l
'LPRPLOT': module to produce logistic regression partial residual plots /
lprplot produces a partial residual plot after logistic / regression. The
plot is computed as described in Landwehr, / Pregibon, and Shoemaker
(1984). Warning: lprplot is / computationally intensive and may take a
lrchg from http://fmwww.bc.edu/RePEc/bocode/l
'LRCHG': module to calculate change in coefficients between logistic
models / lrchg displays the coefficients in two logistic models, and /
calculates the proportion of change in coefficients between two / models.
This is useful in logistic regression modelling in / epidemiology. /
lrmatx from http://fmwww.bc.edu/RePEc/bocode/l
'LRMATX': module to make logistic regression estimates available / lrmatx
must be run after a logistic regression. It stores the / output that you
see from a logistic regression as easily / accessible matrices. It is
intended for use during model / building. By making coefficients
lrplot from http://fmwww.bc.edu/RePEc/bocode/l
'LRPLOT': module to plot coefficients from a logistic regression / Plots
the coefficients from a logistic regression with confidence / intervals,
on a log scale. Simplest use: do lrplot immediately / after a logistic
regression. / Author: Jan Brogger, University of Bergen, Norway /
lrutil from http://fmwww.bc.edu/RePEc/bocode/l
'LRUTIL': modules providing utilities for logistic regression / This
module contains several utilities for logistic regression / which may be
loaded as a single package via lrutil. / Author: Jan Brogger, University
of Bergen, Norway / Support: email jan.brogger@med.uib.no /
madfuller from http://fmwww.bc.edu/RePEc/bocode/m
'MADFULLER': module to perform Dickey-Fuller test on panel data /
madfuller performs the multivariate augmented Dickey-Fuller panel / unit
root test (Sarno and Taylor, 1998; Taylor and Sarno, 1998) / on a variable
that contains both cross-section and time-series / components. The test
magreg from http://fmwww.bc.edu/RePEc/bocode/m
'MAGREG': module to calculate maximum agreement regression / magreg
estimates the maximum agreement regression model / specified in varlist.
/ KW: regression / KW: maximum agreement / Requires: Stata version 9 /
Distribution-Date: 20230915 / Author: Matteo Bottai, Institute of
manski_ci from http://fmwww.bc.edu/RePEc/bocode/m
'MANSKI_CI': module to use Manski type bounds (Manski 2003) to calculate
confidence intervals around a treatment variable's regression coefficient
in a (covariate-adjusted) regression / manski_ci is designed for use in
the context of randomized / controlled trials (RCTs) with missing outcomes
mapsm from http://fmwww.bc.edu/RePEc/bocode/m
'MAPSM': module to perform multiple arms propensity score matching / The
mapsm command; stands for “Multiple Arms Propensity Score / Matching”,
matches the propensity scores predicted from / binary logistic regression
(or multinomial logistic regression), / which indicates the likeliness of
margdistfit from http://fmwww.bc.edu/RePEc/bocode/m
'MARGDISTFIT': module to check the distributional assumptions underlying a
parametric regression model / margdistfit checks the distributional
assumptions underlying a / parametric regression model by displaying a
graph that compares / the distribution of dependent variable with the
marginscontplot2 from http://fmwww.bc.edu/RePEc/bocode/m
'MARGINSCONTPLOT2': module to graph margins for continuous predictors /
marginscontplot2 provides a graph of the marginal effect of a / continuous
predictor on the response variable in the most / recently fitted
regression model. See Royston (Stata Journal, / 2013) for details and
marglmean from http://fmwww.bc.edu/RePEc/bocode/m
'MARGLMEAN': module to compute marginal log means from regression models /
marglmean calculates symmetric confidence intervals for / log marginal
means (also known as log scenario means), and / asymmetric confidence
intervals for the marginal means / themselves. marglmean can be used
margprev from http://fmwww.bc.edu/RePEc/bocode/m
'MARGPREV': module to compute marginal prevalences from binary regression
models / margprev calculates confidence intervals for marginal /
prevalences, also known as scenario proportions. margprev can / be used
after an estimation command whose predicted values are / interpreted as
marhis from http://fmwww.bc.edu/RePEc/bocode/m
'MARHIS': module to produce predictive margins and marginal effects plots
with histogram after regress, logit, xtmixed and mixed / marhis generates
predictive margins and marginal effects plots / with a histogram
summarizing the distribution of the variable on / the x-axis. / KW:
marker from http://fmwww.bc.edu/RePEc/bocode/m
'MARKER': module to generate indicator variable marking desired sample /
marker creates a 0-1 variable markvar, such that markvar has / value 1 if
all values are present (not missing) for every / variable in varlist and
have sensible non-zero weights if / weights are specified and satisfy any
mbitobit from http://fmwww.bc.edu/RePEc/bocode/m
'MBITOBIT': module to fit bivariate Tobit regression / mbitobit fits a
maximum-likelihood two-equation tobit models, / for variables that are
left censored at 0. It contains / modules for easy estimation of
predicted values (predict) / and marginal effects (margins) for most
mcl from http://fmwww.bc.edu/RePEc/bocode/m
'MCL': module to estimate multinomial conditional logit models / MCL
stands for Multinomial Conditional Logit, a term coined by / Breen (1994).
An MCL model uses a conditional logit program to / estimate a multinomial
logistic model. This produces the same log / likelihood, estimates and
mcmccqreg from http://fmwww.bc.edu/RePEc/bocode/m
'MCMCCQREG': module to perform simulation assisted estimation of censored
quantile regression using adaptive Markov chain Monte Carlo / mcmccqreg
can be used to "fit" Powell's (1984, 1986) censored / quantile regression
model(s) using adaptive Markov chain Monte / Carlo simulation. More
mcmclinear from http://fmwww.bc.edu/RePEc/bocode/m
'MCMCLINEAR': module for MCMC sampling of linear models / This package
provides commands for Markov chain Monte Carlo / (MCMC) sampling from the
posterior distribution of linear models. / Two models are provided in this
version: a normal linear / regression model (the Bayesian equivalent of
mcqscore from http://fmwww.bc.edu/RePEc/bocode/m
'MCQSCORE': module to score the Monetary Choice Questionnaire using
logistic regression / MCQScore scores the Monetary Choice Questionnaire
(questions in / standard order), which uses a hyperbolic decay function to
/ summarize the degree to which time discounts the value of a / delayed
medsurv from http://fmwww.bc.edu/RePEc/bocode/m
'MEDSURV': module to calculate the median survival time after Cox/Poisson
regression / This program calculates the median survival time after a /
Cox/Poisson model. It is able to handle / multiple-record-per-subject data
with time-varying covariates, / and produce distinct predicted median
meloreg2 from http://fmwww.bc.edu/RePEc/bocode/m
'MELOREG2': module to perform Minimum Expected Loss (MELO) Instrumental
Variables Regression / meloreg2 performs Minimum Expected Loss (MELO)
Instrumental / Variables Regression / KW: regression / KW: Minimum
Expected Loss / KW: MELO / KW: Instrumental Variables / Requires: Stata
meoprobit from http://fmwww.bc.edu/RePEc/bocode/m
'MEOPROBIT': module to compute marginal effects after estimation of
ordered probit / -meoprobit- computes marginal effects at means and their
standard / errors after the estimation of an ordered probit model. The
mean / values are those of the estimation sample or of a sub-goup of the /
meresc from http://fmwww.bc.edu/RePEc/bocode/m
'MERESC': module to rescale the results of mixed nonlinear probability
models / meresc rescales the results of mixed nonlinear probability /
models such as xtmelogit, xtlogit, or xtprobit to the same scale / as the
intercept-only model. This allows to compare regression / coefficients or
merlin from http://fmwww.bc.edu/RePEc/bocode/m
'MERLIN': module to fit mixed effects regression for linear and non-linear
models / merlin fits linear, non-linear and user-defined mixed effects /
regression models. merlin can fit multivariate outcome models of / any
type, each of which could be repeatedly measured / (longitudinal), with
meta_analysis from http://fmwww.bc.edu/RePEc/bocode/m
'META_ANALYSIS': module to perform subgroup and regression-type fixed- and
random-effects meta-analyses / The meta_analysis module includes four
commands: masum, maanova, / mareg, and maforest. The first three perform
an overall / meta-analysis, a subgroup or categorical moderator analysis,
metabias from http://fmwww.bc.edu/RePEc/bocode/m
'METABIAS': module to test for small-study effects in meta-analysis /
metabias performs several statistical tests for funnel-plot / asymmetry in
meta-analysis and optionally plots associated / graphs. As there are
several possible sources of funnel-plot / asymmetry, these tests assess
metadta from http://fmwww.bc.edu/RePEc/bocode/m
'METADTA': module to perform fixed- and random-effects meta-analysis and
meta-regression of diagnostic accuracy studies / metadta is a routine that
performs meta-analytical pooling of / diagnostic accuracy data from
separate studies with similar / methodology and epidemiology. The routine
metagen from http://fmwww.bc.edu/RePEc/bocode/m
'METAGEN': module to perform meta-analysis of genetic-association studies
/ metagen performs fixed-, and random-effects meta-analysis of / genetic
association case-control studies using Individual Patient / Data (IPD).
metagen performs meta-analysis using fixed- and / random-effects logistic
metandi from http://fmwww.bc.edu/RePEc/bocode/m
'METANDI': module to perform meta-analysis of diagnostic accuracy /
metandi performs meta-analysis of diagnostic test accuracy / studies in
which both the index test under study and the / reference test (gold
standard) are dichotomous. It fits a / two-level mixed logistic regression
metapred from http://fmwww.bc.edu/RePEc/bocode/m
'METAPRED': module producing outlier and influence diagnostics for
meta-analysis / metapred extends the currently available post-estimation /
predictions for meta regress to include standardized residuals, /
studentized residuals, DFITS, Cook's distance, covariate ratio / and
metapreg from http://fmwww.bc.edu/RePEc/bocode/m
'METAPREG': module to compute fixed and random effects meta-analysis and
meta-regression of proportions / This routine provides procedures for
pooling proportions in a / meta-analysis of multiple studies study and/or
displays the / results in a forest plot. The pooled estimates are a
metaprop_one from http://fmwww.bc.edu/RePEc/bocode/m
'METAPROP_ONE': module to perform fixed and random effects meta-analysis
of proportions / This routine provides procedures for pooling proportions
in a / meta-analysis of multiple studies study and/or displays the /
results in a forest plot. The pooled estimate is obtained as a / weighted
metareg from http://fmwww.bc.edu/RePEc/bocode/m
'METAREG': module to perform meta-analysis regression / metareg performs
random-effects meta-regression on study-level / summary data. This is a
revised version of the program / originally written by Stephen Sharp
(STB-42, sbe23). \xa0The major / revisions involve improvements to the
metatrend from http://fmwww.bc.edu/RePEc/bocode/m
'METATREND': module to implement regression methods for detecting trends
in cumulative meta-analysis / metatrend performs a cumulative
meta-analysis (Lau et al, 1995) / using the DerSimonian and Laird
random-effects method and / afterwards, performs two tests for assesing
mhtreg from http://fmwww.bc.edu/RePEc/bocode/m
'MHTREG': module for multiple hypothesis testing controlling for FWER /
mhtreg is a module for multiple hypothesis testing that / asymptotically
controls familywise error rate and is / asymptotically balanced. It is
based on List et al. (Experimental / Economics, 2019) but modified to be
mi_impute_from from http://fmwww.bc.edu/RePEc/bocode/m
'MI_IMPUTE_FROM': module to impute using an external imputation model / mi
impute from fills in missing values using an estimated / imputation model
estimated in one or multiple studies. A / quantitative missing variable
can be imputed passing the / estimates of 99 (q=0.01(.01).99) linear
mi_mvncat from http://fmwww.bc.edu/RePEc/bocode/m
'MI_MVNCAT': module to assign "final" values to (mvn) imputed categorical
variables / mi mvncat assigns "final" values to multiple imputed
categorical / variables, using the procedure described by Allison
(2002:40). / Categorical variables with k levels are supposed to be /
mibmi from http://fmwww.bc.edu/RePEc/bocode/m
'MIBMI': module for cleaning and multiple imputation algorithm for body
mass index (BMI) in longitudinal datasets / mibmi is a multiple imputation
and cleaning command for body / mass index (BMI), compatible with {cmd:mi}
commands. Cleaning / includes standard cleaning that limits values to a
midas from http://fmwww.bc.edu/RePEc/bocode/m
'MIDAS': module for meta-analytical integration of diagnostic test
accuracy studies / midas is a user-written command for idiot-proof
implementation of / some of the contemporary statistical methods for
meta-analysis / of binary diagnostic test accuracy. Primary data synthesis
mimix from http://fmwww.bc.edu/RePEc/bocode/m
'MIMIX': module to perform reference based multiple imputation for
sensitivity analysis of longitudinal clinical trials with protocol
deviation / mimix imputes missing numerical outcomes for a longitudinal /
trial with protocol deviation under distinct reference group / (typically
mira from http://fmwww.bc.edu/RePEc/bocode/m
'MIRA': module to compute Rubin's measure for multiple imputation
regression analysis / mira computes Rubin's (1987) measures for Multiple
Imputation / (MI) regression analysis using numbered datasets. For
example, / pretend that you have the following datasets generated by some
mivif from http://fmwww.bc.edu/RePEc/bocode/m
'MIVIF': module to calculate variance inflation factors after mi estimate
regress / mivif calculates variance inflation factors for the independent
/ variables after mi estimate regress. The program executes . mi / xeq :
regress_cmd ; estat vif VIFs are calculated separately for / each
mkern from http://fmwww.bc.edu/RePEc/bocode/m
'MKERN': module to perform multivariate nonparametric kernel regression /
mkern extimates a multivariate nonparametric local kernel / regression, by
a "radial" local mean or local linear approach / using various Kernel
functions as weighting schemes (at user's / choice). Using the companion
mlogitroc from http://fmwww.bc.edu/RePEc/bocode/m
'MLOGITROC': module to calculate multiclass ROC Curves and AUC from
Multinomial Logistic Regression / mlogitroc generates multiclass ROC
curves for classification / accuracy based on multinomial logistic
regression using mlogit. / The algorithm begins by running mlogit B=100
mlowess from http://fmwww.bc.edu/RePEc/bocode/m
'MLOWESS': module for lowess smoothing with multiple predictors / mlowess
computes lowess smooths of a response on specified / predictors
simultaneously; that is, each smooth is adjusted for / the others. Fitted
values may be saved in new variables. By / default, adjusted values of the
mlt from http://fmwww.bc.edu/RePEc/bocode/m
'MLT': module to provide multilevel tools / The mlt package contains some
postestimation commands for / hierarchical mixed models (xtmixed,
xtmelogit and xtmepoisson) / and some other tools useful for typical tasks
in multilevel / modelling. mltrsq computes the Bosker/Snijders and /
mmqreg from http://fmwww.bc.edu/RePEc/bocode/m
'MMQREG': module to estimate quantile regressions via Method of Moments /
mmqreg estimates quantile regressions using the method of / moments as
proposed by Machado and Santos Silva (J. / Econometrics, 2019). In
contrast with xtqreg, this command allows / for the estimation of quantile
mmsel from http://fmwww.bc.edu/RePEc/bocode/m
'MMSEL': module to simulate (counterfactual) distributions from quantile
regressions (w/optional sample selection correction) / Simulates
(counterfactual) distributions from quantile / regressions. Based on
Machado and Mata (2005). An option to / correct for sample selection has
modeldiag from http://fmwww.bc.edu/RePEc/bocode/m
'MODELDIAG': module to generate graphics after regression / modeldiag is a
set of graphics programs to run after fitting a / regression-type command.
Programs are written for Stata 8, / except that in most cases a previous
version written for Stata / 7 is also included here. Numbering conventions
modlpr from http://fmwww.bc.edu/RePEc/bocode/m
'MODLPR': module to estimate long memory in a timeseries / modlpr computes
a modified form of the Geweke/Porter-Hudak (GPH, / 1983) estimate of the
long memory (fractional integration) / parameter, d, of a timeseries,
proposed by Phillips (1999a, / 1999b). Distinguishing unit-root behavior
more_clarify from http://fmwww.bc.edu/RePEc/bocode/m
'MORE_CLARIFY': module to estimate quantities of interest through
simulation and resampling methods / moreClarify is a new Stata
implementation of a simulation-based / approach for transforming the raw
output of statistical models / (e.g., regression coefficients) into
movestay from http://fmwww.bc.edu/RePEc/bocode/m
'MOVESTAY': module for maximum likelihood estimation of endogenous
regression switching models / This is an update of -movestay- as published
in SJ5-3 (st0071_2), / SJ5-1 (st0071_1) and SJ4-3 (st0071). / KW:
switching regressions / KW: endogeneity / KW: maximum likelihood /
mqgamma from http://fmwww.bc.edu/RePEc/bocode/m
'MQGAMMA': module to estimate quantiles of potential-outcome distributions
/ The -mqgamma- command estimates the quantiles of the / potential-outcome
distributions for each treatment level from / censored observational data
in which the dependent variable is / inherently positive, such as
mrgtbl2 from http://fmwww.bc.edu/RePEc/bocode/m
'MRGTBL2': module to provide a margin-based approach to table 2 from a
regression / The output from the mrgtbl2 command is based on margins from
/ the regression model: outcome = constant + / i.exposure[#i.by1][#i.by2]
+ adjustment variables. The user / specifies the regression method as one
mseffect from http://fmwww.bc.edu/RePEc/bocode/m
'MSEFFECT': module to estimate the mean effect size of (binary/multiple
group) treatment on multiple outcomes / This command is a part of the
online appendix for Lavy et al. / (NBER, 2016) "Empowering Mothers and
Enhancing Early Childhood / Investment: Effect on Adults Outcomes and
mss from http://fmwww.bc.edu/RePEc/bocode/m
'MSS': module to perform heteroskedasticity test for quantile and OLS
regressions / mss computes the Machado-Santos Silva (2000, Glejser's Test
/ Revisited, Journal of Econometrics, 97, 189-202 ) / heteroskedasticity
test for quantile and OLS regressions. / KW: OLS / KW: quantile
mswtable from http://fmwww.bc.edu/RePEc/bocode/m
'MSWTABLE': module to produce MSWord tables of descriptive statistics and
regression estimates / mswtable produces a table in MSWord format. As
input, mswtable / requires either: 1. Stata matrix; 2. Equations stored
via / estimates store. These can be augmented with an additional / matrix
mtebinary from http://fmwww.bc.edu/RePEc/bocode/m
'MTEBINARY': module to compute Marginal Treatment Effects (MTE) With a
Binary Instrument / mtebinary estimates the marginal treatment effect
(MTE) function / using a binary instrument and a binary endogenous
variable. The / MTE is defined as the difference between the potential
mtemore from http://fmwww.bc.edu/RePEc/bocode/m
'MTEMORE': module to compute Marginal Treatment Effects (MTE) With a
Binary Instrument / mtemore is the old version of the command mtebinary.
mtemore / was designed to produce results from Kowalski (NBER 22363,
2016), / and mtebinary was designed to produce results from Kowalski (NBER
mtnardl from http://fmwww.bc.edu/RePEc/bocode/m
'MTNARDL': module to perform Bootstrap Multiple Threshold Nonlinear ARDL /
mtnardl implements the Multiple Threshold Nonlinear / Autoregressive
Distributed Lag (MTNARDL) model proposed by Pal / and Mitra (2016).
Unlike the standard Nonlinear ARDL (NARDL) / which decomposes changes into
mulogit from http://fmwww.bc.edu/RePEc/bocode/m
'MULOGIT': module to calculate multivariate and univariate odds ratios in
logistic regression / When using (unconditional) binary logistic
regression modeling, / the influence of confounders and nuisance
parameters on a / specific risk factor or treatment requires a comparison
multicoefplot from http://fmwww.bc.edu/RePEc/bocode/m
'MULTICOEFPLOT': module to produce advanced repeated cross-section
graphical analysis / multicoefplot runs regressions and generates graphs
for / repeated cross-section analysis, with extensive options for /
multiple specifications comparison, and specification and sample /
multisite from http://fmwww.bc.edu/RePEc/bocode/m
'MULTISITE': module to install the four components of the multisite
poackage / This command installs the four components of the multisite /
poackage. / KW: regression / KW: randomized trials / KW: ITT / Requires:
Stata version 12 / Distribution-Date: 20250228 / Author: Clement de
multisite_regitt from http://fmwww.bc.edu/RePEc/bocode/m
'MULTISITE_REGITT': module to compute the coefficients from a regression
of site-level ITTs on site-level characteristics in multi-site randomized
trials / This command computes the coefficients from a regression of /
site-level ITTs on site-level characteristics in multi-site / randomized
multisite_reglate from http://fmwww.bc.edu/RePEc/bocode/m
'MULTISITE_REGLATE': module to Estimates the sign of univariate regression
coefficients from regressions of site-level LATEs on site-level
characteristics in multi-site randomized trials studied in de Chaisemartin
& Deeb (2024) / This command estimates the sign of coefficients from
mundlak from http://fmwww.bc.edu/RePEc/bocode/m
'MUNDLAK': module to estimate random-effects regressions adding
group-means of independent variables to the model / The command mundlak
estimates random-effects regression models / (xtreg, re) adding
group-means of variables in indepvars which / vary within groups. This
mvardlurt from http://fmwww.bc.edu/RePEc/bocode/m
'MVARDLURT': module to perform Multivariate ARDL Unit Root Test with
Bootstrap Critical Values / mvardlurt implements the multivariate ARDL
unit root test / proposed by Sam, McNown, Goh, and Goh (2024). This test /
augments the standard ADF regression with the lagged level of a /
mvmeta from http://fmwww.bc.edu/RePEc/bocode/m
'MVMETA': module to perform multivariate random-effects meta-analysis /
mvmeta performs multivariate random-effects meta-analysis and /
multivariate random-effects meta-regression on a data-set of / point
estimates, variances and (optionally) covariances. It is / an essential
mvplot from http://fmwww.bc.edu/RePEc/bocode/m
'MVPLOT': module to plot results from multiverse analysis / mvplot
visualizes results from multiverse analysis. The / command produces a plot
with two panels: an upper panel / displaying the multiverse distribution
of the target / coefficient as a density function, and a lower panel
mvprobit from http://fmwww.bc.edu/RePEc/bocode/m
'MVPROBIT': module to calculate multivariate probit regression using
simulated maximum likelihood / mvprobit estimates M-equation probit
models, by the method of / simulated maximum likelihood (SML). (Cf. probit
and biprobit / which estimate 1-equation and 2-equation probit models by
mvsamp1i from http://fmwww.bc.edu/RePEc/bocode/m
'MVSAMP1I': module to determine sample size and power for multivariate
regression / mvsamp1i estimates required sample size or power of tests for
/ multivariate F tests derived from Wilks' lambda. If n() is / specified,
mvsamp1i computes power; otherwise, it computes / sample size. mvsamp1i is
mvsampsi from http://fmwww.bc.edu/RePEc/bocode/m
'MVSAMPSI': module to determine sample size and power for multivariate
regression / mvsamp1i estimates required sample size or power of tests for
/ multivariate F tests derived from Wilks' lambda. If n() is / specified,
mvsamp1i computes power; otherwise, it computes / sample size. mvsamp1i is
mvtest from http://fmwww.bc.edu/RePEc/bocode/m
'MVTEST': module to perform multivariate F tests / mvtest tests linear
hypotheses about the estimated parameters / from the most recently
estimated multivariate regression using / Wilks' lambda, Pillai's trace
and Hotelling-Lawley's trace. An / optional transformation matrix to be
myreg2 from http://fmwww.bc.edu/RePEc/bocode/m
'MYREG2': module to export results of regression models to a word document
/ myreg2 exports regression results to a Word document after the /
collection command in Stata 17. It is designed for creating / tables
formatted like Table 2 in epidemiological journals. The / command
nbinreg from http://fmwww.bc.edu/RePEc/bocode/n
'NBINREG': module to estimate negative binomial regression models / Here
is the first version of a maximum liklihood negative / binomial with
cluster, robust, and score options. Initial values / are calculated my a
call to poisson. Two scores are produced: 1) / the normal B-based scores,
nbstrat from http://fmwww.bc.edu/RePEc/bocode/n
'NBSTRAT': module to estimate Negative Binomial with Endogenous
Stratification / nbstrat fits a maximum-likelihood negative binomial with
/ endogenous stratification regression model of depvar on / indepvars,
where depvar is a nonnegative count variable > 0. / lnalpha is
netreg from http://fmwww.bc.edu/RePEc/bocode/n
'NETREG': module to perform linear regression of a network response with
the exchangeable assumption / netreg provides a method for performing a
regression of a / network response, where each data point represents an
edge on a / network or covariates of interest. It takes advantage of the /
next from http://fmwww.bc.edu/RePEc/bocode/n
'NEXT': module to perform regression discontinuity / This program, which
is designed to estimate a local average / treatment effect in the context
of a strict regression / discontinuity design, uses a data-driven
algorithm that / simultaneously selects the polynomial specification and
niceest from http://fmwww.bc.edu/RePEc/bocode/n
'NICEEST': module to export regression table to excel / niceest relies on
parmest to export regression results into / a formatted Excel-file. As
input niceest takes the most recently / executed regression analysis and
as ouput creates an excel-file / with labeled regression coefficients,
nlcheck from http://fmwww.bc.edu/RePEc/bocode/n
'NLCHECK': module to check linearity assumption after model estimation /
nlcheck is a simple diagnostic tool that can be used after / fitting a
model to quickly check the linearity assumption for a / given predictor.
nlcheck categorizes the predictor into bins, / refits the model including
nlls from http://fmwww.bc.edu/RePEc/bocode/n
'NLLS': module to compute non-negative least squares in Stata / nnls is a
command implementing non-negative least squares / (NNLS) in Stata on top
of Python using the Python function / "nnls()". NNLS is a type of
constrained least squares problem / where the coefficients are not allowed
nnls from http://fmwww.bc.edu/RePEc/bocode/n
'NNLS': module to compute non-negative least squares / nnls is a command
implementing non-negative least squares / (NNLS) in Stata on top of Python
using the Python function / "nnls()". NNLS is a type of constrained least
squares problem / where the coefficients are not allowed to become
npeivreg from http://fmwww.bc.edu/RePEc/bocode/n
'NPEIVREG': module for estimation of nonparametric errors-in-variables
(EIV) regression and construction of its uniform confidence band /
npeivreg executes estimation of nonparametric / errors-in-variables (EIV)
regression and construction of its / uniform confidence band based on Kato
npiv from http://fmwww.bc.edu/RePEc/bocode/n
'NPIV': module to perform Nonparametric instrumental-variable regression
on a scalar endogenous regressor / This package implements nonparametric
instrumental variable / (NPIV) estimation methods without and with a
cross-validated / choice of tuning parameters, respectively. Both
number_exporting from http://fmwww.bc.edu/RePEc/bocode/n
'NUMBER_EXPORTING': module to format a numeric value and export in LaTeX
format / number_exporting formats a given numeric value (numeric_value) /
based on user specifications. It outputs the formatted number / into a
LaTeX compatible .tex file that can then called directly / into the paper.
ocmt from http://fmwww.bc.edu/RePEc/bocode/o
'OCMT': module to perform multiple testing approach in high-dimensional
linear regression / ocmt implements "A One Covariate at a Time, Multiple
Testing / Approach to Variable Selection in High-Dimensional Linear /
Regression Models" based on Chudik, Kapetanios and Pesaran /
oddsrisk from http://fmwww.bc.edu/RePEc/bocode/o
'ODDSRISK': module to convert Logistic Odds Ratios to Risk Ratios /
oddsrisk converts logistic regression odds ratios to relative / risk
ratios. When the incidence of an outcome is common in the / study
population; i.e. greater than 10%, the logistic regression / odds ratio no
oga from http://fmwww.bc.edu/RePEc/bocode/o
'OGA': module to perform estimation and inference for high-dimensional
regressions without imposing the sparsity restriction / oga performs
estimation and inference for high-dimensional / regression models without
imposing a sparsity assumption, based / on the methodology of Cha, Chiang,
oglm from http://fmwww.bc.edu/RePEc/bocode/o
'OGLM': module to estimate Ordinal Generalized Linear Models / oglm
estimates Ordinal Generalized Linear Models. It supports / several link
functions, including logit, probit, complementary / log-log, log-log and
cauchit. When an ordinal regression model / incorrectly assumes that error
oglm9 from http://fmwww.bc.edu/RePEc/bocode/o
'OGLM9': module to estimate Ordinal Generalized Linear Models / oglm
estimates Ordinal Generalized Linear Models. It supports / several link
functions, including logit, probit, complementary / log-log, log-log and
cauchit. When an ordinal regression model / incorrectly assumes that error
omninorm from http://fmwww.bc.edu/RePEc/bocode/o
'OMNINORM': module to calculate omnibus test for univariate/multivariate
normality / omninorm implements an omnibus test for normality proposed by
/ Doornik and Hansen (1994), who find that the test has superior / size
and power properties when compared to many in the / literature. omninorm
oneclick from http://fmwww.bc.edu/RePEc/bocode/o
'ONECLICK': module to screen for control variables that keep the
explanatory variables at a certain level of significance / oneclick By
entering your control variables, the oneclick / command helps you to
select all true subsets of the control / variables and add them to the
onespell from http://fmwww.bc.edu/RePEc/bocode/o
'ONESPELL': module to generate single longest spell for each unit in panel
data, listwise / onespell produces a subset of a panel data set in which
all / observations on varlist are non-missing and contiguous in the / time
dimension. If a panel unit contains more than one such / subset, the
oparallel from http://fmwww.bc.edu/RePEc/bocode/o
'OPARALLEL': module providing post-estimation command for testing the
parallel regression assumption / oparallel is a post-estimation command
testing the parallel / regression assumption in a ordered logit model. By
default it / performs five tests: a likelihood ratio test, a score test, a
opl from http://fmwww.bc.edu/RePEc/bocode/o
'OPL': module for optimal policy learning and multi-action optimal policy
learning / OPL is a package for learning optimal policies from data for /
empirical welfare maximization. Specifically, OPL allows to find /
"treatment assignment rules" that maximize the overall / welfare, defined
outreg2 from http://fmwww.bc.edu/RePEc/bocode/o
'OUTREG2': module to arrange regression outputs into an illustrative table
/ outreg2 provides a fast and easy way to produce an illustrative / table
of regression outputs. The regression outputs are produced / piecemeal and
are difficult to compare without some type of / rearrangement. outreg2
outreg5 from http://fmwww.bc.edu/RePEc/bocode/o
'OUTREG5': module to format regression output for published tables / This
is a version of outreg (as published in STB-46, updated in / STB-49) for
Stata version 5. If you are using Stata version 6, / please use outreg,
which has been actively updated and extended. / outreg5 is no longer under
outsum from http://fmwww.bc.edu/RePEc/bocode/o
'OUTSUM': module to write formatted descriptive statistics to a text file
/ outsum writes means and standard deviations to an external text / file,
in much the same way outreg produces formatted regression / output, i.e.
it creates an ASCII text file with columns separated / with tab characters
outwrite from http://fmwww.bc.edu/RePEc/bocode/o
'OUTWRITE': module to consolidate multiple regressions and export the
results to a .xlsx, .xls, .csv, or .tex file / outwrite reads multiple
regressions saved with estimates store, / consolidates them into a single
table, and exports the results to / a .xlsx, .xls, .csv, or .tex file.
overid from http://fmwww.bc.edu/RePEc/bocode/o
'OVERID': module to conduct postestimation tests of overidentification /
overid computes tests of overidentifying restrictions for a / regression
estimated via instrumental variables in which the / number of instruments
exceeds the number of regressors: that is, / for an overidentified
p2ci from http://fmwww.bc.edu/RePEc/bocode/p
'P2CI': module to calculate confidence limits of a regression coefficient
from the p-value / p2ci is an immediate command to calculate the standard
error and / confidence limits of a regression coefficient when only its /
p-value is known. / KW: standard error / KW: confidence interval / KW:
pantest2 from http://fmwww.bc.edu/RePEc/bocode/p
'PANTEST2': module to perform diagnostic tests in fixed effects panel
regressions / pantest2 tests for serial correlation of residuals, for the
/ significance of fixed effects, and for the normality of / residuals.
This version requires the name of the time variable / (tis...) as the
paragr from http://fmwww.bc.edu/RePEc/bocode/p
'PARAGR': module for parallel graphing of a coefficient across different
equations / paragr provides a fast and easy way to compare a coefficient /
across different equations within an estimation. It can used to /
visualize the parallel assumption of ordered logit or the / equality of
paramed from http://fmwww.bc.edu/RePEc/bocode/p
'PARAMED': module to perform causal mediation analysis using parametric
regression models / paramed performs causal mediation analysis using
parametric / regression models. Two models are estimated: a model for the
/ mediator conditional on treatment (exposure) and covariates (if /
pariv from http://fmwww.bc.edu/RePEc/bocode/p
'PARIV': module to perform nearly-collinear robust instrumental-variables
regression / pariv fits a partitioned 2SLS regression that is more robust
to / near collinearity than existing Stata 2SLS commands. / KW:
instrumental variables / KW: robust / KW: collinearity / Requires: Stata
partpred from http://fmwww.bc.edu/RePEc/bocode/p
'PARTPRED': module to generate partial predictions / partpred calculates
partial predictions for regression / equations. Multi-equation models are
supported via the eq() / option. / KW: predictions / KW: partial /
Requires: Stata version 11.1 / Distribution-Date: 20131016 / Author: Paul
pcdid from http://fmwww.bc.edu/RePEc/bocode/p
'PCDID': module to perform principal components difference-in-differences
/ pcdid implements factor-augmented difference-in-differences / (DID)
estimation. It is useful in situations where the user / suspects that
trends may be unparallel and/or stochastic among / control and treated
pdi from http://fmwww.bc.edu/RePEc/bocode/p
'PDI': module to calculate the polytomous discrimination index (PDI) /
This program calculates the polytomous discrimination index / (PDI) which
was proposed by Calster et al. (2012). PDI extends / the binary
discrimination measure, the c-statistic or area under / the ROC curve
perturb from http://fmwww.bc.edu/RePEc/bocode/p
'PERTURB': module to evaluate collinearity and ill-conditioning / perturb
is a tool for assessing the impact of small random / changes
(perturbations) to variables on parameter estimates. It / is an
alternative for collinearity diagnostics such as vif, / collin, coldiag,
pescadf from http://fmwww.bc.edu/RePEc/bocode/p
'PESCADF': module to perform Pesaran's CADF panel unit root test in
presence of cross section dependence / pescadf runs the t-test for unit
roots in heterogenous panels / with cross-section dependence, proposed by
Pesaran (2003). / Parallel to Im, Pesaran and Shin (IPS, 2003) test, it is
pgmhaz8 from http://fmwww.bc.edu/RePEc/bocode/p
'PGMHAZ8': module to estimate discrete time (grouped data) proportional
hazards models / pgmhaz8 estimates by ML two discrete time (grouped data)
/ proportional hazards regression models, one of which incorporates / a
gamma mixture distribution to summarize unobserved individual /
piaactools from http://fmwww.bc.edu/RePEc/bocode/p
'PIAACTOOLS': module to provide PIAAC tools / The PIAAC tools package
contains three commands that facilitate / analysis of the data from the
OECD Programme for the / International Assessment of Adult Competencies
(PIAAC). These / commands allow analysis with plausible values and derive
pisareg from http://fmwww.bc.edu/RePEc/bocode/p
'PISAREG': module to perform linear regression with PISA data and
plausible values / Pisareg runs linear regression with PISA data. First
variable / listed after pisareg command is the dependent variable. You can
/ use math, scie or read as dependent variables in which case the /
pisatools from http://fmwww.bc.edu/RePEc/bocode/p
'PISATOOLS': module to facilitate analysis of the data from the PISA OECD
study / The pisatools package contains several commands that facilitate /
analysis of the data from the OECD PISA study. These commands / allow
analysis with plausible values and derive standard errors / using the BRR
plotbeta from http://fmwww.bc.edu/RePEc/bocode/p
'PLOTBETA': module to plot linear combinations of coefficients / plotbeta
computes point estimates and confidence intervals for / linear
combinations of coefficients after any estimation / command, using the
lincom command. The results are then / displayed graphically to give the
plssas from http://fmwww.bc.edu/RePEc/bocode/p
'PLSSAS': module to execute SAS partial least squares procedure (Windows
only) / saspls creates a *.sas program to run a PLS analysis, then runs /
this file in the background and the output datasets created by / SAS are
converted to *.CSV files. / KW: SAS / KW: PLS / KW: partial least squares
poi2hdfe from http://fmwww.bc.edu/RePEc/bocode/p
'POI2HDFE': module to estimate a Poisson regression with two
high-dimensional fixed effects / This command allows for the estimation of
a Poisson regression / model with two high dimensional fixed effects.
Estimation is / implemented by an iterative process using the algorithm of
poisml from http://fmwww.bc.edu/RePEc/bocode/p
'POISML': module to estimate maximum likelihood Poisson regression models
/ poisml estimates maximum likelihood Poisson regression models / using
Stata's ml method for estimation. It includes the cluster, / robust, and
score options. / Author: Joseph Hilbe, Arizona State University /
polyspline from http://fmwww.bc.edu/RePEc/bocode/p
'POLYSPLINE': module to generate sensible bases for polynomials and other
splines / The polyspline package inputs an X-variable and a list of /
reference points on the X-axis, and generates a basis of / reference
splines (one per reference point) for a polynomial / or other unrestricted
power_arima_itsa from http://fmwww.bc.edu/RePEc/bocode/p
'POWER_ARIMA_ITSA': module to compute power for an interrupted time series
intervention evaluated using ARIMA with AR(1) errors / power_arima_itsa
computes power for a single-group interrupted / time series analysis
(ITSA) that will ultimately be evaluated / using an Autoregressive
power_itsa from http://fmwww.bc.edu/RePEc/bocode/p
'POWER_ITSA': module to compute power for single and multiple-group
interrupted time series analysis / Power_itsa computes power for a
specified number of time periods / n() in a single-group or multiple-group
interrupted time series / analysis (ITSA), using simulation. For a
power_step from http://fmwww.bc.edu/RePEc/bocode/p
'POWER_STEP': module to compute power for a step intervention with AR(1)
Error / power_step computes power for an interrupted time series /
analysis (ITSA) in which the intervention is expected to change / the
level (step) of the series (McLeod and Vingilis 2008). The / results are
power_tworates_zhu from http://fmwww.bc.edu/RePEc/bocode/p
'POWER_TWORATES_ZHU': module to calculate sample size or power for a
two-sample test of rates / This routine assumes analysis is by negative
binomial / regression: Zhu & Lakkis (2014) / KW: power tworates_zhu / KW:
tworates / KW: rates / KW: power / KW: power_cmd_tworates_zhu / Requires:
powersim from http://fmwww.bc.edu/RePEc/bocode/p
'POWERSIM': module for simulation-based power analysis for linear and
generalized linear models / powersim exploits the flexibility of a
simulation-based / approach to the analysis of statistical power by
providing a / facility for automated power simulations in the context of
ppml from http://fmwww.bc.edu/RePEc/bocode/p
'PPML': module to perform Poisson pseudo-maximum likelihood estimation /
ppml estimates Poisson regression by pseudo maximum likelihood. / It
differs from Stata's poisson command because it uses the / method of
Santos Silva and Tenreyro (Santos Silva, J.M.C. and / Tenreyro, S., 2010,
ppml_fe_bias from http://fmwww.bc.edu/RePEc/bocode/p
'PPML_FE_BIAS': module to provide bias corrections for Poisson
Pseudo-Maximum Likelihood (PPML) gravity models with two-way and three-way
fixed effects / ppml_fe_bias implements analytical bias corrections
described in / Weidner & Zylkin (2020) for PPML "gravity" regressions with
ppmlhdfe from http://fmwww.bc.edu/RePEc/bocode/p
'PPMLHDFE': module for Poisson pseudo-likelihood regression with multiple
levels of fixed effects / ppmlhdfe implements Poisson pseudo-maximum
likelihood / regressions (PPML) with multi-way fixed effects, as described
/ by Correia, Guimarães, Zylkin (arXiv:1903.01690). The estimator /
predcalc from http://fmwww.bc.edu/RePEc/bocode/p
'PREDCALC': module to calculate out-of-sample predictions for regression,
logistic / predcalc calculates predicted values and confidence intervals /
from linear or logistic regression model estimates for user / specified
values for the X variables. / KW: regression / KW: logistic / KW:
predxcat from http://fmwww.bc.edu/RePEc/bocode/p
'PREDXCAT': module to calculate predicted means, medians, or proportions
for nominal X's / predxcat calculates and optionally graphs adjusted means
from / linear regression models, adjusted medians from quantile /
regression models, or adjusted proportions from logistic / regression
pretty_suite from http://fmwww.bc.edu/RePEc/bocode/p
'PRETTY_SUITE': module with programs to aid with routine reporting for
clinical trials / pretty_suite contains a suite of programmes which are
designed / to aid with routine reporting for clinical trials. The suite /
comprised the programme pretty_baseline which makes routinely /
probexog-tobexog from http://fmwww.bc.edu/RePEc/bocode/p
'PROBEXOG-TOBEXOG': modules to test exogeneity in probit/tobit / probexog
(tobexog) computes a test of exogeneity for a probit / (tobit) model
proposed by Smith and Blundell (1986). The test / involves specifying that
the exogeneity of one or more / explanatory variables is under suspicion.
psacalc from http://fmwww.bc.edu/RePEc/bocode/p
'PSACALC': module to calculate treatment effects and relative degree of
selection under proportional selection of observables and unobservables /
psacalc is performed after linear models to evaluate the / possible degree
of omitted variable bias under the assumption / that the selection on the
pspline from http://fmwww.bc.edu/RePEc/bocode/p
'PSPLINE': module providing a penalized spline scatterplot smoother based
on linear mixed model technology / pspline uses xtmixed to fit a penalized
spline regression and / plots the smoothed function. Additional covariates
can be / specified to adjust the smooth and plot partial residuals. / KW:
psr from http://fmwww.bc.edu/RePEc/bocode/p
'PSR': module to perform propensity score residual regression for
overlap-weight average treatment effect / psr implements OLS with
propensity score residual and IV / regression with instrument score
residual. The first syntax / fits OLS and the second the IV regression.
psreg from http://fmwww.bc.edu/RePEc/bocode/p
'PSREG': module for blocking with regression adjustments / This command
implements blocking with regression adjustments, / proposed by Imbens (J.
Human Resources, 2015). It relies on the / estimate of the propensity
score and uses regressions in / subclasses (blocks) of the propensity
psweight from http://fmwww.bc.edu/RePEc/bocode/p
'PSWEIGHT': module to perform IPW- and CBPS-type propensity score
reweighting, with various extensions / psweight is a Stata command that
offers Stata users easy access / to the psweight Mata class. psweight
subcmd computes / inverse-probability weighting (IPW) weights for average
ptrend from http://fmwww.bc.edu/RePEc/bocode/p
'PTREND': module for trend analysis for proportions / ptrend calculates a
chi-square statistic for the trend / (regression) of pvar on xvar, where
pvar is the proportion / rvar/(rvar+nrvar). A variable called _prop,
containing the values / of pvar, is left behind by ptrend. ptrend also
pvw from http://fmwww.bc.edu/RePEc/bocode/p
'PVW': module to perform predictive value weighting for covariate
misclassification in logistic regression / pvw implements the predictive
value weighting approach for / adjustment for misclassification in a
binary covariate in a / logistic regression model, as proposed by Lyles
pystacked from http://fmwww.bc.edu/RePEc/bocode/p
'PYSTACKED': module for stacking generalization and machine learning in
Stata / pystacked implements stacked generalization for regression and /
binary classification via Python's scikit-learn. Stacking / combines
multiple supervised machine learners---the “base” or / “level-0”'
pzms from http://fmwww.bc.edu/RePEc/bocode/p
'PZMS': module to implement the Placebo Zone optimal Model Selection
algorithm for regression discontinuity and kink designs / pzms implements
the placebo zone model selection algorithm for / regression discontinuity
(RDD) and kink (RKD) designs proposed in / Kettlewell & Siminski
qadf from http://fmwww.bc.edu/RePEc/bocode/q
'QADF': module to perform the Quantile Autoregression (QAR) unit root test
proposed by Koenker and Xiao (JASA, 2004) / The qadf package provides a
comprehensive Stata implementation / of the quantile unit root testing
framework, offering significant / advantages over standard ADF and
qardl from http://fmwww.bc.edu/RePEc/bocode/q
'QARDL': module to perform Quantile Autoregressive Distributed-Lag (QARDL)
estimation / The qardl command implements the Quantile Autoregressive /
Distributed-Lag (QARDL) model proposed by Cho, Kim, and Shin / (2015,
Journal of Econometrics, 188(2), 281-300). The QARDL model / extends the
qcount from http://fmwww.bc.edu/RePEc/bocode/q
'QCOUNT': program to fit quantile regression models for count data /
qcount estimates quantile regression models for count data using / the
jittering method suggested by Machando and Santos Silva / (2005). / KW:
quantile regression / KW: count data / Requires: Stata version 9.1 /
qhapipf from http://fmwww.bc.edu/RePEc/bocode/q
'QHAPIPF': module to perform analysis of quantitative traits using
regression and log-linear modelling when PHASE is unknown / This command
models the relationship between a normally / distributed continuous
variable in a population-based random / sample and individuals' haplotype.
qic from http://fmwww.bc.edu/RePEc/bocode/q
'QIC': module to compute model selection criterion in GEE analyses / qic
calculates the QIC and QIC_u criteria for model selection in / GEE, which
is an extension of the widely used AIC criterion in / ordinary regression
(Pan 2001). It allows for specification of / all 7 distributions -
qll from http://fmwww.bc.edu/RePEc/bocode/q
'QLL': module to implement Elliott-M\xfcller efficient test for general
persistent time variation in regression coefficients / qll performs the
qLL efficient test for general persistence in / time variation in
regression coefficients proposed by Elliott and / M\xfcller (Rev. Ec. Stud.,
qreg2 from http://fmwww.bc.edu/RePEc/bocode/q
'QREG2': module to perform quantile regression with robust and clustered
standard errors / qreg2 is a wrapper for qreg which estimates quantile
regression / and reports standard errors and t-statistics that are /
asymptotically valid under heteroskedasticity or under /
qregpd from http://fmwww.bc.edu/RePEc/bocode/q
'QREGPD': module to perform Quantile Regression for Panel Data / qregpd
can be used to fit the quantile regression for panel data / (QRPD)
estimator developed in Powell (2015). The estimator / addresses a
fundamental problem posed by alternative fixed-effect / quantile
qregplot from http://fmwww.bc.edu/RePEc/bocode/q
'QREGPLOT': module for plotting coefficients of a Quantile Regression /
qregplot graphs the coefficients of a quantile regression / produced by
various programs that produce quantile coefficients / including, qreg,
bsqreg, sqreg, mmqreg and rifhdreg (for / unconditional quantiles).
qregsel from http://fmwww.bc.edu/RePEc/bocode/q
'QREGSEL': module to estimate quantile regression corrected for sample
selection / qregsel estimates a copula-based sample selection model for /
quantile regression, as proposed by Arellano and Bonhomme, / Econometrica,
2017. / KW: quantile regression / KW: copula / KW: sample selection /
qrkd from http://fmwww.bc.edu/RePEc/bocode/q
'QRKD': module to estimate and produce robust inference for heterogeneous
causal effects of a continuous treatment in quantile regression kink
designs / qrkd executes estimation and robust inference for heterogeneous
/ causal effects of a continuous treatment in the quantile / regression
qrprocess from http://fmwww.bc.edu/RePEc/bocode/q
'QRPROCESS': module for quantile regression: fast algorithm, pointwise and
uniform inference / This package offers fast estimation and inference
procedures for / the linear quantile regression model. First, qrprocess
implements / new algorithms that are much quicker than the built-in Stata
qv from http://fmwww.bc.edu/RePEc/bocode/q
'QV': module to compute quasi-variances / qv estimates quasi-variances
(Firth, Sociological Methodology, / 2003) for one multi-category variable.
This approach addresses / the zero standard error issue for the reference
category in / regression models by "reallocating" the variances. / KW:
r2_mz from http://fmwww.bc.edu/RePEc/bocode/r
'R2_MZ': module to compute McKelvey & Zavoina's R2 / r2_mz is a
post-estimation command that computes McKelvey & / Zavoina's R2 for
multilevel logistic regression, random effects, / and fixed effects logit
and probit models. / KW: McKelvey / KW: Zavoina / KW: R2 / KW:
r2_nakagawa from http://fmwww.bc.edu/RePEc/bocode/r
'R2_NAKAGAWA': module for computing Nakagawa's R-squared statistic for
multilevel mixed-effects linear regression / r2_nakagawa is a
post-estimation command (also see: estat) that / computes the R-squared
statistic following -mixed-. R-squared is / computed as described by
r2o from http://fmwww.bc.edu/RePEc/bocode/r
'R2O': module to calculate an ordinal explained variation statistic / r2o
calculates the ordinal explained variation statistic (i.e., / R-squared)
described by Lacy (2006), which is used to summarize / the fit of a
regression model for an ordinal response. It rests / on an ordinal
r2var from http://fmwww.bc.edu/RePEc/bocode/r
'R2VAR': Module to Compute (VAR) Overall System R2, F-Test, and Chi2-Test
/ r2var Computes (VAR) Overall System R2, F-Test, and Chi2-Test / KW:
Vector Autoregressive Model / KW: VAR / KW: SUR / KW: Regression / KW:
Overall System R-squared / KW: Overall System F-Test / KW: Overall System
r_ml_stata_cv from http://fmwww.bc.edu/RePEc/bocode/r
'R_ML_STATA_CV': module to implement machine learning regression in Stata
/ r_ml_stata_cv is a command for implementing machine / learning
regression algorithms in Stata 16. It uses the / Stata/Python integration
(sfi) capability of Stata 16 and allows / to implement the following
radf from http://fmwww.bc.edu/RePEc/bocode/r
'RADF': module to calculate unit root tests for explosive behaviour / radf
computes the right-tail augmented Dickey-Fuller (1979) / (ADF) unit root
test, and its further developments based on / supremum statistics derived
from ADF-type regressions / estimated using recursive windows (Phillips,
randcmdci from http://fmwww.bc.edu/RePEc/bocode/r
'RANDCMDCI': module to produce robust randomization-t p-values and
confidence intervals for regression coefficients / randcmdci computes
randomization confidence intervals and / p-values that are asymptotically
robust to deviations from the / sharp null in favour of average treatment
rangerun from http://fmwww.bc.edu/RePEc/bocode/r
'RANGERUN': module to run Stata commands on observations within range /
rangerun runs a user-supplied Stata program for each observation / in the
sample. At each pass, the data in memory is cleared and / replaced with
observations that fall within the interval bounds / specified for the
ranktest from http://fmwww.bc.edu/RePEc/bocode/r
'RANKTEST': module to test the rank of a matrix / ranktest implements
various tests for the rank of a matrix. / Tests of the rank of a matrix
have many practical applications. / For example, in econometrics the
requirement for identification / is the rank condition, which states that
rardl from http://fmwww.bc.edu/RePEc/bocode/r
'RARDL': module to perform Rolling-Window and Recursive ARDL Cointegration
Analysis / rardl implements the Rolling-Window ARDL bounds testing /
approach of Shahbaz, Khan & Mubarak (2023) and the Recursive / ARDL, ADF,
and Granger causality tests of Khan, Shahbaz & Napari / (2023). These
rassign from http://fmwww.bc.edu/RePEc/bocode/r
'RASSIGN': module to perform regression-based test for random assignment
to peer groups / rassign performs a regression-based test for the
(conditional) / random assignment of individuals in urns to peer groups /
(Jochmans, 2020). The dependent variable is a characteristic of / the
rbiprobit from http://fmwww.bc.edu/RePEc/bocode/r
'RBIPROBIT': module to estimate recursive bivariate probit regressions /
rbiprobit is a user-written command that fits a recursive / bivariate
probit regression using maximum likelihood estimation. / The model
involves an outcome equation and a treatment equation, / whereas the
rc_spline from http://fmwww.bc.edu/RePEc/bocode/r
'RC_SPLINE': module to generate restricted cubic splines / rc_spline
creates variables that can be used for regression / models in which the
linear predictor f(xvar) is assumed to equal / a restricted cubic spline
function of an independent variable / xvar. In these regressions, the
rcm from http://fmwww.bc.edu/RePEc/bocode/r
'RCM': module to implement regression control method / panel data approach
to program evaluation / rcm effectively implements regression control
method (RCM), / aka a panel data approach for program evaluation (Hsiao et
al., / J. Ap. Met. 2012), which exploits cross-sectional correlation / to
rcspline from http://fmwww.bc.edu/RePEc/bocode/r
'RCSPLINE': module for restricted cubic spline smoothing / rcspline
computes and graphs a restricted cubic spline smooth of / a response given
a predictor. It creates variables containing a / restricted cubic spline,
regresses the response against those new / variables, thus obtaining
rctable from http://fmwww.bc.edu/RePEc/bocode/r
'RCTABLE': module to create a table used in randomized controlled trials /
rctable creates a simple table to be used mainly in Randomized /
Controlled Trials or in experimental settings where a treatment / group is
compared to a comparison group. rctable creates a table / in your dataset
rd from http://fmwww.bc.edu/RePEc/bocode/r
'RD': module for regression discontinuity estimation / rd implements a set
of regression-discontinuity estimation / methods that are thought to have
very good internal validity, for / estimating the causal effect of one
explanatory variable in / the case where there is an observable jump
rdcont from http://fmwww.bc.edu/RePEc/bocode/r
'RDCONT': module to compute non-randomized approximate sign test of
density continuity / Regression discontinuity designs operate under the
assumption / that the running variable is continuous at a threshold.
rdcont / tests that assumption using a non-randomized approximate sign /
rdcv from http://fmwww.bc.edu/RePEc/bocode/r
'RDCV': module to perform Sharp Regression Discontinuity Design with Cross
Validation Bandwidth Selection / This command implements estimation of
sharp regression / discontinuity designs using a flexible cross-validation
(CV) / procedure for optimal bandwidth selection. / KW: regression
rddsga from http://fmwww.bc.edu/RePEc/bocode/r
'RDDSGA': module to conduct subgroup analysis for regression discontinuity
designs / rddsga allows to conduct a binary subgroup analysis in RDD /
settings based on inverse propensity score weights (IPSW). / Observations
in each subgroup are weighted by the inverse of / their conditional
rdexo from http://fmwww.bc.edu/RePEc/bocode/r
'RDEXO': module to produces relevant estimates for testing the external
validity of LATE / The rdexo command produces relevant estimates for
testing the / external validity of LATE to other compliance groups at the
/ threshold in fuzzy regression discontinuity designs, according to /
rdmse from http://fmwww.bc.edu/RePEc/bocode/r
'RDMSE': module to estimate the mean squared error of a local polynomial
regression discontinuity or regression kink estimator / This program
computes the (asymptotic) mean squared error (MSE) / of a local polynomial
regression discontinuity or regression kink / estimator as proposed by
rdpermute from http://fmwww.bc.edu/RePEc/bocode/r
'RDPERMUTE': module to perform a permutation test for the Regression Kink
(RK) and Regression Discontinuity (RD) Design / rdpermute implements a
permutation test for the Regression Kink / (RK) and Regression
Discontinuity (RD) Design for the one / dimensional case of one Outcome
rdqte from http://fmwww.bc.edu/RePEc/bocode/r
'RDQTE': module for estimation and robust inference for quantile treatment
effects (QTE) in regression discontinuity designs (RDD) / This program
executes estimation and robust inference for / quantile treatment effects
(QTE) in the sharp and fuzzy / regression discontinuity designs (RDD)
rdrobust from http://fmwww.bc.edu/RePEc/bocode/r
'RDROBUST': module to provide robust data-driven inference in the
regression-discontinuity design / rdrobust implements local polynomial
Regression Discontinuity / (RD) point estimators with robust
bias-corrected / confidence intervals and inference procedures developed
reffadjust from http://fmwww.bc.edu/RePEc/bocode/r
'REFFADJUST': module to estimate adjusted regression coefficients for the
association between two random effects variables / reffadjust provides two
postestimation commands, / reffadjustsim and reffadjust4nlcom, to estimate
adjusted / regression coefficients for the association between two random
reformat from http://fmwww.bc.edu/RePEc/bocode/r
'REFORMAT': module to reformat regression output / The output from the
last regression command is re-displayed in a / more readable format using
variable and value labels for clarity. / The columns to be displayed can
be controlled by the user and / extra options to show the number of
reg2docx from http://fmwww.bc.edu/RePEc/bocode/r
'REG2DOCX': module to report regression results to formatted table in DOCX
file. / reg2docx is used after est store. Users can estimate / different
regression models. After that they can save the / regression results with
est store command. Then, users can call / reg2docx to design a formatted
reg2hdfe from http://fmwww.bc.edu/RePEc/bocode/r
'REG2HDFE': module to estimate a Linear Regression Model with two High
Dimensional Fixed Effects / This command implements the algorithm of
Guimaraes & Portugal / for estimation of a linear regression model with
two high / dimensional fixed effects. The command is particularly suited
reg2logit from http://fmwww.bc.edu/RePEc/bocode/r
'REG2LOGIT': module to approximate logistic regression parameters using
OLS linear regression / reg2logit estimates the parameters of a logistic
regression / of yvar on xvars by transforming OLS estimates of the linear
/ regression of yvar on xvars. Factor xvars are allowed. The /
reg_sandwich from http://fmwww.bc.edu/RePEc/bocode/r
'REG_SANDWICH': module to compute cluster-robust (sandwich) variance
estimators with small-sample corrections for linear regression /
reg_sandwich provides cluster-robust variance estimators (i.e., / sandwich
estimators) for ordinary and weighted least squares / linear regression
reg_ss from http://fmwww.bc.edu/RePEc/bocode/r
'REG_SS': module to compute confidence intervals, standard errors, and
p-values in a linear regression in which the regressor of interest has a
shift-share structure / This package computes confidence intervals,
standard errors, and / p-values in a linear regression in which the
regall from http://fmwww.bc.edu/RePEc/bocode/r
'REGALL': module to run and compare all regressions derived from complete
sets of regressors / regall runs all possible regressions derived from
varlist and / compares results with R2 (R2, Adjusted R2 or Pseudo R2) and
/ Information Criteria (AIC or BIC). For example, a set of 3 / regressors
reganat from http://fmwww.bc.edu/RePEc/bocode/r
'REGANAT': module to perform graphical inspection of linear multivariate
models based on regression anatomy / reganat is a graphical tool for
inspecting the effect of a / covariate on a dependent variable in the
context of multivariate / OLS estimation. The name is an acronym for the
regcheck from http://fmwww.bc.edu/RePEc/bocode/r
'REGCHECK': module to examine regression assumptions / This routine
examines several underlying assumptions after / regression. It invokes the
Breusch-Pagan test, computes Variance / Inflation Factors, the
Shapiro-Wilk test, the linktest, the RESET / test and Cook's distance. /
regcoef from http://fmwww.bc.edu/RePEc/bocode/r
'REGCOEF': module to compute coefficients for quantifying relative
importance of predictors / regcoef computes the following five different
coefficients first / three of which are commonly used to determine the
relative / importance of predictors of a regression model. These are the /
regdis from http://fmwww.bc.edu/RePEc/bocode/r
'REGDIS': module to control variables and decimals in regression displays
/ regdis provides a fast and easy way to control variables and / decimals
in regression displays. In addition to setting the / number of decimals to
be displayed, regdis will also drop/keep / variables from the standard
regfit from http://fmwww.bc.edu/RePEc/bocode/r
'REGFIT': module to Output The Equation of a Regression / regfit Outputs
The Equation of a Regression / KW: regression / KW: fit / Requires: Stata
version 9 / Distribution-Date: 20201125 / Author: Liu Wei, School of
Sociology and Population Studies, Renmin University of China / Support:
reghdfe from http://fmwww.bc.edu/RePEc/bocode/r
'REGHDFE': module to perform linear or instrumental-variable regression
absorbing any number of high-dimensional fixed effects / reghdfe fits a
linear or instrumental-variable regression / absorbing an arbitrary number
of categorical factors and / factorial interactions Optionally, it saves
regife from http://fmwww.bc.edu/RePEc/bocode/r
'REGIFE': module to estimate linear models with interactive fixed effects
/ regife fits a model with interactive fixed effects following Bai /
(Econometrica, 2009). Optionally, it saves the estimated / factors.
Errors are computed following the regressions indicated / in Section 6,
regintfe from http://fmwww.bc.edu/RePEc/bocode/r
'REGINTFE': module to estimate a linear regression model with one
interacted high dimensional fixed effect / This command estimates a linear
regression model with one / high-dimensional interacted fixed effect. The
command makes use / of the Frisch-Waugh-Lovell result to avoid computing
reglike from http://fmwww.bc.edu/RePEc/bocode/r
'REGLIKE': module to calculate log-likelihood function value from regress
/ After running regress, reglike computes the log-likelihood and / puts
its value into the global macro S_E_ll. / Author: Bill Sribney, Stata
Corporation / Support: email wsribney@stata.com / Distribution-Date:
regmain from http://fmwww.bc.edu/RePEc/bocode/r
'REGMAIN': module to perform Quasi-Maximum Likelihood Regression / regmain
allows the user to run regressions specifying a specific / distribution
for the error term. This program also calculates / distributional
parameters and displays graphically the fit of the / distribution. Users
regoprob from http://fmwww.bc.edu/RePEc/bocode/r
'REGOPROB': module to estimate random effects generalized ordered probit
models / regoprob is a user-written procedure to estimate random effects /
generalized ordered probit models in Stata. The actual values / taken on
by the dependent variable are irrelevant except that / larger values are
regoptwgt from http://fmwww.bc.edu/RePEc/bocode/r
'REGOPTWGT': module to produce optimal population weighting for
regressions and instrumental variables / regoptwgt estimates regression
coefficients based on an / optimal weighting that takes into account both
(1) variance in / the error term that is correlated with the inverse of
regpar from http://fmwww.bc.edu/RePEc/bocode/r
'REGPAR': module to compute population attributable risks from binary
regression models / regpar calculates confidence intervals for population
/ attributable risks, and also for scenario proportions. / regpar can be
used after an estimation command whose / predicted values are interpreted
regpred from http://fmwww.bc.edu/RePEc/bocode/r
'REGPRED': module to calculate linear regression predictions / regpred
calculates and prints predicted values and 95% confidence / intervals from
linear regression estimates for a continuous X / variable, adjusted for
covariates. Default prints predicted / values and confidence intervals;
regresby from http://fmwww.bc.edu/RePEc/bocode/r
'REGRESBY': module to generate regression residuals by byvarlist / The
syntax is regresby varlist [if <exp>] [in <range>] [weight], /
by(byvarlist) generate(resvar) [regress_options] to get the / residuals
from by byvarlist: regress varlist ..., regress_options / / Author:
regsave from http://fmwww.bc.edu/RePEc/bocode/r
'REGSAVE': module to save regression results to a Stata-formatted dataset
/ regsave fetches output from Stata's e() macros, scalars, and / matrices
and stores them in a Stata-formatted dataset. This / command provides a
user-friendly way to manipulate a large number / of regression results by
regsensitivity from http://fmwww.bc.edu/RePEc/bocode/r
'REGSENSITIVITY': module for regression sensitivity analysis / This module
provides a set of tools for analyzing the / sensitivity of regression
estimates to the presence of omitted / variables. Specifically, it
calculates bounds on regression / coefficients by relaxing the assumption
regtex from http://fmwww.bc.edu/RePEc/bocode/r
'REGTEX': module to export regression results to publication-ready LaTeX
tables / regtex exports regression results from multiple models to a /
publication-ready LaTeX table. It automatically handles / coefficient
formatting, significance stars, standard errors, and / model statistics.
regwls from http://fmwww.bc.edu/RePEc/bocode/r
'REGWLS': module to estimate Weighted Least Squares with factor variables
/ This command incorporates support for factor variables, / extending the
command wls0 (Ender, UCLA). It also allows for the / absorption of one
fixed effects using the algorithm of the / command areg. This is
regxfe from http://fmwww.bc.edu/RePEc/bocode/r
'REGXFE': module to fit a linear high-order fixed-effects model / regxfe
estimates a linear high order fixed effect, allowing for / up to 7 fixed
effects. It allows for the use of weights, / robust and one way clustered
standard errors. Robust and cluster / errors are estimated based on the
relogit from http://fmwww.bc.edu/RePEc/bocode/r
'RELOGIT': module to perform Rare Event Logistic Regression / relogit is a
suite of programs for estimating and interpreting / logit results when the
sample is unbalanced (one outcome is / rarer than the other) or has been
selected by a rule / correlated with the dependent variable. RELOGIT
rely from http://fmwww.bc.edu/RePEc/bocode/r
'RELY': module to graph reliability plot of predictions for linear or
logistic regression models / rely examines reliability of predicted risks
following a / logistic model. It creates categories of predicted risk, /
divided either into fractions, e.g. tenths, or any number of / equal size
relyplot from http://fmwww.bc.edu/RePEc/bocode/r
'RELYPLOT': module to graph reliability plot of predictions for linear or
logistic regression models / relyplot examines reliability of predicted
risks following a / logistic model. It creates categories of predicted
risk, / divided either into fractions, e.g. tenths, or any number of /
remr from http://fmwww.bc.edu/RePEc/bocode/r
'REMR': module to implement robust error meta-regression method for
dose–response meta-analysis / remr performs dose-response meta-analysis
using inverse variance / weighted least squares (WLS) regression with
cluster robust error / variances. This approach is a special case of the
reset from http://fmwww.bc.edu/RePEc/bocode/r
'RESET': module to calculate specification tests in regression analysis /
reset computes several forms of the Ramsey Specification Error / Test
after an OLS regression. / KW: regression / KW: OLS / KW: Ramsey
Specification ResetF Test / KW: DeBenedictis-Giles Specification ResetL
reset2 from http://fmwww.bc.edu/RePEc/bocode/r
'RESET2': module to calculate specification tests in 2SLS-IV regression
analysis / reset computes several forms of the Ramsey Specification Error
/ Test after an IV-2SLS regression. / KW: regression / KW: OLS / KW:
Ramsey Specification ResetF Test / KW: DeBenedictis-Giles Specification
resetxt from http://fmwww.bc.edu/RePEc/bocode/r
'RESETXT': Module to Compute Panel Data REgression Specification Error
Tests (RESET) / resetxt computes Panel Data REgression Specification Error
Tests / (RESET) / KW: Regression / KW: Panel / KW: Cross Sections-Time
Series / KW: Ramsey RESET Test / KW: DeBenedictis-Giles Specification
reu from http://fmwww.bc.edu/RePEc/bocode/r
'REU': module to compute number of random error units (REU) in
epidemiological studies / reu is a post-estimation command that displays
the number of / random error units (REU) for continuous and binary
predictors / of the previously fitted model (regress, glm, logit,
rforest from http://fmwww.bc.edu/RePEc/bocode/r
'RFOREST': module to implement Random Forest algorithm / rforest is a
plugin for random forest classification and / regression algorithms. It is
built on a Java backend which acts / as an interface to the RandomForest
Java class presented in / the WEKA project, developed at the University of
rho_xtregar from http://fmwww.bc.edu/RePEc/bocode/r
'RHO_XTREGAR': module to estimate a consistent and asymptotically unbiased
autocorrelation coefficient for xtregar fixed-effects or random-effects
linear model with an AR(1) disturbance / rho_xtregar estimates the
autoregressive parameter for / cross-sectional time-series regression
ridge2sls from http://fmwww.bc.edu/RePEc/bocode/r
'RIDGE2SLS': module to compute Two-Stage Least Squares (2SLS) Ridge &
Weighted Regression / ridge2sls computes Two-Stage Least Squares (2SLS)
Ridge & / Weighted Regression. ridge2sls estimates Model Selection /
Diagnostic Criteria and Marginal Effects and Elasticities. / KW:
ridgereg from http://fmwww.bc.edu/RePEc/bocode/r
'RIDGEREG': module to compute Ridge Regression Models / ridgereg estimates
Ridge Regression Models / KW: regression / KW: Multicollinearity / KW:
ridge / KW: Ridge Regression / KW: Farrar-Glauber Multicollinearity tests
/ KW: Variance Inflation Factor / KW: Condition Index / KW: Theil R2
rif from http://fmwww.bc.edu/RePEc/bocode/r
'RIF': module to compute Recentered Influence Functions (RIF):
RIF-Regression and RIF-Decomposition / rif contains 5 community
contributed commands that aim to / facilitate the use of recentered
influence functions as a / statistical tool for statistical inference
riflogit from http://fmwww.bc.edu/RePEc/bocode/r
'RIFLOGIT': module to fit unconditional logistic regression / riflogit
fits an unconditional logistic regression by applying / least-squares
estimation to the RIF (recentered influence / function) of the marginal
log odds of a positive outcome. The / exponents of the coefficients have
rii from http://fmwww.bc.edu/RePEc/bocode/r
'RII': module to perform Repeated-Imputation Inference / rii is a prefix
command that runs multiple imputations of a / model based on the value of
the multiple imputation variable. / rii has been tested on probit, tobit,
cnreg, and regress. rii / uses the repeated-imputation inference (RII)
riigen from http://fmwww.bc.edu/RePEc/bocode/r
'RIIGEN': module to generate Variables to Compute the Relative Index of
Inequality / riigen calculates new variables for a list of determinants
that / allow to estimate the relative index of inequality in regression /
models. The relative index of inequality (RII) is a / regression-based
rkqte from http://fmwww.bc.edu/RePEc/bocode/r
'RKQTE': module for estimation and robust inference for quantile treatment
effects (QTE) in regression kink designs (RKD) / rkqte executes estimation
and robust inference for quantile / treatment effects (QTE) in regression
kink designs (RKD) based on / Chen, Chiang, and Sasaki (Econometric
robit from http://fmwww.bc.edu/RePEc/bocode/r
'ROBIT': module to estimate robit regression for binary outcomes / robit
fits a robit regression model, with a number of degrees / of freedom
specified by the user, as a robust alternative to / logistic regression.
/ KW: robit / KW: regression / Requires: Stata version 16 and xlink from
robreg from http://fmwww.bc.edu/RePEc/bocode/r
'ROBREG': module providing robust regression estimators / robreg provides
a number of robust estimators for linear / regression models. Among them
are the high breakdown-point and / high efficiency MM estimator, the Huber
and bisquare M estimator, / the S estimator, as well as quantile
robreg10 from http://fmwww.bc.edu/RePEc/bocode/r
'ROBREG10': module providing robust regression estimators / robreg10
provides a number of robust estimators for linear / regression models.
Among them are the high breakdown-point and / high efficiency
MM-estimator, the Huber and bisquare M-estimator, / and the S-estimator,
robumeta from http://fmwww.bc.edu/RePEc/bocode/r
'ROBUMETA': module to perform robust variance estimation in
meta-regression with dependent effect size estimates / robumeta provides a
robust method for estimating standard errors / in meta-regression,
particularly when there are dependent / effects. Dependent effects occur
rolling2 from http://fmwww.bc.edu/RePEc/bocode/r
'ROLLING2': module to perform rolling window and recursive estimation /
rolling2 is identical to the official rolling prefix with one / exception.
Although not documented as such, official rolling / operates separately on
each panel of a panel data set. Under some / circumstances, you may want
rolling3 from http://fmwww.bc.edu/RePEc/bocode/r
'ROLLING3': module to compute predicted values for rolling regressions /
rolling3 generates predicted values for each rolling regression / and
saved them as new variables in original data file. It also / allows user
looping rolling predict command on data panels. / KW: rolling regression
rollreg from http://fmwww.bc.edu/RePEc/bocode/r
'ROLLREG': module to perform rolling regression estimation / rollreg
computes three different varieties of rolling regression / estimates.
With the move() option, moving-window estimates of / the specified window
width are computed for the available sample / period. With the add()
ros from http://fmwww.bc.edu/RePEc/bocode/r
'ROS': module for estimation of regression order statistics / The command
ros is for estimating upper reference bounds for a / dataset with possibly
non-detectable/censored values and / possibly contaminated in the upper
end. The upper reference / bounds are from the mean and standard
rqr from http://fmwww.bc.edu/RePEc/bocode/r
'RQR': module to estimate the residualized quantile regression model / The
rqr package includes the rqr and rqrplot commands. The rqr / command
implements the residualized quantile regression model, / which estimates
unconditional quantile treatment effects. It is a / flexible and fast
rrlogit from http://fmwww.bc.edu/RePEc/bocode/r
'RRLOGIT': module to estimate logistic regression for randomized response
data / rrlogit fits a maximum-likelihood logistic regression for /
randomized response data. / KW: randomized response technique / KW: RRT /
KW: logit / Requires: Stata version 9.1 / Distribution-Date: 20110512 /
rrp from http://fmwww.bc.edu/RePEc/bocode/r
'RRP': module to compute Rescaled Regression Prediction (RRP) using two
samples / rrp implements a Rescaled Regression Prediction (RRP) using /
two samples in two steps. First it creates a new variable, by / imputing
the dependent variable in the current sample, using the / stored
rrr from http://fmwww.bc.edu/RePEc/bocode/r
'RRR': module to perform Reduced rank regression / rrr executes the
reduced rank regression, a multivariate / linear regression with the
function of dimension reduction. / This command is based on the PCA of
the OLS predicted vaules / for dependent variables. It generates the
rscore from http://fmwww.bc.edu/RePEc/bocode/r
'RSCORE': module for estimation of responsiveness scores / rscore computes
unit-specific responsiveness scores using an / iterated
Random-Coefficient-Regression (RCR). The basic / econometrics of this
model can be found in Wooldridge (2002, pp. / 638-642). The model
rtmci from http://fmwww.bc.edu/RePEc/bocode/r
'RTMCI': module to estimate regression to the mean effects with confidence
intervals / rtmci calculates the regression to the mean effect for a /
variable that is generally measured at two points in time (i.e., /
"pre-test" and "post-test"), based on a defined cutoff value on / the
runmixregls from http://fmwww.bc.edu/RePEc/bocode/r
'RUNMIXREGLS': Run the MIXREGLS software from within Stata / / This module
runs the MIXREGLS mixed-effects location scale software / (Hedeker and
Nordgren 2013) from within Stata. The mixed-effects location / scale model
extends the standard two-level random-intercept mixed-effects / model for
russ_stata from http://fmwww.bc.edu/RePEc/bocode/r
'RUSS_STATA': tutorial in Russian / Applied econometric analysis with
Stata 6 (in Russian) is a / 110-page introduction into econometric uses of
regression with / Stata 6 written in Russian. The initial purpose of this
book / was to serve as the lecture notes on the author's weekly / seminars
rwrmed from http://fmwww.bc.edu/RePEc/bocode/r
'RWRMED': module for performing causal mediation analysis using
regression-with-residuals / rwrmed performs causal mediation analysis
using / regression-with-residuals. Using gsem, two models are estimated: /
a model for the mediator conditional on treatment and the / pre-treatment
sampsi_reg from http://fmwww.bc.edu/RePEc/bocode/s
'SAMPSI_REG': module to calculate the sample size/power for linear
regression / sampsi_reg calculates the power and sample size for a simple
/ linear regression. The theory behind this command is described in /
Dupont and Plummer (1998) Power and Sample Size Calculations for / Studies
samregc from http://fmwww.bc.edu/RePEc/bocode/s
'SAMREGC': module to perform Sensitivity Analysis of Main Regression
Coefficients / SAMREGC is a Stata package designed to enhance robustness
and / sensitivity analyses for regression coefficients by / systematically
performing subset regressions. It significantly / improves upon existing
scenttest from http://fmwww.bc.edu/RePEc/bocode/s
'SCENTTEST': module to compute scenario arithmetic means and their
difference / scenttest calculates confidence intervals for 2 scenario /
arithmetic (or geometric) means, and for their difference / (or ratio).
scenttest can be used after an estimation / command whose predicted values
scoregrp from http://fmwww.bc.edu/RePEc/bocode/s
'SCOREGRP': module to perform a score test for equality of parameters
across groups of observations / scoregrp performs a score test for the
equality of (a) / parameter(s) across groups of observations. It is a /
postestimation command and works after poisson, logit, logistic, / probit
seldum from http://fmwww.bc.edu/RePEc/bocode/s
'SELDUM': module to transform indicator variables coefficients in semilog
model / seldum is a post-estimation command to be used after the /
estimation of a linear regression model with a logarithmic / dependent
variable. It eases the correct interpretation of dummy / variables in
semipar from http://fmwww.bc.edu/RePEc/bocode/s
'SEMIPAR': module to compute Robinson's (1988) semiparametric regression
estimator / semipar estimates Robinson's (Econometrica, 1988) double /
residual estimator and estimates the nonlinear relation between / the
variable set in nonpar and the dependent variable. The test / option
sendtoslack from http://fmwww.bc.edu/RePEc/bocode/s
'SENDTOSLACK': module to send notifications from Stata to your smartphone
through Slack / sendtoslack allows you to send messages from Stata to your
/ smartphone, passing through Slack. Setup is very fast and for The /
module is made available under terms of the GPL v3 /
sensimatch from http://fmwww.bc.edu/RePEc/bocode/s
'SENSIMATCH': module to provide data-driven sensitivity analysis for
Matching estimator / sensimatch provides a sensitivity test for checking
the / robustness of the selection-on-observables assumption in / treatment
effect observational studies, both within a regression / adjustment and a
sentinel from http://fmwww.bc.edu/RePEc/bocode/s
'SENTINEL': module for selecting sentinel variants from SNPs in a
case-control study / sentinel selects sentinel SNPs from the genetic
variants in / indepvars. These are SNPs that best detect independent /
risk-altering signals. In a multivariable multiplicative / logistic
sgtreg from http://fmwww.bc.edu/RePEc/bocode/s
'SGTREG': module to perform Regression using the Skewed Generalized T
Distribution / The standard linear regression model typically assumes that
the / errors are in- dependently and identically distributed. The /
corresponding ordinary least squares (OLS) estimators will yield / the
shapley2 from http://fmwww.bc.edu/RePEc/bocode/s
'SHAPLEY2': module to compute additive decomposition of estimation
statistics by regressors or groups of regressors / Shapley2 is a
post-estimation command to compute the / Shorrocks-Shapley decomposition
of any statistic of the model / (normally the R squared). Shapley2 can be
sigcoef from http://fmwww.bc.edu/RePEc/bocode/s
'SIGCOEF': module to count statistically significant coefficients across
models / This is a Stata program to count the number of statistically /
significant coefficients for the same variables (using same / names)
across models using z as test statistics (usually / using maximum
simarwilson from http://fmwww.bc.edu/RePEc/bocode/s
'SIMARWILSON': module to perform Simar & Wilson (2007) efficiency analysis
/ simarwilson implements the procedures proposed by Simar and / Wilson
(2007) for regression analysis of DEA (data envelopment / analysis)
efficiency scores. Unlike naive two-step approaches, / the Simar and
sivqr from http://fmwww.bc.edu/RePEc/bocode/s
'SIVQR': module to perform smoothed IV quantile regression / sivqr
estimates quantile regression models in which one or more / of the
regressors are endogenously determined. It is like qreg, / but allowing
for instrumental variables to address endogeneity. / Or, it is like
sivreg from http://fmwww.bc.edu/RePEc/bocode/s
'SIVREG': module to perform adaptive Lasso with some invalid instruments /
sivreg estimates a linear instrumental variables regression / where some
of the instruments fail the exclusion restriction / and are thus invalid.
The LARS algorithm (Efron et al., 2004) is / applied as long as the Hansen
sjive from http://fmwww.bc.edu/RePEc/bocode/s
'SJIVE': module providing Shrunken Jackknife instrumental variables
estimator (SJIVE) / sjive implements the Shrunken Jackknife Instrumental /
Variables Estimator (SJIVE) developed by Frandsen, Lefgren, / Leslie, and
McIntyre (2025). Two-stage least squares is / well-known to be biased and
skewreg from http://fmwww.bc.edu/RePEc/bocode/s
'SKEWREG': module to estimate skewness and kurtosis regressions / skewreg
performs skewness regression for cross-sectional or / time-series data as
defined in Chen and Xiao (2020), which / quantifies the effects of
covariates on quantile-based measure of / skewness of the conditional
smvcir from http://fmwww.bc.edu/RePEc/bocode/s
'SMVCIR': module to compute sliced mean variance-covariance inverse
regression / smvcir performs the sliced mean variance–covariance inverse
/ regression (SMVCIR) algorithm on grouped multivariate data. The / data
are transformed to a new coordinate system where the group / mean,
soreg from http://fmwww.bc.edu/RePEc/bocode/s
'SOREG': module to implement the stereotype ordinal regression model /
soreg implements the stereotype ordinal regression model / proposed by
Anderson. Constraints may be defined to perform / constrained estimation.
/ KW: regression / KW: ordinal measures / Requires: Stata version 6.0 /
sortlistby from http://fmwww.bc.edu/RePEc/bocode/s
'SORTLISTBY': module to sort by random or by ancillary numlist /
sortlistby will sort the items in list at random or by the / values of an
ancillary numlist. sortlistby2 does the same; / however, it is a little
bit faster if the number of items in list / is small (and much slower if
spatial_hac_iv from http://fmwww.bc.edu/RePEc/bocode/s
'SPATIAL_HAC_IV': module to estimate an instrumental variable regression,
adjusting standard errors for spatial correlation, heteroskedasticity, and
autocorrelation / This routine performs an instrumental variables
regression with / Conley, HAC-robust standard errors / KW: IV / KW:
spautoreg from http://fmwww.bc.edu/RePEc/bocode/s
'SPAUTOREG': module to estimate Spatial
(Lag-Error-Durbin-SAC-SPGKS-SPGSAR-GS2SLS-GS3SLS-SPML-SPGS-SPIVREG-IVTobit)
/ spautoreg module to run Spatial /
(Lag-Error-Durbin-SAC-GS2SLS-GS3SLS-SPML-SPGS-SPIVREG-IVTobit) /
speccheck from http://fmwww.bc.edu/RePEc/bocode/s
'SPECCHECK': module to check model specification / speccheck generates a
standardized graphical output from a / regression specification. The
command will individually regress / depvar against all possible indepvars
combinations. The command / will generate a figure with four subfigures.
spglsxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPGLSXT': module to estimate Spatial Panel Autoregressive Generalized
Least Squares Regression / spglsxt estimates Spatial Panel Autoregressive
Generalized Least / Squares Regression / KW: regression / KW: spatial /
KW: panel / KW: Spatial Panel Autoregressive Generalized Least Squares /
spgmm from http://fmwww.bc.edu/RePEc/bocode/s
'SPGMM': module to estimate Spatial Autoregressive Generalized Method of
Moments Cross Sections Regression / spglsxt estimates Spatial
Autoregressive Generalized Method of / Moments Cross Sections Regression /
KW: regression / KW: spatial / KW: autoregression / KW: GMM / Requires:
spgmmxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPGMMXT': module to estimate Spatial Panel Autoregressive Generalized
Method of Moments Regression / spgmmxt estimates Spatial Panel
Autoregressive Generalized / Method of Moments Regression / KW: regression
/ KW: spatial / KW: panel / KW: GMM / Requires: Stata version 11 /
spmstar from http://fmwww.bc.edu/RePEc/bocode/s
'SPMSTAR': module to Estimate (m-STAR) Spatial Multiparametric Spatio
Temporal AutoRegressive Regression Models / spmstar estimates (m-STAR)
Spatial Multiparametric Spatio / Temporal AutoRegressive Regression for
Cross Sections data. / KW: Spatial / KW: Cross Section / KW: Regression /
spmstard from http://fmwww.bc.edu/RePEc/bocode/s
'SPMSTARD': module to estimate Multiparametric Spatio Temporal
AutoRegressive Regression Spatial Durbin Cross Sections Models / spmstard
estimates Multiparametric Spatio Temporal / AutoRegressive Regression
Spatial Durbin Cross Sections Models / KW: Spatial / KW: Cross Section /
spmstardh from http://fmwww.bc.edu/RePEc/bocode/s
'SPMSTARDH': module to Estimate (m-STAR) Spatial Multiparametric Spatio
Temporal AutoRegressive Regression: Spatial Durbin Multiplicative
Heteroscedasticity Cross Sections Models / spmstardh estimates estimate
(m-STAR) Spatial Multiparametric / Spatio Temporal AutoRegressive
spmstardhxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPMSTARDHXT': module to estimate (m-STAR) Spatial Multiparametric Spatio
Temporal AutoRegressive Regression: Spatial Durbin Multiplicative
Heteroscedasticity Panel Models / spmstardhxt estimates (m-STAR) Spatial
Multiparametric Spatio / Temporal AutoRegressive Regression for Spatial
spmstarh from http://fmwww.bc.edu/RePEc/bocode/s
'SPMSTARH': Module to Estimate (m-STAR) Spatial Multiparametric Spatio
Temporal AutoRegressive Regression: Spatial Lag Multiplicative
Heteroscedasticity Cross Sections Models / spmstarh estimates (m-STAR)
Spatial Multiparametric Spatio / Temporal AutoRegressive Regression for
spmstarhxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPMSTARHXT': module to Estimate (m-STAR) Spatial Multiparametric Spatio
Temporal AutoRegressive Regression: Spatial Lag Multiplicative
Heteroscedasticity Panel Models / spmstarhxt estimates (m-STAR) Spatial
Multiparametric Spatio / Temporal AutoRegressive Regression for Spatial
spmstarxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPMSTARXT': module to Estimate (m-STAR) Spatial Panel Multiparametric
Spatio Temporal AutoRegressive Regression Models / spmstar estimates
(m-STAR) Spatial Panel Multiparametric Spatio / Temporal AutoRegressive
Regression for Cross Sections data. / KW: Spatial / KW: Cross Section /
sppack from http://fmwww.bc.edu/RePEc/bocode/s
'SPPACK': module for cross-section spatial-autoregressive models / The
spmat, spreg and spivreg commands create spatial-weighting / matrices,
manage spatial-weighting matrices, and estimate the / parameters of
cross-sectional spatial-autoregressive models with /
spregcs from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGCS': Module Econometric Toolkit to Estimate Spatial Cross Section
Regression Models / spregcs is a Stata Econometric Toolkit to Estimate
Spatial Cross / Sections Regression Models: /
(SAR-SEM-SDM-SAC-MSTAR-SPGMM-GS2SLS-GS2SLSAR-GS3SLS-GS3SLSAR /
spregdpd from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGDPD': module to estimate Spatial Panel Arellano-Bond Linear Dynamic
Regression: Lag & Durbin Models / spregdpd estimate Spatial Panel
Arellano-Bond Linear Dynamic / Regression Models for both Spatial Lag and
Durbin models / spregdpd calculates Spatial Autocorrelation, Non
spregfext from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGFEXT': module to compute Spatial Panel Fixed Effects Regression: Lag
and Durbin Models / spregfext estimates Spatial Panel Fixed Effects
Regression for / both Lag and Durbin Panel data Models and calculate /
Heteroscedasticity tests, Model Selection Diagnostic Criteria and /
spreghetxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGHETXT': module to Estimate Spatial Panel Random-Effects
Multiplicative Heteroscedasticity Regression: Lag and Durbin Models /
spreghetxt estimates Spatial Panel Random-Effects Multiplicative /
Heteroscedasticity Regression for both Lag and Durbin Panel data / Models
spregrext from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGREXT': module to compute Spatial Panel Random Effects Regression:
Lag and Durbin Models / spregfext estimates Spatial Panel Random Effects
Regression for / both Lag and Durbin Panel data Models / KW: Panel / KW:
Regression / KW: Spatial Lag Model / KW: Spatial Durbin Model / KW: Random
spregsac from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGSAC': module to estimate Maximum Likelihood Estimation
AutoCorrelation (SAC) Cross Section Regression / spregsac calculates
Spatial Autocorrelation, Non Normality, / Heteroscedasticity, and Total,
Direct, and InDirect Marginal / Effects and Elasticities, spregsac can fit
spregsacxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGSACXT': module to Estimate Maximum Likelihood Estimation Spatial
AutoCorrelation (SAC) Panel Regression / spregsacxt estimates Maximum
Likelihood Estimation Spatial / AutoCorrelation (SAC) Panel Regression
spregsar calculates / Spatial Autocorrelation, Non Normality,
spregsar from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGSAR': module to estimate Maximum Likelihood Estimation Spatial Lag
Cross Sections Regression / spregsar estimates Maximum Likelihood
Estimation Spatial Lag / Cross Sections Regression spregsar calculates
Spatial / Autocorrelation, Non Normality, Heteroscedasticity, and Total, /
spregsarxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGSARXT': module to Estimate Maximum Likelihood Estimation Spatial Lag
Panel Regression / spregsarxt estimates Maximum Likelihood Estimation
Spatial Lag / Panel Regression spregsar calculates Spatial
Autocorrelation, Non / Normality, Heteroscedasticity, and Total, Direct,
spregsem from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGSEM': Module to Estimate Maximum Likelihood Estimation Spatial Error
Cross Sections Regression / spregsem estimates Maximum Likelihood
Estimation Spatial Error / Cross Sections Regression. spregsem calculates
Spatial / Autocorrelation, Non Normality, Heteroscedasticity, and Total, /
spregsemxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGSEMXT': Module to Estimate Maximum Likelihood Estimation Spatial
Error Panel Regression / spregsemxt estimates Maximum Likelihood
Estimation Spatial Error / Panel Regression. spregsemxt calculates Spatial
Autocorrelation, / Non Normality, Heteroscedasticity, and Total, Direct,
spregxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPREGXT': Econometric Toolkit to Estimate Spatial Panel Regression Models
/ spregxt is a Stata Toolkit to estimate Spatial Panel Regression /
Models: (SAR-SEM-SDM-SAC-GWR-mSTAR-SPGMM-GS2SLS-Tobit) for panel / data
with be, fe, pa, re Effects, and calculate Panel / Autocorrelation, Panel
spseudor2 from http://fmwww.bc.edu/RePEc/bocode/s
'SPSEUDOR2': module to calculate goodness-of-fit measures in spatial
autoregressive models / spseudor2 calculates two so-called pseudo R2
measures to assess / goodness of fit in spatial autoregressive models
estimated by / spatial two stage least squares or spatial GMM. The first
spsfe from http://fmwww.bc.edu/RePEc/bocode/s
'SPSFE': module for estimation of spatial stochastic frontier models /
This software implements spatial stochastic frontier models that / address
both endogenous frontier/environmental variables and / spatial spillover
effects, based on the methodology developed in / Kutlu et al.
spsiv from http://fmwww.bc.edu/RePEc/bocode/s
'SPSIV': module to generate Synthetic Instrumental Variables (SIV) for
spatial regression / spsiv generates synthetic instrumental variables from
/ endogenous variables and spatial filters derived from a / symmetric
connectivity matrix. The synthetic instruments satisfy / the conditions of
sptobitmstar from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITMSTAR': Module to Estimate Tobit (m-STAR) Spatial Multiparametric
Spatio Temporal AutoRegressive Regression: Spatial Lag Cross Sections
Models / sptobitmstar estimates Tobit (m-STAR) Spatial Multiparametric /
Spatio Temporal AutoRegressive Regression for Spatial Lag Cross / Sections
sptobitmstard from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITMSTARD': Module to Estimate Tobit (m-STAR) Spatial Multiparametric
Spatio Temporal AutoRegressive Regression: Spatial Durbin Cross Sections
Models / sptobitmstard estimates Tobit (m-STAR) Spatial Multiparametric /
Spatio Temporal AutoRegressive Regression for Spatial Durbin / Cross
sptobitmstardhxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITMSTARDHXT': Module to Estimate Tobit (m-STAR) Spatial
Multiparametric Spatio Temporal AutoRegressive Regression: Spatial Durbin
Multiplicative Heteroscedasticity Panel Models / sptobitmstardhxt
estimates Tobit (m-STAR) Spatial / Multiparametric Spatio Temporal
sptobitmstarh from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITMSTARH': Module to Estimate Tobit (m-STAR) Spatial Multiparametric
Spatio Temporal AutoRegressive Regression: Spatial Lag Multiplicative
Heteroscedasticity Cross Sections Models / sptobitmstarh estimates Tobit
(m-STAR) Spatial Multiparametric / Spatio Temporal AutoRegressive
sptobitmstarhxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITMSTARHXT': module to estimate Tobit (m-STAR) Spatial
Multiparametric Spatio Temporal AutoRegressive Regression: Spatial Lag
Multiplicative Heteroscedasticity Panel Models / sptobitmstarxt estimates
Tobit (m-STAR) Spatial Multiparametric / Spatio Temporal AutoRegressive
sptobitmstarxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITMSTARXT': module to estimate Tobit (m-STAR) Spatial
Multiparametric Spatio Temporal AutoRegressive Regression: Spatial Lag
Panel Models / sptobitmstarxt estimates Tobit (m-STAR) Spatial
Multiparametric / Spatio Temporal AutoRegressive Regression for Spatial
sptobitsac from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITSAC': module to Estimate Tobit MLE Spatial Autocorrelation Cross
Sections Regression / sptobitsac estimates Tobit MLE Spatial
AutoCorrelation (SAC) / Cross Sections Regression, and calculates Spatial
/ Autocorrelation, Non Normality, Heteroscedasticity, and Total, / Direct,
sptobitsacxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITSACXT': module to estimate Tobit MLE Spatial AutoCorrelation (SAC)
Panel Regression / spregsacxt estimates Tobit MLE Spatial AutoCorrelation
(SAC) / Panel Regression and calculates Spatial Autocorrelation, Non /
Normality, Heteroscedasticity, and Total, Direct, and InDirect / Marginal
sptobitsar from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITSAR': module to Estimate Tobit MLE Spatial Lag Cross Sections
Regression / sptobitsar estimates Tobit Maximum Likelihood Estimation
Spatial / Lag Cross Sections Regression, and calculates Spatial /
Autocorrelation, Non Normality, Heteroscedasticity, and Total, / Direct,
sptobitsarxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITSARXT': module to estimate Tobit MLE Spatial Lag Panel Regression
/ sptobitsarxt estimates Tobit MLE Spatial Lag Panel Regression / and
calculates Spatial Autocorrelation, Non Normality, / Heteroscedasticity,
and Total, Direct, and InDirect Marginal / Effects and Elasticities / KW:
sptobitsdm from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITSDM': module to Estimate Tobit MLE Spatial Durbin Cross Sections
Regression / sptobitsdm estimates Tobit MLE Spatial Durbin Cross Sections
/ Regression, and calculates Spatial Autocorrelation, Non / Normality,
Heteroscedasticity, and Total, Direct, and InDirect / Marginal Effects and
sptobitsdmxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITSDMXT': module to estimate Tobit MLE Spatial Panel Durbin
Regression / sptobitsdmxt estimates Tobit MLE Spatial Panel Durbin
Regression / and calculates Spatial Autocorrelation, Non Normality, /
Heteroscedasticity, and Total, Direct, and InDirect Marginal / Effects and
sptobitsem from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITSEM': module to Estimate Tobit MLE Spatial Error Cross Sections
Regression / sptobitsem estimates Tobit MLE Spatial Error Cross Sections /
Regression and calculates Spatial Autocorrelation, Non Normality, /
Heteroscedasticity, and Total, Direct, and InDirect Marginal / Effects and
sptobitsemxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPTOBITSEMXT': module to estimate Tobit MLE Spatial Error Panel
Regression / sptobitsemxt module to estimate Tobit MLE Spatial Error Panel
/ Regression and calculates Spatial Autocorrelation, Non Normality, /
Heteroscedasticity, and Total, Direct, and InDirect Marginal / Effects and
spweight from http://fmwww.bc.edu/RePEc/bocode/s
'SPWEIGHT': module to compute Cross Section Spatial Weight Matrix /
spweight computes Design Cross Section Spatial Weight Matrix / among
neighbor locations, for using in cross section spatial / regression
analysis / KW: spatial / KW: weight matrix / KW: cross section / Requires:
spweightcs from http://fmwww.bc.edu/RePEc/bocode/s
'SPWEIGHTCS': module to compute Cross Section Spatial Weight Matrix /
spweightcs creates or generates cross section spatial weight / matrix
among neighbor locations, for using in cross section / spatial regression
analysis / KW: spatial / KW: weight matrix / KW: cross section / Requires:
spweightxt from http://fmwww.bc.edu/RePEc/bocode/s
'SPWEIGHTXT': module to compute Panel Spatial Weight Matrix / spweightxt
creates or generates panel spatial weight matrix / among neighbor
locations, for using in panel spatial regression / analysis / KW: spatial
/ KW: weight matrix / KW: panel data / Requires: Stata version 10 /
spxttobit from http://fmwww.bc.edu/RePEc/bocode/s
'SPXTTOBIT': module to estimate Tobit Spatial Panel Autoregressive
Generalized Least Squares Regression / spxttobit estimates Tobit Spatial
Panel Autoregressive / Generalized Least Squares Regression / KW:
regression / KW: tobit / KW: spatial / KW: panel / KW: Spatial Panel
sqmc from http://fmwww.bc.edu/RePEc/bocode/s
'SQMC': module to compute squared multiple correlation / sqmc computes the
variance each variable shares with the / remaining variables in a
correlation matrix. Each variable is / regressed on the remaining
variables using OLS and the resulting / R-square values would represent
srslogit from http://fmwww.bc.edu/RePEc/bocode/s
'SRSLOGIT': module to perform Logit regression with secondary ridit
splines / srslogit fits a primary logit model for a binary / dependent
variable in a list of independent variables, / followed by a secondary
ridit spline model for the same / binary dependent variable in the ridit
srtree from http://fmwww.bc.edu/RePEc/bocode/s
'SRTREE': module to implement regression trees via optimal pruning,
bagging, random forests, and boosting methods / srtree is a Stata wrapper
for the R functions "tree()", / "randomForest()", and "gbm()". It allows
to implement the / following regression tree models: (1) regression tree
ssaggregate from http://fmwww.bc.edu/RePEc/bocode/s
'SSAGGREGATE': module to create shock-level aggregates for shift-share IV
/ Converts variables of a shift-share IV dataset into a dataset of /
weighted shock-level aggregates, as described in Borusyak, Hull, / and
Jaravel (2019), to implement quasi-experimental shift-share / (Bartik)
sslope from http://fmwww.bc.edu/RePEc/bocode/s
'SSLOPE': module to calculate slope coefficients for regression
interactions / This routine calculates simple slopes for interactions
between / continuous variables in regression analysis. / KW: slopes / KW:
marginal effects / KW: interactions / Requires: Stata version 8 /
sspecialreg from http://fmwww.bc.edu/RePEc/bocode/s
'SSPECIALREG': module to estimate binary choice model with discrete
endogenous regressor via special regressor method / sspecialreg estimates
a binary choice model that includes one or / more endogenous regressors
using Lewbel and Dong's special / regressor method (Econometric Reviews,
stackdid from http://fmwww.bc.edu/RePEc/bocode/s
'STACKDID': module to estimate Stacked Difference-in-Differences
Regression / stackdid performs a stacked difference-in-differences
regression / for staggered treatment settings, as described in Gormley and
/ Matsa (2011). It offers three primary advantages compared to a /
stackreg from http://fmwww.bc.edu/RePEc/bocode/s
'STACKREG': module to perform stacked linear regression analysis to
facilitate testing of multiple hypotheses / stackreg implements the
stacked regression analysis which / facilitates statistical testing in a
multiple testing framework. / The stacked regression approach was
staticfc from http://fmwww.bc.edu/RePEc/bocode/s
'STATICFC': module to compute static forecasts for a recursive rolling
regression / staticfc runs a specified linear regression on a recursive /
rolling sample: that is, on a sequence of estimation periods /
successively including each additional period. It then generates / an
stbrier from http://fmwww.bc.edu/RePEc/bocode/s
'STBRIER': module to compute Brier score for censored time-to-event
(survival) data / stbrier computes the Brier score for risk prediction
models / in survival analysis based on right censored data by weighting /
individuals by their inverse probability of being uncensored / [Graf et
stbtcalc from http://fmwww.bc.edu/RePEc/bocode/s
'STBTCALC': module to calculate time-varying regression coefficients in
Cox PH models / stbtcalc calculates local estimates of the regression /
coefficients in the Cox model defined by stset with predictors in /
varlist. The estimates are calculated within moving windows which /
stcompadj from http://fmwww.bc.edu/RePEc/bocode/s
'STCOMPADJ': module to estimate the covariate-adjusted cumulative
incidence function in the presence of competing risks / stcompadj
estimates the adjusted cumulative incidence function / based on a Cox or a
flexible parametric regression model in the / presence of competing risks.
stcstat from http://fmwww.bc.edu/RePEc/bocode/s
'STCSTAT': module to generate evaluation of fit for Cox regression model /
Frank Harrell describes a c-statistic that can be computed by / comparing
the predictions generated from a Cox model with the / observed survival
time, 'Draw a pair of patients and determine / which patient lived longer
stcumh from http://fmwww.bc.edu/RePEc/bocode/s
'STCUMH': module to check proportional hazards assumption / stcumh plots
estimates of the cumulative baseline hazards for / two strata in stratavar
against each other. The slope of the / plotted graph will (at any time
point) approximately be the / relative risk between the two strata. If the
stgtcalc from http://fmwww.bc.edu/RePEc/bocode/s
'STGTCALC': module to calculate time-varying regression coefficients in
Cox PH models (variant) / stgtcalc calculates local estimates of the
regression / coefficients in the Cox model most recently fitted for using
/ stcox. stgtplot plots the estimates and confidence intervals / against
stipw from http://fmwww.bc.edu/RePEc/bocode/s
'STIPW': module to estimate inverse probability weighted parametric
survival models with variance obtained via M-estimation / stipw performs
an inverse probability weighted analysis on / survival data. It begins by
using logistic regression to model / the treatment/exposure variable
stpm2cr from http://fmwww.bc.edu/RePEc/bocode/s
'STPM2CR': module to estimate flexible parametric competing risks
regression models / stpm2cr fits competing risks flexible parametric
regression / models (Royston-Parmar models) by directly modelling the /
cumulative incidence function. stpm2cr can be used with single- / or
subset from http://fmwww.bc.edu/RePEc/bocode/s
'SUBSET': module to implement best covariates and stepwise subset
selection / subset is a Stata wrapper for the R function "regsubsets()", /
providing "best", "backward", and "forward" stepwise subset / covariates
selection, a Machine Learning approach to select the / optimal number of
subsetbyvif from http://fmwww.bc.edu/RePEc/bocode/s
'SUBSETBYVIF': module to select a subset of covariates constrained by VIF
/ subsetByVIF selects subsets of the covariates listed in varlist / such
that each covariate in a given subset has a VIF that is less / than or
equal to a specified value given by viflist. We are / frequently faced
supsmooth from http://fmwww.bc.edu/RePEc/bocode/s
'SUPSMOOTH': module to perform Friedman's super smoother / supsmooth is an
implementation of a bivariate regression / smoother based on local linear
regression with adaptive / bandwidths. This method is known as Friedman's
super / smoother. / KW: local regression / KW: nonparametric / KW:
suregr from http://fmwww.bc.edu/RePEc/bocode/s
'SUREGR': module to calculate robust, or cluster-robust variance after
sureg / Stata's sureg fits seemingly unrelated regressions by /
generalised least squares. However sureg cannot estimate robust / or
cluster-robust variance matrix and standard errors for the / parameter
surloads from http://fmwww.bc.edu/RePEc/bocode/s
'SURLOADS': module to calculate simple scores / surloads provides an
alternative to calculating factor scores by / the regression or Bartlett's
method if the researcher is not / interested in the uniqueness factor of a
variable. Given the / rotated loading matrix of a factor analysis, the
svmachines from http://fmwww.bc.edu/RePEc/bocode/s
'SVMACHINES': module providing Support Vector Machines for both
Classification and Regression / svmachines fits a support vector machine
(SVM) model. SVM is / not one, but several, variant models each based
upon the / principles of splitting hyperplanes and the culling of /
svr from http://fmwww.bc.edu/RePEc/bocode/s
'SVR': module to compute estimates with survey replication (SVR) based
standard errors / The prefix svr stands for SurVey Replication, and refers
to / commands that analyze complex survey data using replication /
methods. The available methods are balanced repeated replication / (BRR),
svypxcat from http://fmwww.bc.edu/RePEc/bocode/s
'SVYPXCAT': module to calculate predicted means or proportions for nominal
X's for survey data / svypxcat calculates and optionally graphs means from
linear / regression models or proportions from logistic regression /
models corrected for the survey sampling scheme (weights) for / one or two
swboot from http://fmwww.bc.edu/RePEc/bocode/s
'SWBOOT': module to bootstrap stepwise linear or logistic regression
models / swboot uses bootstrap samples of size N (based on number of /
observations without missing values) to validate the choice of / variables
in stepwise procedures for linear or logistic / regression; variables
switchoprobit from http://fmwww.bc.edu/RePEc/bocode/s
'SWITCHOPROBIT': module to estimate a switching regression for a binary
endogenous treatment and ordered outcome / switchoprobit estimates a
switching regression for a binary / endogenous treatment and ordered
outcome. / KW: treatment / KW: ordered probit / KW: endogenous treatment
switchoprobitsim from http://fmwww.bc.edu/RePEc/bocode/s
'SWITCHOPROBITSIM': module to estimate a switching regression for a binary
endogenous treatment and ordered outcome using a latent factor structure /
switchoprobitsim estimates a switching regression for a binary /
endogenous treatment and ordered outcome using a latent factor /
switchr from http://fmwww.bc.edu/RePEc/bocode/s
'SWITCHR': module to estimate switching regression models / This revised
version of switchr calculates switching regression / estimates for a
two-component model in which observations are / drawn from two different
regression regimes (or components), but / separation into the separate
tau2ci from http://fmwww.bc.edu/RePEc/bocode/t
'TAU2CI': module to compute standard error and confidence interval for the
tau-squared statistic in random-effects meta-analysis / tau2ci is a
post-estimation command that computes the standard / error and confidence
interval for the between-study variance / component tau-squared statistic
ted from http://fmwww.bc.edu/RePEc/bocode/t
'TED': module to test Stability of Regression Discontinuity Models / ted
estimates the "local average treatment effect" (LATE), / the "compliers'
probabilty discontinuity" (CPD), and / "treatment effect derivative" (TED)
for either sharp or fuzzy / Regression Discontinuity (RD) models.
teffects2 from http://fmwww.bc.edu/RePEc/bocode/t
'TEFFECTS2': module to estimate average treatment effects with
observational data / teffects2 estimates average treatment effects (ATEs)
and / average treatment effects on the treated (ATTs) using /
observational data. As in Stata's official teffects command, / inverse
tgmixed from http://fmwww.bc.edu/RePEc/bocode/t
'TGMIXED': module to perform Theil-Goldberger mixed estimation of
regression equation / tgmixed estimates a regression equation subject to
stochastic / linear constraints, using the Theil-Goldberger (1961) mixed /
estimation technique. This estimator is a generalization of / cnsreg,
theilr2 from http://fmwww.bc.edu/RePEc/bocode/t
'THEILR2': module to compute Theil R2 Multicollinearity Effect / theilr2
computes Theil R2 Multicollinearity Effect / KW: regression / KW: Theil R2
Multicollinearity Effect / KW: collinearity / Requires: Stata version 10 /
Distribution-Date: 20120208 / Author: Emad Abd Elmessih Shehata,
thsearch from http://fmwww.bc.edu/RePEc/bocode/t
'THSEARCH': module to evaluate threshold search model for non-linear
models based on information criterion / thsearch implements the threshold
search model based on / information criterion for optimal threshold model
selection. / See Gannon, Harris and Harris (Health Econ., 2014) for /
tobithetm from http://fmwww.bc.edu/RePEc/bocode/t
'TOBITHETM': module to estimate Tobit Multiplicative Heteroscedasticity
Regression / tobithetm fits MLE for Tobit Multiplicative
Heteroscedasticity / Regression / KW: tobit / KW: Heteroscedasticity /
Requires: Stata version 10 / Distribution-Date: 20111114 / Author: Emad
tpm from http://fmwww.bc.edu/RePEc/bocode/t
'TPM': module to estimate two-part cross-sectional models / tpm fits
two-part regression cross-sectional models. The first / part models the
probability that depvar>0 using a binary choice / model (logit or probit).
The second part models the distribution / of depvar | depvar>0 using
tpoisson from http://fmwww.bc.edu/RePEc/bocode/t
'TPOISSON': module to estimate truncated Poisson regression / tpoisson
fits a truncated Poisson maximum-likelihood regression / of depvar on
indepvars, where depvar is a non-negative count / variable. The trunc
option is required. If no observations are / truncated, a trunc variable
translog from http://fmwww.bc.edu/RePEc/bocode/t
'TRANSLOG': module to create new variables for a translog function /
translog generates new variables for a translog function with / the
specified variables. The translog function form is widely / applied in
empirical studies for it is regarded as the second / order approximation
treeplot from http://fmwww.bc.edu/RePEc/bocode/t
'TREEPLOT': module to graph a tree in Stata / treeplot is a command for
graphing a regression or / classification tree in Stata 16. It uses the
Stata/Python / integration (sfi) capability of Stata 16 making use of the
Python / Scikit-learn API. / KW: regression tree / KW: classification
trnbin0 from http://fmwww.bc.edu/RePEc/bocode/t
'TRNBIN0': module to estimate zero-truncated negative binomial regression
/ A 0-truncated negative binomial model is appropriate when / modeling
count data which have no possibility of having 0 / values. This is to be
distinguished from data sets without 0 / values, but which may have 0's.
trpois0 from http://fmwww.bc.edu/RePEc/bocode/t
'TRPOIS0': module to estimate zero-truncated Poisson regression models /
trpois0 estimates maximum likelihood zero-truncated Poisson / regression
models using Stata's ml method for estimation. A / 0-truncated Poisson
model is appropriate when modeling count / data which does not have values
tryem from http://fmwww.bc.edu/RePEc/bocode/t
'TRYEM': module to run all possible subset regressions / tryem calculates
all possible regressions of a given subset / size. / Author: Al Feiveson,
Johnson Spaceflight Center / Support: email alan.h.feiveson1@jsc.nasa.gov
/ Distribution-Date: 20120617
tscb from http://fmwww.bc.edu/RePEc/bocode/t
'TSCB': module to implement the two-stage cluster bootstrap estimator /
This package implements the two-stage cluster bootstrap (TSCB) / estimator
described in Abadie et al., ("When Should You Adjust / Standard Errors for
Clustering?", QJE, 2023). The TSCB estimator / allows for the calculation
tslstarmod from http://fmwww.bc.edu/RePEc/bocode/t
'TSLSTARMOD': module to estimate a Logistic Smooth Transition
Autoregressive Regression (LSTAR) Model for Time Series Data / tslstarmod
performs an estimation of a logistic smooth / transition autoregressive
regression (LSTAR) model for time / series data. This command allows
tteir from http://fmwww.bc.edu/RePEc/bocode/t
'TTEIR': module to prepare time-to-event data for incidence rates / The
command tteir is a wrapper for the use of stset, stsplit, / and collapse
as described in sections 4.3 to 4.5 in Royston / and Lambert (2011) to
prepare time-to-event datasets for poisson / regressions with piecewise
twc_inf from http://fmwww.bc.edu/RePEc/bocode/t
'TWC_INF': module to implement the twoway-clustered inference tools of
"Analytic inference with two-way clustering" by Davezies et al. (wp, 2025)
/ twc_inf fits a regression model of depvar on indepvars using / the model
specified by method(). For each regression / coefficient, the command
twexp from http://fmwww.bc.edu/RePEc/bocode/t
'TWEXP': module to estimate exponential-regression models with two-way
fixed effects / twexp computes the method-of-moment estimators from
Jochmans / (2017) for estimating exponential-regression models with
two-way / fixed effects from balanced panel data. / KW: panel / KW:
twfe from http://fmwww.bc.edu/RePEc/bocode/t
'TWFE': module to perform regressions with two-way fixed effects or match
effects for large datasets / twfe fits a linear regression model of depvar
on indepvars / including fixed effects for the units defined by
id1(varname) and / id2(varname). If matcheffect is specified, fixed
twgravity from http://fmwww.bc.edu/RePEc/bocode/t
'TWGRAVITY': module to estimate exponential-regression models with two-way
fixed effects from a cross-section of data on dyadic interactions /
twgravity computes the method-of-moment estimators from Jochmans / (2017)
for estimating exponential-regression models with two-way / fixed effects
twopm from http://fmwww.bc.edu/RePEc/bocode/t
'TWOPM': module to estimate two-part models / twopm fits two-part models
for mixed discrete-continuous / outcomes. In the two-part model, a binary
choice model is fit for / the probability of observing a
positive-versus-zero outcome. / Then, conditional on a positive outcome,
twoway_estfit from http://fmwww.bc.edu/RePEc/bocode/t
'TWOWAY_ESTFIT': module to enable graph twoway estfit / estfit is a
modified copy of graph twoway lfit that allows you to / specify any Stata
estimation command instead of the default / regress built into lfit (or
the other defaults of qfit, fpfit, / etc.). For example, you can specify
twowayfeweights from http://fmwww.bc.edu/RePEc/bocode/t
'TWOWAYFEWEIGHTS': module to estimate the weights and measure of
robustness to treatment effect heterogeneity attached to two-way fixed
effects regressions / twowayfeweights estimates the weights and the
measure of / robustness to treatment effect heterogeneity attached to the
ueve from http://fmwww.bc.edu/RePEc/bocode/u
'UEVE': module to compute unbiased errors-in-variables estimator and
variants from grouped data / ueve fits a linear regression using one of
three estimators for / grouped data: Devereux (2007) errors-in-variables
estimator that / is approximately unbiased (UEVE); Deaton (1985) /
uirt from http://fmwww.bc.edu/RePEc/bocode/u
'UIRT': module to fit unidimensional Item Response Theory models / uirt is
a Stata module for estimating variety of unidimensional / IRT models
(2PLM, 3PLM, GRM, PCM, GPCM). It features multi-group / modelling, DIF
analysis, item-fit analysis and generating / plausible values (PVs)
underid from http://fmwww.bc.edu/RePEc/bocode/u
'UNDERID': module producing postestimation tests of under- and
over-identification after linear IV estimation / underid reports tests of
underidentification and / overidentification after estimation of
single-equation linear / instrumental variables (IV) models, including
usos from http://fmwww.bc.edu/RePEc/bocode/u
'USOS': module for computing the unweighted sum of squares test for global
goodness of fit after logistic model / usos is a post-estimation (estat)
command following / logistic/logit regression that implements the
unweighted sum of / squared errors (USOS) test for global goodness of fit
var_nr from http://fmwww.bc.edu/RePEc/bocode/v
'VAR_NR': module to estimate set identified SVARS / The toolbox var_nr
allows for the estimation of set identified / SVARS in Stata using sign
and narrative restrictions. The suite / is able to produce impulse
responses functions, forecast error / variance decompositions, and
varlag from http://fmwww.bc.edu/RePEc/bocode/v
'VARLAG': module to determine the appropriate lag length in VARs, ECMs /
varlag reports various statistics that are meant to help select / the
proper lag structure to use in the estimation of Vector / autoregressions
(VARs) and Error Correction Models (ECMs). For / each lag length, varlag
vc_pack from http://fmwww.bc.edu/RePEc/bocode/v
'VC_PACK': module for the estimation of smooth varying coefficient models
/ Nonparametric regressions are powerful statistical tools to / model
relationships between dependent and independent variables / with minimal
assumptions on the underlying functional forms. / However, the added
vce_mcov from http://fmwww.bc.edu/RePEc/bocode/v
'VCE_MCOV': module to compute the Leave-Cluster-Out-Crossfit (LCOC)
variance estimates for user-chosen coefficients in a linear regression
model. / vce_mcov is an eclass command that can be used after running /
reg. It replaces the entries of the variance matrix (stored in / e(V))
vecar from http://fmwww.bc.edu/RePEc/bocode/v
'VECAR': module to estimate vector autoregressive (VAR) models / vecar
estimates vector autoregression (VAR) models. Each of the / variables in
depvarlist is regressed on maxlag lags of / depvarlist, a constant (unless
suppressed) and the exogenous / variables provided in varlist (if any).
vecar6 from http://fmwww.bc.edu/RePEc/bocode/v
'VECAR6': module to estimate vector autoregressive (VAR) models (version
6) / vecar6 estimates vector autoregression (VAR) models. Version 7 /
users should use vecar (q.v.) / Distribution-Date: 20020604 / Author:
Christopher F Baum, Boston College / Support: email baum@bc.edu / Author:
vselect from http://fmwww.bc.edu/RePEc/bocode/v
'VSELECT': module to perform linear regression variable selection /
vselect performs variable selection for linear regression. / Through the
use of the Furnival-Wilson leaps-and-bounds / algorithm, all-subsets
variable selection is supported. The / stepwise methods, forward
wcbregress from http://fmwww.bc.edu/RePEc/bocode/w
'WCBREGRESS': module to estimate a Linear Regression Model with Clustered
Errors Using the Wild Cluster Bootstrap Standard Errors / wcbregress
estimates a linear regression model with clustered / errors and provides
accurate inference either when cluster number / is large or small, using
wclogit from http://fmwww.bc.edu/RePEc/bocode/w
'WCLOGIT': module to perform conditional logistic regression with
within-group varying weights / This command maximises the partial
log-likelihood for conditional / logistic regression with weights that can
vary within the matched / set defined by the group option. In calculations
wdireshape from http://fmwww.bc.edu/RePEc/bocode/w
'WDIRESHAPE': module to reshape World Development Indicators database /
wdireshape takes a World Development Indicators (WDI) dataset as /
downloaded from the World Bank's web site or as exported from the / WDI
CD-ROM software and reshapes it into a structure suitable / for panel data
weakiv from http://fmwww.bc.edu/RePEc/bocode/w
'WEAKIV': module to perform weak-instrument-robust tests and confidence
intervals for instrumental-variable (IV) estimation of linear, probit and
tobit models / weakiv calculates weak-instrument-robust tests of the /
coefficients on the endogenous regressors in instrumental / variables (IV)
weakiv10 from http://fmwww.bc.edu/RePEc/bocode/w
'WEAKIV10': module to perform weak-instrument-robust tests and confidence
intervals for instrumental-variable (IV) estimation of linear, probit and
tobit models / weakiv10 calculates weak-instrument-robust tests of the /
coefficients on the endogenous regressors in instrumental / variables (IV)
weakivtest from http://fmwww.bc.edu/RePEc/bocode/w
'WEAKIVTEST': module to perform weak instrument test for a single
endogenous regressor in TSLS and LIML / weakivtest implements the weak
instrument test of Montiel Olea / and Pflueger (J.Bus.Ec.Stat., 2013) that
is robust to / heteroskedasticity, serial correlation, and clustering. /
weakivtest2 from http://fmwww.bc.edu/RePEc/bocode/w
'WEAKIVTEST2': module to compute Robust Test for Weak Instruments with
Multiple Endogenous Regressors / weakivtest2 implements the robust
bias-based test for weak / instruments for two-stage least squares (2SLS)
with multiple / endogenous regressors proposed by Lewis and Mertens (2024,
wgttest from http://fmwww.bc.edu/RePEc/bocode/w
'WGTTEST': module to test the impact of sampling weights in regression
analysis / wgttest performs a test proposed by DuMouchel and Duncan (1983)
/ to evaluate whether the weighted and unweighted estimates of a /
regression model are significantly different. / KW: weights / KW:
white from http://fmwww.bc.edu/RePEc/bocode/w
'WHITE': module to perform White's test for heteroscedasticity / htest,
szroeter, and white provide tests for the assumption of / the linear
regression model that the residuals e are / homoscedastic, i.e., have
constant variance. The tests differ / with respect to the specification of
whitetst from http://fmwww.bc.edu/RePEc/bocode/w
'WHITETST': module to perform White's test for heteroskedasticity /
whitetst computes White's test for heteroskedasticity following / regress
or cnsreg. This test is a special case of the / Breusch-Pagan test (q.v.
bpagan). The White test does not require / specification of a list of
whotdeck from http://fmwww.bc.edu/RePEc/bocode/w
'WHOTDECK': module to perform multiple imputation using the Approximate
Bayesian Bootstrap with weights / whotdeck will tabulate the missing data
patterns within the / varlist. A row of data with missing values in any of
the / variables in the varlist is defined as a `missing line' of data, /
williams from http://fmwww.bc.edu/RePEc/bocode/w
'WILLIAMS': module to estimate logistic regression via Williams procedure
/ The Williams procedure, originally written in GLIM, helps / accomodate
for overdispersion in binomial (proportional) models. / It is not a post
facto sort of adjustment; rather there is an / adjustment made to the
wooldid from http://fmwww.bc.edu/RePEc/bocode/w
'WOOLDID': module to estimate Difference-in-Differences Treatment Effects
with Staggered Treatment Onset Using Heterogeneity-Robust Two-Way Fixed
Effects Regressions / wooldid offers a set of tools for implementing /
difference-in-differences style analyses with staggered treatment / onset
xsmle from http://fmwww.bc.edu/RePEc/bocode/x
'XSMLE': module for spatial panel data models estimation / Econometricians
have begun to devote more attention to spatial / interactions when
carrying out applied econometric studies. We / provide the new Stata
command -xsmle-, which fits fixed and / random-effects spatial models for
xtabond2 from http://fmwww.bc.edu/RePEc/bocode/x
'XTABOND2': module to extend xtabond dynamic panel data estimator /
xtabond2 can fit two closely related dynamic panel data / models. The
first is the Arellano-Bond (1991) estimator, which / is also available
with xtabond without the two-step / finite-sample correction described
xtavplot from http://fmwww.bc.edu/RePEc/bocode/x
'XTAVPLOT': module to produce added-variable plots for panel data
estimation / xtavplot creates an added-variable plot (a.k.a. /
partial-regression leverage plot, partial regression plot, or / adjusted
partial residual plot) after xtreg, fe (fixed-effects / estimation),
xtcce from http://fmwww.bc.edu/RePEc/bocode/x
'XTCCE': module to implement the Common Correlated Effects estimator /
xtcce is a Stata command that implements the Pesaran (2006) / Common
Correlated Effects estimator ('CCE') for static panel data / models with
strictly exogenous regressors, the Chudik and Pesaran / (2015) Dynamic CCE
xtcointreg from http://fmwww.bc.edu/RePEc/bocode/x
'XTCOINTREG': module for panel data generalization of cointegration
regression using fully modified ordinary least squares, dynamic ordinary
least squares, and canonical correlation regression methods / xtcointreg
generalizes Qunyong Wang and Na Wu's cointreg / command to panel data. It
xtcspqardl from http://fmwww.bc.edu/RePEc/bocode/x
'XTCSPQARDL': module to perform Cross-Sectionally Augmented Panel Quantile
ARDL, Quantile CCE Mean Group, and Quantile CCE Pooled Mean Group
Estimation / xtcspqardl implements two estimators for dynamic /
heterogeneous panel data models with cross-sectional / dependence at the
xtdhazard from http://fmwww.bc.edu/RePEc/bocode/x
'XTDHAZARD': module to perform Own-Differences IV/CF Estimation of the
Discrete-Time Hazard Model / xtdhazard implements the linear (first)
differences instrumental / variables estimator, suggested by Farbmacher &
Tauchmann (2023) / for dealing with unit-level unobserved heterogeneity
xtdpdbc from http://fmwww.bc.edu/RePEc/bocode/x
'XTDPDBC': module to perform bias-corrected estimation of linear dynamic
panel data models / xtdpdbc implements a bias-corrected method-of-moments
estimator / for linear dynamic panel data models with fixed or random /
effects. Higher-order autoregressive models and unbalanced panel / data
xtdpdml from http://fmwww.bc.edu/RePEc/bocode/x
'XTDPDML': module to estimate Dynamic Panel Data Models using Maximum
Likelihood / Panel data make it possible both to control for unobserved /
confounders and to include lagged, endogenous regressors. Trying / to do
both at the same time, however, leads to serious estimation /
xtdpdserial from http://fmwww.bc.edu/RePEc/bocode/x
'XTDPDSERIAL': module to perform panel data serial correlation tests /
xtdpdserial implements serial correlation tests for linear / panel data
models that fall into the framework of the / portmanteau test developed by
Jochmans (2020). Special cases are / the Arellano and Bond (1991) and
xtendothresdpd from http://fmwww.bc.edu/RePEc/bocode/x
'XTENDOTHRESDPD': module to estimate a Dynamic Panel Data Threshold
Effects Model with Endogenous Regressors / xtendothresdpd performs
estimations of a dynamic panel data / threshold effects model with
endogenous regressors. If we have a / panel data model which is dynamic,
xtewreg from http://fmwww.bc.edu/RePEc/bocode/x
'XTEWREG': module to estimate errors-in-variable model with mismeasured
regressors / xtewreg runs an errors-in-variables regression, with
arbitrarily / many mismeasured and perfectly measured regressors. It uses
/ either the higher-order cumulant estimators from Erickson, Jiang, / and
xtfeis from http://fmwww.bc.edu/RePEc/bocode/x
'XTFEIS': module to estimate linear Fixed-Effects model with
Individual-specific Slopes (FEIS) / The module provides Stata command
xtfeis to estimate linear / Fixed-Effects models with Individual-specific
Slopes (FEIS). It / also provides commands to compute two versions of the
xtfmb from http://fmwww.bc.edu/RePEc/bocode/x
'XTFMB': module to execute Fama-MacBeth two-step panel regression / xtfmb
is an implementation of the Fama and MacBeth (J. Polit. / Econ. 1973) two
step procedure. The procedure is as follows: In / the first step, for each
single time period a cross-sectional / regression is performed. Then, in
xtglsr from http://fmwww.bc.edu/RePEc/bocode/x
'XTGLSR': module to calculate robust, or cluster-robust variance after
xtgls / Stata's [XT] xtgls fits panel-data models by using GLS estimates /
panel data models by generalised least squares. However xtgls / cannot
estimate robust or cluster-robust variance matrix and / standard errors
xtgps from http://fmwww.bc.edu/RePEc/bocode/x
'XTGPS': module to estimate Hoechle, Schmid, and Zimmermann's (2024) GPS
regression model for analyzing asset returns / xtgps estimates the GPS
regression model proposed by Hoechle, / Schmid, and Zimmermann (2024) in
their working paper "Does / Unobservable Heterogeneity Matter for
xthrtest from http://fmwww.bc.edu/RePEc/bocode/x
'XTHRTEST': module to perform Born & Breitung Bias-corrected HR-test for
first order panel serial correlation / xthrtest implements the
heteroscedasticity robust HR-test for / panel serial correlation
introduced in Born & Breitung / (Econometric Reviews, 2016). The test is
xthst from http://fmwww.bc.edu/RePEc/bocode/x
'XTHST': module to test slope homogeneity in large panels / xthst performs
a test of slope homogeneity in panels with a / large number observations
of the cross-sectional (N) and time (T) / dimension. The test is based on
Pesaran, Yamagata (2008, Journal / of Econometrics) and Blomquist,
xtistest from http://fmwww.bc.edu/RePEc/bocode/x
'XTISTEST': module to perform Portmanteau test for panel serial
correlation / xtistest implements the Portmanteau IS-test for panel serial
/ correlation introduced in Inoue & Solon (ET, 2006). The test is /
suited only for fixed effects regressions and can handle any sort / of
xtivreg2 from http://fmwww.bc.edu/RePEc/bocode/x
'XTIVREG2': module to perform extended IV/2SLS, GMM and AC/HAC, LIML and
k-class regression for panel data models / xtivreg2 implements IV/GMM
estimation of the fixed-effects and / first-differences panel data models
with possibly endogenous / regressors. It is essentially a wrapper for
xtivreg28 from http://fmwww.bc.edu/RePEc/bocode/x
'XTIVREG28': module to perform extended IV/2SLS, GMM and AC/HAC, LIML and
k-class regression for panel data models (version 8) / xtivreg28
implements IV/GMM estimation of the fixed-effects and / first-differences
panel data models with possibly endogenous / regressors. It is
xtloglin from http://fmwww.bc.edu/RePEc/bocode/x
'XTLOGLIN': module to perform robust Lagrange multiplier test of linear
and log-linear models against Box-Cox alternatives after regress or xtreg
/ xtloglin implements a Lagrange multiplier test for testing / the null of
linear and log-linear regression models against / Box-Cox alternatives.
xtlsdvc from http://fmwww.bc.edu/RePEc/bocode/x
'XTLSDVC': module to estimate bias corrected LSDV dynamic panel data
models / xtlsdvc calculates bias corrected LSDV estimators for the /
standard autoregressive panel data model using the bias / approximations
in Bruno (2005a), who extends the results by Bun / and Kiviet (2003),
xtmg from http://fmwww.bc.edu/RePEc/bocode/x
'XTMG': module to estimate panel time series models with heterogeneous
slopes / The xtmg command implements three estimators from the recent /
panel time series literature which allow for heterogeneous / slopes across
panel units: the Pesaran and Smith (1995) Mean / Group estimator, the
xtmixed_corr from http://fmwww.bc.edu/RePEc/bocode/x
'XTMIXED_CORR': module to compute model-implied intracluster correlations
after xtmixed / Linear mixed models as fit by xtmixed have complex
expressions / for intracluster correlation. Correlation comes from two /
sources: (1) the design of the random effects and their assumed /
xtmod from http://fmwww.bc.edu/RePEc/bocode/x
'XTMOD': module to analyze and display interactions based on time-series
data / xtmod helps running multiple moderated regressions. It generates /
the necessary interaction variables, calculates the coefficients, /
optionally displays tests, and displays the interaction. It is / mainly a
xtnptimevar from http://fmwww.bc.edu/RePEc/bocode/x
'XTNPTIMEVAR': module to estimate non-parametric time-varying coefficients
panel data models with fixed effects / xtnptimevar performs estimations of
non-parametric time-varying / coefficients panel data models with fixed
effects. Often / researchers desire to estimate the effects of some
xtoverid from http://fmwww.bc.edu/RePEc/bocode/x
'XTOVERID': module to calculate tests of overidentifying restrictions
after xtreg, xtivreg, xtivreg2, xthtaylor / xtoverid computes versions of
a test of overidentifying / restrictions (orthogonality conditions) for a
panel data / estimation. For an instrumental variables estimation, this
xtpedroni from http://fmwww.bc.edu/RePEc/bocode/x
'XTPEDRONI': module to perform Pedroni's panel cointegration tests and
Panel Dynamic OLS estimation / xtpedroni has two functions: First, it
allows Stata users to / compute Pedroni's (OBES 1999, REStat 2001) seven
test statistics / under a null of no cointegration in a heterogeneous
xtpqardl from http://fmwww.bc.edu/RePEc/bocode/x
'XTPQARDL': module to estimate Panel Quantile Autoregressive Distributed
Lag (PQARDL) models / xtpqardl estimates Panel Quantile Autoregressive
Distributed Lag / (PQARDL) models, combining the panel ARDL framework of
Pesaran, / Shin & Smith (1999) with the quantile ARDL methodology of Cho,
xtpqml from http://fmwww.bc.edu/RePEc/bocode/x
'XTPQML': module to estimate Fixed-effects Poisson (Quasi-ML) regression
with robust standard errors / xtpqml provides a wrapper for "xtpoisson,
fe" that computes / robust standard errors, as described by J. Wooldridge
in the / Journal of Econometrics (1999, 77-97). This specification /
xtpsse from http://fmwww.bc.edu/RePEc/bocode/x
'XTPSSE': module to estimate a conditional fixed-effects Poisson panel
regression / The xtpsse command runs a conditional fixed-effects Poisson /
panel regression, computes sandwich and spatial standard errors, / and
tests for time-invariant spatial dependence according to / Bertanha and
xtqreg from http://fmwww.bc.edu/RePEc/bocode/x
'XTQREG': module to compute quantile regression with fixed effects /
xtqreg estimates quantile regressions with fixed effects using / the
method of Machado and Santos Silva (J. Econometrics, 2018). / KW:
quantile regression / KW: fixed effects / Requires: Stata version 14 /
xtregam from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGAM': module to estimate Amemiya Random-Effects Panel Data: Ridge and
Weighted Regression / xtregam estimates Amemiya Random-Effects Panel Data
with Ridge / and Weighted Regression and calculate Panel
Heteroscedasticity, / Model Selection Diagnostic Criteria, and Marginal
xtregbem from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGBEM': module to estimate Between-Effects Panel Data: Ridge and
Weighted Regression / xtregbem estimates Between-Effects Panel Data with
Ridge and / Weighted Regression and calculate Panel Heteroscedasticity, /
Model Selection Diagnostic Criteria, and Marginal Effects and /
xtregbn from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGBN': module to estimate Balestra-Nerlove Random-Effects Panel Data:
Ridge and Weighted Regression / xtregbn estimates Balestra-Nerlove
Random-Effects Panel Data / with Ridge and Weighted Regression and
calculate Panel / Heteroscedasticity, Model Selection Diagnostic Criteria,
xtregdhp from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGDHP': module to estimate Han-Philips (2010) Linear Dynamic Panel
Data Regression / xtregdhp estimates Han-Philips (2010) Linear Dynamic
Panel Data / Regression with be, fe, re Effects, and calculate Panel /
Heteroscedasticity, Model Selection Diagnostic Criteria, and / Marginal
xtregfem from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGFEM': module to estimate Fixed-Effects Panel Data: Ridge and
Weighted Regression / xtregfem estimates Fixed-Effects Panel Data: Ridge
and Weighted / Regression and calculate Panel Heteroscedasticity, Model /
Selection Diagnostic Criteria, and Marginal Effects and / Elasticities. /
xtreghet from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGHET': module to estimate MLE Random-Effects with Multiplicative
Heteroscedasticity Panel Data Regression / xtreghet estimates MLE
Random-Effects with Multiplicative / Heteroscedasticity Panel Data
Regression and calculate Panel / Heteroscedasticity, Model Selection
xtregmle from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGMLE': module to estimate Trevor Breusch MLE Random-Effects Panel
Data: Ridge and Weighted Regression / xtregmle estimates Trevor Breusch
MLE Random-Effects Panel Data: / Ridge and Weighted Regression and
calculate Panel / Heteroscedasticity, Model Selection Diagnostic Criteria,
xtregrem from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGREM': module to estimate Fuller-Battese GLS Random-Effects Panel
Data: Ridge and Weighted Regression / xtregrem estimates Fuller-Battese
GLS Random-Effects Panel Data: / Ridge and Weighted Regression and
calculate Panel / Heteroscedasticity, Model Selection Diagnostic Criteria,
xtregsam from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGSAM': module to estimate Swamy-Arora Random-Effects Panel Data:
Ridge and Weighted Regression / xtregsam estimates Swamy-Arora
Random-Effects Panel Data: Ridge / and Weighted Regression and calculate
Panel Heteroscedasticity, / Model Selection Diagnostic Criteria, and
xtregtwo from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGTWO': module to estimate panel regression with standard errors
robust to two-way clustering and serial correlation in time effects /
xtregtwo executes estimation of linear panel regression models / with
standard errors robust to two-way clustering and / untruncated serial
xtregwem from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGWEM': module to estimate Within-Effects Panel Data: Ridge and
Weighted Regression / xtregwem estimates Within-Effects Panel Data: Ridge
and Weighted / Regression and calculate Panel Heteroscedasticity, Model /
Selection Diagnostic Criteria, and Marginal Effects and / Elasticities. /
xtregwhm from http://fmwww.bc.edu/RePEc/bocode/x
'XTREGWHM': module to estimate Wallace-Hussain Random-Effects Panel Data:
Ridge and Weighted Regression / xtregwhm estimates Wallace-Hussain
Random-Effects Panel Data: / Ridge and Weighted Regression and calculate
Panel / Heteroscedasticity, Model Selection Diagnostic Criteria, and /
xtrobreg from http://fmwww.bc.edu/RePEc/bocode/x
'XTROBREG': module providing pairwise-differences and first-differences
robust regression estimators / xtrobreg provides robust
pairwise-differences estimators and / robust first-differences estimators
for panel data. In case of / least-squares regression, the
xtscc from http://fmwww.bc.edu/RePEc/bocode/x
'XTSCC': module to calculate robust standard errors for panels with
cross-sectional dependence / xtscc produces Driscoll and Kraay (Rev. Ec.
Stat. 1998) standard / errors for coefficients estimated by pooled OLS/WLS
or / fixed-effects (within) regression. / KW: robust standard errors /
xtseqreg from http://fmwww.bc.edu/RePEc/bocode/x
'XTSEQREG': module to perform sequential estimation of linear panel data
models / xtseqreg implements sequential estimators for linear panel data /
models with the analytical second-stage standard error correction / of
Kripfganz and Schwarz (2019, Journal of Applied Econometrics). / The
xtserialpm from http://fmwww.bc.edu/RePEc/bocode/x
'XTSERIALPM': module to perform a portmanteau test for serial correlation
in panel data / xtserialpm performs the portmanteau test developed in
Jochmans / (2019). The procedure tests for serial correlation in the
errors / of a linear panel model after estimation of the regression /
xtspj from http://fmwww.bc.edu/RePEc/bocode/x
'XTSPJ': module for split-panel jackknife estimation / xtspj provides
bias-corrected estimation of panel data models / with fixed effects. It
implements the split-panel jackknife for / fixed-effect versions of
linear, probit, logit, Poisson, / exponential, gamma, Weibull, and negbin2
xtss from http://fmwww.bc.edu/RePEc/bocode/x
'XTSS': module to estimate (S,s) rule regression models for panel data /
xtss estimates the parameters of a linear latent variable / model, where
the observed outcome remains unchanged from the / previous period, if the
difference relative to the current value / of the latent variable is
xtsur from http://fmwww.bc.edu/RePEc/bocode/x
'XTSUR': module to estimate seemingly unrelated regression model on
unbalanced panel data / xtsur fits a many-equation seemingly-unrelated
regression (SUR) / model of the y1 variable on the x1 variables and the y2
variable / on the x1 or x2 variables and etc..., using random effect /
xtsurmg from http://fmwww.bc.edu/RePEc/bocode/x
'XTSURMG': module implementing Fourier Seemingly Unrelated Regression Mean
Group Estimator / xtsurmg implements the Fourier Seemingly Unrelated
Regression / Mean Group (F-SURMG) estimator for heterogeneous panel data
with / smooth changes (Guliyev, Innovation and Green Development, 2025). d
xttacce from http://fmwww.bc.edu/RePEc/bocode/x
'XTTACCE': module to compute Time Averaged Common Correlated Effects
estimator (TACCE) for fixed-N panel data and SUR / Time Averaged Common
Correlated Effects estimator (TACCE) for / fixed-N panel data and SUR,
with interactive fixed effects in / the errors. The idea is to use time
xttest2 from http://fmwww.bc.edu/RePEc/bocode/x
'XTTEST2': module to perform Breusch-Pagan LM test for cross-sectional
correlation in panel data model / xttest2 calculates the Breusch-Pagan
statistic for / cross-sectional independence in the residuals of a panel
data / regression model or a GLS model estimated from cross-section /
xttest3 from http://fmwww.bc.edu/RePEc/bocode/x
'XTTEST3': module to compute Modified Wald statistic for groupwise
heteroskedasticity / xttest3 calculates a modified Wald statistic for
groupwise / heteroskedasticity in the residuals of a fixed effect
regression / model. It is for use after xtreg, fe or xtgls (with the
xtusreg from http://fmwww.bc.edu/RePEc/bocode/x
'XTUSREG': module to estimate dynamic panel models under irregular time
spacing / xtusreg estimates coefficients of fixed-effect linear dynamic /
panel models under unequal spacing of time periods in data, based / on the
identification and estimation theories developed in / Sasaki and Xin
xtvar2 from http://fmwww.bc.edu/RePEc/bocode/x
'XTVAR2': module to compute panel vector autoregression / xtvar2 is a copy
of our xtvar command, designed for / compatibility with Stata 18.5 (or
higher). Our original xtvar / command is no longer usable due to a
conflict with a newly / introduced command in Stata 18.5. xtvar2 estimates
xtvfreg from http://fmwww.bc.edu/RePEc/bocode/x
'XTVFREG': module for estimating variance function panel regression /
xtvfreg implements an iterative mean-variance panel regression / estimator
that allows both the mean and variance of the / dependent variable to be
functions of covariates. The method is / based on Mooi-Reci & Liao (2025)
xvalols from http://fmwww.bc.edu/RePEc/bocode/x
'XVALOLS': module to crossvalidate an OLS regression / xvalols
crossvalidates an OLS regression over a pre-specified / number of
crossfolds and generates crossvalidated predicted / values. / KW:
crossvalidation / KW: crossfolds / KW: OLS / KW: regression / Requires:
ztg from http://fmwww.bc.edu/RePEc/bocode/z
'ZTG': module to estimate zero-truncated geometric regression / ztg fits a
maximum-likelihood zero-truncated geometric regression / model of depvar
on indepvars, where depvar is a positive count / variable. / KW:
zero-truncated / KW: geometric / KW: regression / KW: maximum likelihood /
ztnbp from http://fmwww.bc.edu/RePEc/bocode/z
'ZTNBP': module to estimate zero-truncated NegBin-P regression / ztnbp
fits a zero-truncated Negbin-P model. Setting P=1 or P=2 / gives the
ztnb-1 or ztnb-2 model. Otherwise ztnbp generalizes / these models in the
sense that you get an estimate for P. ztnbp / thus nests these popular
ztpflex from http://fmwww.bc.edu/RePEc/bocode/z
'ZTPFLEX': module to estimate zero-truncated Poisson mixture regression /
ztpflex fits a zero-truncated Poisson model with a more flexible / mixing
distribution than ztpnm. Since it nests the ztpnm, it can / simply be
tested by appropriate parametric restrictions. The / integral is
install_spatial from http://digital.cgdev.org/doc/stata/NonCGD
{cmd:install_spatial}. Install some useful user-written spatial programs.
/ This program need only be run once in order to download and / install on
the user's computer more than 20 programs that are / useful for
descriptive (e.g. maps) and inferential (e.g. regressions) / spatial
bspline from https://www.rogernewsonresources.org.uk/stata16
bspline: Create a basis of B-splines or reference splines / The bspline
package contains 3 commands, bspline, frencurv / and flexcurv. bspline
generates a basis of B-splines in the / X-variate based on a list of
knots, for use in the design / matrix of a regression model. frencurv
ipwbreg from https://www.rogernewsonresources.org.uk/stata16
ipwbreg: Inverse propensity weights from Bernoulli regression / ipwbreg
fits a Bernoulli generalized linear regression model / for a binary
dependent variable in a list of independent / variables, and then outputs
a list of inverse propensity / weight variables. These propensity weight
polyspline from https://www.rogernewsonresources.org.uk/stata16
polyspline: Generate sensible bases for polynomials and other splines /
The polyspline package inputs an X-variable and a list of / reference
points on the X-axis, and generates a basis of / reference splines (one
per reference point) for a polynomial / or other unrestricted spline.
robit from https://www.rogernewsonresources.org.uk/stata16
robit: Robit regression / robit fits a robit regression model, with a
number of degrees / of freedom specified by the user. robit requires the
SSC / package xlink in order to work. / Author: Roger Newson / Author:
Milena Falcaro / Distribution-Date: 26october2022 / Stata-Version: 16
srslogit from https://www.rogernewsonresources.org.uk/stata16
srslogit: Logit regression with secondary ridit splines / srslogit fits a
primary logit model for a binary dependent variable / in a list of
independent variables, followed optionally by a / secondary ridit spline
model for the same binary dependent variable / in the ridit of the
marglmean from https://www.rogernewsonresources.org.uk/stata14
marglmean: Marginal log means from regression models / marglmean
calculates symmetric confidence intervals for log / marginal means (also
known as log scenario means), and / asymmetric confidence intervals for
the marginal means / themselves. marglmean can be used after an
margprev from https://www.rogernewsonresources.org.uk/stata14
margprev: Marginal prevalences from binary regression models / margprev
calculates confidence intervals for marginal / prevalences, also known as
scenario proportions. margprev can be / used after an estimation command
whose predicted values are / interpreted as conditional proportions, such
regpar from https://www.rogernewsonresources.org.uk/stata14
regpar: Population attributable risks from binary regression models /
regpar calculates confidence intervals for population attributable /
risks, and also for scenario proportions. regpar can be used after / an
estimation command whose predicted values are interpreted as / conditional
scenttest from https://www.rogernewsonresources.org.uk/stata14
scenttest: Scenario arithmetic means and their difference / scenttest
calculates confidence intervals for 2 scenario arithmetic / (or geometric)
means, and for their difference (or ratio). / scenttest can be used after
an estimation command whose predicted / values are interpreted as
marglmean from https://www.rogernewsonresources.org.uk/stata13
marglmean: Marginal log means from regression models / marglmean
calculates symmetric confidence intervals for log / marginal means (also
known as log scenario means), and / asymmetric confidence intervals for
the marginal means / themselves. marglmean can be used after an
margprev from https://www.rogernewsonresources.org.uk/stata13
margprev: Marginal prevalences from binary regression models / margprev
calculates confidence intervals for marginal / prevalences, also known as
scenario proportions. margprev can be / used after an estimation command
whose predicted values are / interpreted as conditional proportions, such
regpar from https://www.rogernewsonresources.org.uk/stata13
regpar: Population attributable risks from binary regression models /
regpar calculates confidence intervals for population attributable /
risks, and also for scenario proportions. regpar can be used after / an
estimation command whose predicted values are interpreted as / conditional
scenttest from https://www.rogernewsonresources.org.uk/stata13
scenttest: Scenario arithmetic means and their difference / scenttest
calculates confidence intervals for 2 scenario arithmetic / (or geometric)
means, and for their difference (or ratio). / scenttest can be used after
an estimation command whose predicted / values are interpreted as
marglmean from https://www.rogernewsonresources.org.uk/stata12
marglmean: Marginal log means from regression models / marglmean
calculates symmetric confidence intervals for log / marginal means (also
known as log scenario means), and / asymmetric confidence intervals for
the marginal means / themselves. marglmean can be used after an
margprev from https://www.rogernewsonresources.org.uk/stata12
margprev: Marginal prevalences from binary regression models / margprev
calculates confidence intervals for marginal / prevalences, also known as
scenario proportions. margprev can be / used after an estimation command
whose predicted values are / interpreted as conditional proportions, such
regpar from https://www.rogernewsonresources.org.uk/stata12
regpar: Population attributable risks from binary regression models /
regpar calculates confidence intervals for population attributable /
risks, and also for scenario proportions. regpar can be used after / an
estimation command whose predicted values are interpreted as / conditional
estparm from https://www.rogernewsonresources.org.uk/stata11
estparm: Save results from a parmest resultsset and test equality /
estparm is an inverse of parmest. It inputs 2 or 3 variables in / the
varlist, containing parameter estimates, standard errors, and /
(optionally) degrees of freedom. It saves a set of estimation / results
haif from https://www.rogernewsonresources.org.uk/stata11
haif: Homoskedastic adjustment inflation factors for model selection /
haif calculates homoskedastic adjustment inflation factors / (HAIFs) for
core variables in the corevarlist, caused by / adjustment by the
additional variables specified by addvars() / and/or by sampling
bspline from https://www.rogernewsonresources.org.uk/stata10
bspline: Create a basis of B-splines or reference splines / The bspline
package contains 3 commands, bspline, frencurv / and flexcurv. bspline
generates a basis of B-splines in the / X-variate based on a list of
knots, for use in the design / matrix of a regression model. frencurv
esetran from https://www.rogernewsonresources.org.uk/stata10
esetran: Transforming estimates and standard errors in parmest resultssets
/ esetran is designed for use in parmest resultssets, which have one /
observation per estimated parameter and data on parameter estimates. / It
inputs 2 user-specified variables, containing the estimates and the /
estparm from https://www.rogernewsonresources.org.uk/stata10
estparm: Save results from a parmest resultsset and test equality /
estparm is an inverse of parmest. It inputs 2 or 3 variables in / the
varlist, containing parameter estimates, standard errors, and /
(optionally) degrees of freedom. It saves a set of estimation / results
haif from https://www.rogernewsonresources.org.uk/stata10
haif: Homoskedastic adjustment inflation factors for model selection /
haif calculates homoskedastic adjustment inflation factors (HAIFs) / for
core variables in the corevarlist, caused by adjustment by the /
additional variables specified by addvars(). HAIFs are calculated / for
polyspline from https://www.rogernewsonresources.org.uk/stata10
polyspline: Generate sensible bases for polynomials and other splines /
The polyspline package inputs an X-variable and a list of / reference
points on the X-axis, and generates a basis of / reference splines (one
per reference point) for a polynomial / or other unrestricted spline.
predsurv from https://www.rogernewsonresources.org.uk/stata10
predsurv: Compute predicted or baseline survival after streg or stcox /
predsurv and predbasesurv are intended for use in a survival time /
dataset set up by stset. predsurv is used after streg has been used to /
fit a survival time regression model. It computes a survival /
bspline from https://www.rogernewsonresources.org.uk/stata6
bspline: Create a basis of B-splines or reference splines / The bspline
package contains the programs bspline and frencurv, which / generate bases
of splines in an X-variable for inclusion in the / design matrix of a
regression model. The program bspline generates a / basis of Schoenberg
ccweight from https://www.rogernewsonresources.org.uk/stata5
ccweight: module to generate inverse sampling probability weights /
ccweight takes, as input, a varlist whose distinct values / correspond to
case groups, and a status variable (1 for cases, 0 / for controls) in the
option status. It creates, as output, a new / variable, suitable for use
robit from https://www.rogernewsonresources.org.uk/papers
robit: Robit regression in Stata / Logistic and probit models are the most
popular regression models / for binary outcomes. A simple robust
alternative is the robit model, / which replaces the underlying normal
distribution in the probit / model with a Student\x92s t distribution. The
sensparm from https://www.rogernewsonresources.org.uk/papers
sensparm: Sensible parameters for univariate and multivariate splines /
This is a pre-publication draft of a Stata Journal paper / (Newson, 2012).
The paper describes the bspline package, / which now has 3 modules. The
first, bspline, generates a / basis of Schoenberg B-splines. The second,
gmratio from https://www.rogernewsonresources.org.uk/papers
gmratio: Stata tip 1: The eform() option of regress / This is a
post-publication update of a Stata Tip in The Stata Journal / (Newson,
2003), describing the use of the eform() option of the regress / command
to estimate geometric means and their ratios. These are often used / with
uk2017 from https://www.rogernewsonresources.org.uk/usergp
uk2017: Ridit splines with applications to propensity weighting / Given a
random variable X, the ridit function R_X(.) specifies its / distribution.
The SSC package wridit can compute ridits (possibly / weighted) for a
variable. A ridit spline in a variable X is a spline / in the ridit
uk2012 from https://www.rogernewsonresources.org.uk/usergp
uk2012: Scenario comparisons: How much good can we do? / Applied
scientists, especially public health scientists, frequently / want to know
how much good can be caused by a proposed intervention. / For instance,
they might want to estimate how much we could decrease / the level of a
uk2009 from https://www.rogernewsonresources.org.uk/usergp
uk2009: Homoskedastic adjustment inflation factors in model selection /
Insufficient confounder adjustment is viewed as a common source of "false
/ discoveries", especially in the epidemiology sector. However, adjustment
for / "confounders" that are correlated with the exposure, but which do
uk2002 from https://www.rogernewsonresources.org.uk/usergp
uk2002: Creating plots and tables of estimation results using parmest /
Statisticians make their living mostly by producing confidence intervals
and / P-values. However, the ones supplied in the Stata log are not in any
fit / state to be delivered to the end user, who usually at least wants
uk2001 from https://www.rogernewsonresources.org.uk/usergp
uk2001: Splines with parameters that can be explained to
non-mathematicians / Splines are traditionally used to model non-linear
relationships involving / continuous predictors, usually confounders. One
example is in asthma / epidemiology, where splines are used to model a
testalterr from https://stats.oarc.ucla.edu/stat/stata/ado/analysis
testalterr. Alternative test after regress. / Philip B. Ender /
Statistical Computing and Consulting / UCLA Academic Technology Services /
ender@ucla.edu / STATA ado and hlp files in the package /
distribution-date: 20080826
hinflu6 from https://stats.oarc.ucla.edu/stat/stata/ado/analysis
hinflu6. Hadi measure of regression influence / Philip B. Ender /
Statistical Computing and Consulting / UCLA Academic Technology Services /
ender@ucla.edu / STATA ado and hlp files in the package /
distribution-date: 20000626
pathreg from https://stats.oarc.ucla.edu/stat/stata/ado/analysis
pathreg. Path analysis using ols regression. / Phillip B. Ender / UCLA
Statistical Consulting / ender@ucla.edu / STATA ado and hlp files in the
package / distribution-date: 20090903
regeffectsize from https://stats.oarc.ucla.edu/stat/stata/ado/analysis
regeffectsize. Computes effect size for regression models. / Philip B.
Ender / UCLA Statistical Consulting / ender@ucla.edu / STATA ado and hlp
files for simple main effects program / distribution-date: 20130429
rsquare from https://stats.oarc.ucla.edu/stat/stata/ado/analysis
rsquare. Display R-Square for all possible regressions. / Philip B. Ender
/ Statistical Computing and Consulting / UCLA Office of Academic Computing
/ ender@ucla.edu / STATA ado and hlp files in the package /
distribution-date: 20111010
test2 from https://stats.oarc.ucla.edu/stat/stata/ado/analysis
test2. Alternative test after regress. / Philip B. Ender / Statistical
Computing and Consulting / UCLA Academic Technology Services /
ender@ucla.edu / STATA ado and hlp files in the package /
distribution-date: 20020519
wls0 from https://stats.oarc.ucla.edu/stat/stata/ado/analysis
wls0. Weighted least squares regressin a la Greene / Philip B. Ender /
UCLA Department of Education / UCLA Academic Technology Services /
ender@ucla.edu / STATA ado and hlp files in the package /
distribution-date: 20130822
ldfbeta from https://stats.oarc.ucla.edu/stat/stata/ado/analysis
ldfbeta. Calculates dfbeta for logistic regression / Xiao Chen /
Statistical Computing and Consulting / UCLA Academic Technology Services /
jingy1@ucla.edu / STATA ado and hlp files in the package /
distribution-date: 20010425
logsub from https://stats.oarc.ucla.edu/stat/stata/ado/analysis
logsub. Logistic regression subsets. / Philip B. Ender / Statistical
Computing and Consulting / UCLA Academic Technology Services /
ender@ucla.edu / STATA ado and hlp files in the package /
distribution-date: 20001103
predcalc from https://stats.oarc.ucla.edu/stat/stata/ado/analysis
'PREDCALC': module to calculate out-of-sample predictions for regression,
logistic / predcalc calculates predicted values and confidence intervals /
from linear or logistic regression model estimates for user / specified
values for the X variables. / Author: Joanne M. Garrett, University of
scatlog from https://stats.oarc.ucla.edu/stat/stata/ado/analysis
scatlog. Scatterplot with Logistic Regression Line / Michael N. Mitchell
/ Statistical Computing and Consulting / UCLA Office of Academic Computing
/ mnm@ucla.edu / STATA ado and hlp files in the package /
distribution-date: 20020110
grols from https://stats.oarc.ucla.edu/stat/stata/ado/teach
grolsw. Graph OLS Regression Line / Allows you to modify the slope and
intercept / and display the resulting OLS regression line. / Statistical
Consulting Group / Institute for Digital Research and Education, UCLA /
idrestat@ucla.edu / STATA ado and hlp files in the package /
grlog from https://stats.oarc.ucla.edu/stat/stata/ado/teach
grlogw. Graph Logistic Regression / Allows you to adjust the slope and
intercept / and display the resulting logistic regression line. /
Statistical Consulting Group / Institute for Digital Research and
Education, UCLA / idrestat@ucla.edu / STATA ado and hlp files in the
regpt from https://stats.oarc.ucla.edu/stat/stata/ado/teach
regpt. The Influence of a Single Point in Regression / Statistical
Consulting Group / Institute for Digital Research and Education, UCLA /
idrestat@ucla.edu / STATA ado and hlp files in the package /
distribution-date: 20150326
bspline from http://www.rogernewsonresources.org.uk/stata16
bspline: Create a basis of B-splines or reference splines / The bspline
package contains 3 commands, bspline, frencurv / and flexcurv. bspline
generates a basis of B-splines in the / X-variate based on a list of
knots, for use in the design / matrix of a regression model. frencurv
ipwbreg from http://www.rogernewsonresources.org.uk/stata16
ipwbreg: Inverse propensity weights from Bernoulli regression / ipwbreg
fits a Bernoulli generalized linear regression model / for a binary
dependent variable in a list of independent / variables, and then outputs
a list of inverse propensity / weight variables. These propensity weight
polyspline from http://www.rogernewsonresources.org.uk/stata16
polyspline: Generate sensible bases for polynomials and other splines /
The polyspline package inputs an X-variable and a list of / reference
points on the X-axis, and generates a basis of / reference splines (one
per reference point) for a polynomial / or other unrestricted spline.
robit from http://www.rogernewsonresources.org.uk/stata16
robit: Robit regression / robit fits a robit regression model, with a
number of degrees / of freedom specified by the user. robit requires the
SSC / package xlink in order to work. / Author: Roger Newson / Author:
Milena Falcaro / Distribution-Date: 26october2022 / Stata-Version: 16
srslogit from http://www.rogernewsonresources.org.uk/stata16
srslogit: Logit regression with secondary ridit splines / srslogit fits a
primary logit model for a binary dependent variable / in a list of
independent variables, followed optionally by a / secondary ridit spline
model for the same binary dependent variable / in the ridit of the
marglmean from http://www.rogernewsonresources.org.uk/stata14
marglmean: Marginal log means from regression models / marglmean
calculates symmetric confidence intervals for log / marginal means (also
known as log scenario means), and / asymmetric confidence intervals for
the marginal means / themselves. marglmean can be used after an
margprev from http://www.rogernewsonresources.org.uk/stata14
margprev: Marginal prevalences from binary regression models / margprev
calculates confidence intervals for marginal / prevalences, also known as
scenario proportions. margprev can be / used after an estimation command
whose predicted values are / interpreted as conditional proportions, such
regpar from http://www.rogernewsonresources.org.uk/stata14
regpar: Population attributable risks from binary regression models /
regpar calculates confidence intervals for population attributable /
risks, and also for scenario proportions. regpar can be used after / an
estimation command whose predicted values are interpreted as / conditional
scenttest from http://www.rogernewsonresources.org.uk/stata14
scenttest: Scenario arithmetic means and their difference / scenttest
calculates confidence intervals for 2 scenario arithmetic / (or geometric)
means, and for their difference (or ratio). / scenttest can be used after
an estimation command whose predicted / values are interpreted as
marglmean from http://www.rogernewsonresources.org.uk/stata13
marglmean: Marginal log means from regression models / marglmean
calculates symmetric confidence intervals for log / marginal means (also
known as log scenario means), and / asymmetric confidence intervals for
the marginal means / themselves. marglmean can be used after an
margprev from http://www.rogernewsonresources.org.uk/stata13
margprev: Marginal prevalences from binary regression models / margprev
calculates confidence intervals for marginal / prevalences, also known as
scenario proportions. margprev can be / used after an estimation command
whose predicted values are / interpreted as conditional proportions, such
regpar from http://www.rogernewsonresources.org.uk/stata13
regpar: Population attributable risks from binary regression models /
regpar calculates confidence intervals for population attributable /
risks, and also for scenario proportions. regpar can be used after / an
estimation command whose predicted values are interpreted as / conditional
scenttest from http://www.rogernewsonresources.org.uk/stata13
scenttest: Scenario arithmetic means and their difference / scenttest
calculates confidence intervals for 2 scenario arithmetic / (or geometric)
means, and for their difference (or ratio). / scenttest can be used after
an estimation command whose predicted / values are interpreted as
marglmean from http://www.rogernewsonresources.org.uk/stata12
marglmean: Marginal log means from regression models / marglmean
calculates symmetric confidence intervals for log / marginal means (also
known as log scenario means), and / asymmetric confidence intervals for
the marginal means / themselves. marglmean can be used after an
margprev from http://www.rogernewsonresources.org.uk/stata12
margprev: Marginal prevalences from binary regression models / margprev
calculates confidence intervals for marginal / prevalences, also known as
scenario proportions. margprev can be / used after an estimation command
whose predicted values are / interpreted as conditional proportions, such
regpar from http://www.rogernewsonresources.org.uk/stata12
regpar: Population attributable risks from binary regression models /
regpar calculates confidence intervals for population attributable /
risks, and also for scenario proportions. regpar can be used after / an
estimation command whose predicted values are interpreted as / conditional
estparm from http://www.rogernewsonresources.org.uk/stata11
estparm: Save results from a parmest resultsset and test equality /
estparm is an inverse of parmest. It inputs 2 or 3 variables in / the
varlist, containing parameter estimates, standard errors, and /
(optionally) degrees of freedom. It saves a set of estimation / results
haif from http://www.rogernewsonresources.org.uk/stata11
haif: Homoskedastic adjustment inflation factors for model selection /
haif calculates homoskedastic adjustment inflation factors / (HAIFs) for
core variables in the corevarlist, caused by / adjustment by the
additional variables specified by addvars() / and/or by sampling
bspline from http://www.rogernewsonresources.org.uk/stata10
bspline: Create a basis of B-splines or reference splines / The bspline
package contains 3 commands, bspline, frencurv / and flexcurv. bspline
generates a basis of B-splines in the / X-variate based on a list of
knots, for use in the design / matrix of a regression model. frencurv
esetran from http://www.rogernewsonresources.org.uk/stata10
esetran: Transforming estimates and standard errors in parmest resultssets
/ esetran is designed for use in parmest resultssets, which have one /
observation per estimated parameter and data on parameter estimates. / It
inputs 2 user-specified variables, containing the estimates and the /
estparm from http://www.rogernewsonresources.org.uk/stata10
estparm: Save results from a parmest resultsset and test equality /
estparm is an inverse of parmest. It inputs 2 or 3 variables in / the
varlist, containing parameter estimates, standard errors, and /
(optionally) degrees of freedom. It saves a set of estimation / results
haif from http://www.rogernewsonresources.org.uk/stata10
haif: Homoskedastic adjustment inflation factors for model selection /
haif calculates homoskedastic adjustment inflation factors (HAIFs) / for
core variables in the corevarlist, caused by adjustment by the /
additional variables specified by addvars(). HAIFs are calculated / for
polyspline from http://www.rogernewsonresources.org.uk/stata10
polyspline: Generate sensible bases for polynomials and other splines /
The polyspline package inputs an X-variable and a list of / reference
points on the X-axis, and generates a basis of / reference splines (one
per reference point) for a polynomial / or other unrestricted spline.
predsurv from http://www.rogernewsonresources.org.uk/stata10
predsurv: Compute predicted or baseline survival after streg or stcox /
predsurv and predbasesurv are intended for use in a survival time /
dataset set up by stset. predsurv is used after streg has been used to /
fit a survival time regression model. It computes a survival /
bspline from http://www.rogernewsonresources.org.uk/stata6
bspline: Create a basis of B-splines or reference splines / The bspline
package contains the programs bspline and frencurv, which / generate bases
of splines in an X-variable for inclusion in the / design matrix of a
regression model. The program bspline generates a / basis of Schoenberg
ccweight from http://www.rogernewsonresources.org.uk/stata5
ccweight: module to generate inverse sampling probability weights /
ccweight takes, as input, a varlist whose distinct values / correspond to
case groups, and a status variable (1 for cases, 0 / for controls) in the
option status. It creates, as output, a new / variable, suitable for use
robit from http://www.rogernewsonresources.org.uk/papers
robit: Robit regression in Stata / Logistic and probit models are the most
popular regression models / for binary outcomes. A simple robust
alternative is the robit model, / which replaces the underlying normal
distribution in the probit / model with a Student\x92s t distribution. The
sensparm from http://www.rogernewsonresources.org.uk/papers
sensparm: Sensible parameters for univariate and multivariate splines /
This is a pre-publication draft of a Stata Journal paper / (Newson, 2012).
The paper describes the bspline package, / which now has 3 modules. The
first, bspline, generates a / basis of Schoenberg B-splines. The second,
gmratio from http://www.rogernewsonresources.org.uk/papers
gmratio: Stata tip 1: The eform() option of regress / This is a
post-publication update of a Stata Tip in The Stata Journal / (Newson,
2003), describing the use of the eform() option of the regress / command
to estimate geometric means and their ratios. These are often used / with
uk2017 from http://www.rogernewsonresources.org.uk/usergp
uk2017: Ridit splines with applications to propensity weighting / Given a
random variable X, the ridit function R_X(.) specifies its / distribution.
The SSC package wridit can compute ridits (possibly / weighted) for a
variable. A ridit spline in a variable X is a spline / in the ridit
uk2012 from http://www.rogernewsonresources.org.uk/usergp
uk2012: Scenario comparisons: How much good can we do? / Applied
scientists, especially public health scientists, frequently / want to know
how much good can be caused by a proposed intervention. / For instance,
they might want to estimate how much we could decrease / the level of a
uk2009 from http://www.rogernewsonresources.org.uk/usergp
uk2009: Homoskedastic adjustment inflation factors in model selection /
Insufficient confounder adjustment is viewed as a common source of "false
/ discoveries", especially in the epidemiology sector. However, adjustment
for / "confounders" that are correlated with the exposure, but which do
uk2002 from http://www.rogernewsonresources.org.uk/usergp
uk2002: Creating plots and tables of estimation results using parmest /
Statisticians make their living mostly by producing confidence intervals
and / P-values. However, the ones supplied in the Stata log are not in any
fit / state to be delivered to the end user, who usually at least wants
uk2001 from http://www.rogernewsonresources.org.uk/usergp
uk2001: Splines with parameters that can be explained to
non-mathematicians / Splines are traditionally used to model non-linear
relationships involving / continuous predictors, usually confounders. One
example is in asthma / epidemiology, where splines are used to model a
bivariate from http://digital.cgdev.org/doc/stata/MO/Misc
{cmd:bivariate}: Displays/saves a table of bivariate correlations. / This
program is intended as a utility that a user could / execute prior to
estimating a multiple regression model. / Using the same estimation sample
to be used for the / subsequent regression, this program constructs a
regmsng from http://digital.cgdev.org/doc/stata/MO/Misc
`regmsng'. Regression with missing values of right hand side variables /
Updated to work with longer variables names and pass through {cmd:regress}
options. / {cmd:aidsdata.do} file now works in STATA version 9.2 or later
/ For the paper explaining the application in the {cmd:aidsdata.do} file,
scset from http://digital.cgdev.org/doc/stata/MO/Misc
`scset'. "Rotates" a Stata dataset for DIY synthetic control analysis /
`scset' reshapes and then stacks the data to a structure / which is
amenable to Do-It-Yourself (DIY) Synthetic Control methods. / / In Stata's
conventional data structure, columns contain variables / and rows contain
superscatter from http://digital.cgdev.org/doc/stata/MO/Misc
`superscatter': An enhanced scatter plot / Starting from the scatter plot
with marginal / distributions shown in Stata's {help graph combine} /
documentation, this program adds optional enhancements. / It offers the
options of using a kernel density rather / than a histogram. It can
wtsreg from http://digital.cgdev.org/doc/stata/MO/Misc
`wtsreg'. Constrains the coefficients of a regression to be non-negative
and add to 1.0 / `wtsreg' (think "Weights Regression") is an estimation
program constructed / from Stata's `nl' and `nlcom'. The approach
implements the suggestions / published by Isabel Canette in her blog
cleancmdline from http://digital.cgdev.org/doc/stata/MO/flexcost
{cmd:cleancmdline}. Extract the commmand line from previously issued
estimation command / From a previously issued estimation command, this
program extracts a list / consisting of the dependent variable and the
right-hand-side variables. / Factor variable syntax or time-series
flexmake from http://digital.cgdev.org/doc/stata/MO/flexcost
{cmd:flexmake}. Create the variables and the {help factor variable}
expressions to estimate a cost function / {cmd:flexmake} is a utility to
create the variables and the {help factor variable} / expressions used to
estimate a flexible cost function using a linear / regression method such
st0239_1 from http://www.stata-journal.com/software/sj25-3
SJ25-3 st0239_1. Update: Weighted-average least ... / Update:
Weighted-average least squares: / Improvements and extensions / by
Giuseppe De Luca, University of Palermo, / Palermo, Italy / Jan R. Magnus,
Vrije Universiteit Amsterdam, / Amsterdam, The Netherlands / Support:
st0716 from http://www.stata-journal.com/software/sj23-2
SJ23-2 st0716. Visualizing uncertainty in ... / Visualizing uncertainty in
a two-dimensional / estimate using confidence and comparison / regions /
by Maren Eckert, Institute of Medical Biometry / and Statistics, Division
Methods in / Clinical Epidemiology, Faculty of Medicine / and Medical
st0557 from http://www.stata-journal.com/software/sj19-2
SJ19-2 st0557. xtspj: A command for split-panel ... / xtspj: A command for
split-panel jackknife / estimation / by Yutao Sun, Northeast Normal
University, / School of Economics, Changchun, China, and / Erasmus
University Rotterdam, Rotterdam, / The Netherlands / Geert Dhaene, KU
st0461 from http://www.stata-journal.com/software/sj16-4
SJ16-4 st0461. Support vector machines / Support vector machines / by Nick
Guenther, University of Waterloo, / Waterloo, Canada / Matthias Schonlau,
University of Waterloo, / Waterloo, Canada / Support:
nguenthe@uwaterloo.ca, / schonlau@uwaterloo.ca / After installation,
st0193 from http://www.stata-journal.com/software/sj10-2
SJ10-2 st0193. Data Envelopment Analysis / Data Envelopment Analysis / by
Ji, Yong-Bae, Korea National Defense University, / Republic of Korea /
Lee, Choonjoo, Korea National Defense University, / Republic of Korea /
Support: sarang64@snu.ac.kr, sarang90@kndu.ac.kr, /
art2tex from http://fmwww.bc.edu/RePEc/bocode/a
'ART2TEX': module to automatically generate structured academic paper in
the LaTeX framework / art2tex is a professional Stata program designed for
empirical / researchers in economics, management, and related fields. It /
automatically generates LaTeX paper frameworks that comply with /
gtools from http://fmwww.bc.edu/RePEc/bocode/g
'GTOOLS': module to provide a fast implementation of common group commands
/ gtools is a Stata package that provides a fast implementation / of
common group commands like collapse, egen, isid, levelsof, / contract,
distinct, and so on using C plugins for a massive / speed improvement. /
itsp_ado from http://fmwww.bc.edu/RePEc/bocode/i
'ITSP_ADO': module to accompany Introduction to Stata Programming book /
The routines contained in this package are the ado-files and Mata / files
contained in Baum, Introduction to Stata Programming, Stata / Press, 2008.
After installing the package, give command itsp_ado / to build the Mata
167 references found in tables of contents
------------------------------------------
http://www.stata-journal.com/software/sj25-4/
Stata Journal volume 25, issue 4 / Update: Finding variable names /
Update: Nice axis labels for logarithmic / scales / Stata tip 165:
Marginal titles for graph / bar, graph dot, graph box, and beyond /
Update: Conducting interrupted time-series / analysis for single- and
http://www.stata-journal.com/software/sj25-2/
Stata Journal volume 25, issue 2 / metaxl: A package of tools to handle /
metadata / Link frames and copy variables from the / using frames based on
multiple match / relationships / Stata tip 162: Add marginal rugs using /
marker symbols or axis ticks / Julia as a universal platform for /
http://www.stata-journal.com/software/sj25-1/
Stata Journal volume 25, issue 1 / Visualizing statistical significance
across / coefficients / Stata tip 159: Absent friends: How to plot / what
is not present / Update: Conducting interrupted time-series / analysis for
single- and multiple-group / comparisons / Binscatter regressions /
http://www.stata-journal.com/software/sj24-3/
Stata Journal volume 24, issue 3 / Quantile-quantile plots, generalized /
Stata tip 157: Adding extra lines to graphs / Update:
Simultaneous-inference output table / Update: Fit a Bayesian
misclassification / model / Getting away from the cutoff in regression /
http://www.stata-journal.com/software/sj24-2/
Stata Journal volume 24, issue 2 / The joy of sets: Graphical alternatives
to / Euler and Venn diagrams / Update: Conducting interrupted time-series
/ analysis for single- and multiple-group / comparisons /
Instrumental-variables method to bound / treatment-effects estimates with
http://www.stata-journal.com/software/sj24-1/
Stata Journal volume 24, issue 1 / Update: Estimating text regressions
using / txtreg_train / Update: Nice axis labels for general scales / Use
Windows PowerShell to send email / Open the online help file or PDF /
documentation for a specific command in the / default browser / Update:
http://www.stata-journal.com/software/sj23-4/
Stata Journal volume 23, issue 4 / Update: Report number(s) of distinct /
observations or values / Update: Finding variable names / Update:
Calculate travel distance and travel / time between two addresses or two
points / identified by their geographical / coordinates / Advanced matrix
http://www.stata-journal.com/software/sj23-3/
Stata Journal volume 23, issue 3 / Update: iefieldkit: Commands for
primary / data collection and cleaning / Extract the travel distance and
travel time / between two locations from the Baidu Maps / API
(http://api.map.baidu.com) / Training text regression models in Stata /
http://www.stata-journal.com/software/sj23-1/
Stata Journal volume 23, issue 1 / Update: Speaking Stata: Graphing model
/ diagnostics / Update: Bias, precision, and agreement plots / for
comparison of measurement methods / A note on creating inset plots using
graph / twoway / Update: ART (binary outcomes) -- Sample size / and power
http://www.stata-journal.com/software/sj22-4/
Stata Journal volume 22, issue 4 / Visualizing single observations as /
questionnaires / Nice axis labels for general scales / Machine learning
regression in Stata / Update: gologit2: Generalized ordered /
logit/partial proportional odds models for / ordinal dependent variables /
http://www.stata-journal.com/software/sj22-3/
Stata Journal volume 22, issue 3 / Update: Two-stage nonparametric
bootstrap / sampling with shrinkage correction for / clustered data /
Instrumental-variables estimator for / correlated random-coefficients
model / Calculate the second-generation p-values / (SGPVs) and their
http://www.stata-journal.com/software/sj22-2/
Stata Journal volume 22, issue 2 / Update: One-, two-, and three-way bar
charts / for tables / Binned scatterplots with variables / distribution /
Update: Testing for Granger causality in / panel data / Fit unidimensional
item response theory / models / Test for stationarity in time series using
http://www.stata-journal.com/software/sj21-4/
Stata Journal volume 21, issue 4 / Stata tip 142: joinby is the real merge
m:m / Stata tip 143: Creating donut charts in / Stata / Stata tip 144:
Adding variable text to / graphs that use a by() option / Update: Event
study / Fit panel event study models and generate / event study plots /
http://www.stata-journal.com/software/sj21-3/
Stata Journal volume 21, issue 3 / Stata tip 141: Adding marginal spike /
histograms to quantile and cumulative / distribution plots / Update:
Correlation with confidence / intervals / Update: Conducting interrupted
time-series / analysis for single- and multiple-group / comparisons /
http://www.stata-journal.com/software/sj21-2/
Stata Journal volume 21, issue 2 / Update: Extensions to the label
commands / Plots for each subset with rest of the data / as backdrop /
Update: Conducting interrupted time-series / analysis for single- and
multiple-group / comparisons / Update: Generalized maximum entropy /
http://www.stata-journal.com/software/sj21-1/
Stata Journal volume 21, issue 1 / Stata tip 140: Shorter or fewer
category / labels with graph bar / Update: MM-robust regression / Update:
The S-estimator of multivariate / location and scatter in Stata / Update:
Medcouple measure of asymmetry and / tail heaviness / Update: Event study
http://www.stata-journal.com/software/sj20-4/
Stata Journal volume 20, issue 4 / Update: Report number(s) of distinct /
observations or values / Update: A set of utilities for managing / missing
values / Baidu Map API is widely used in China. This / command helps to
extract longitude and / latitude for a given Chinese address from / Baidu
http://www.stata-journal.com/software/sj20-3/
Stata Journal volume 20, issue 3 / Update: One-, two-, and three-way bar
charts / for tables / Update: Perform fixed- or random-effects /
meta-analyses / Update: Estimating net survival using a life / table
approach / Update: Global search regression (gsreg): A / new automatic
http://www.stata-journal.com/software/sj20-2/
Stata Journal volume 20, issue 2 / Update: Finding variable names /
Visualization strategies for regression / estimates with randomization
inference / Stata tip 136: Between-group comparisons in / a scatterplot
with weighted marker / Update: Speaking Stata: More ways for / rowwise /
http://www.stata-journal.com/software/sj20-1/
Stata Journal volume 20, issue 1 / Added-variable plots for panel-data /
estimation / Update: Estimation of mean health care costs / within a time
horizon with possibly / censored data / Update: cvcrand and cptest:
Commands for / efficient design and analysis of cluster / randomized
http://www.stata-journal.com/software/sj19-3/
Stata Journal volume 19, issue 3 / Speaking Stata: The last day of the
month / Update: Multiple quantile plots / Update: Design plots for
graphical summary / of a response given factors / Added-variable plots
with confidence / intervals / Stata tip 132: Tiny tricks and tips on ticks
http://www.stata-journal.com/software/sj19-2/
Stata Journal volume 19, issue 2 / Update: Fit a linear model with two /
high-dimensional fixed effects / Update: Estimating net survival using a
life / table approach / Update: Event study / Parametric quantile models /
Estimation of finite mixture of Markov chain / models by maximum
http://www.stata-journal.com/software/sj19-1/
Stata Journal volume 19, issue 1 / Draws technical analysis charts for /
financial securities with daily high, low, / open, and close prices /
Update: Distribution function plots / Update: Weight raking by iterative /
proportional fitting / Update: Econometric convergence test and / club
http://www.stata-journal.com/software/sj18-4/
Stata Journal volume 18, issue 4 / Import data from statistical agencies
using / the SDMX standard / Customizing Stata graphs made easy (part 2) /
Color palettes, symbol palettes, and line- / pattern palettes / Update:
gologit2: Generalized ordered / logit/partial proportional odds models for
http://www.stata-journal.com/software/sj18-1/
Stata Journal volume 18, issue 1 / Update: Easy management of complex
spell / data / Nice axis labels for logarithmic scales / Some utilities to
help produce Rich Text / Files from Stata / Update: Generalized Poisson
regression / Update: Negative binomial(p) regression / models / Update:
http://www.stata-journal.com/software/sj17-4/
Stata Journal volume 17, issue 4 / Calculate travel distance and travel
time / between two addresses or two geographical / points identified by
their coordinates / Assessing the calibration of dichotomous / outcome
models with the calibration belt / Update: Age-period-cohort models in
http://www.stata-journal.com/software/sj17-3/
Stata Journal volume 17, issue 3 / Update: A set of utilities for managing
/ missing values / Update: Design plots for graphical summary / of a
response given factors / Update: One-, two-, and three-way bar charts /
for tables / Provide graph schemes sensitive to color / vision deficiency
http://www.stata-journal.com/software/sj17-2/
Stata Journal volume 17, issue 2 / Update: Easy management of complex
spell / data / Update: graphlog: Creating log files with / embedded
graphics / Heuristic criteria for optimal aspect ratios / in a
two-variable line plot / Update: Local polynomial regression- /
http://www.stata-journal.com/software/sj17-1/
Stata Journal volume 17, issue 1 / Automatic creation of a REDCap
instrument / Implement bias and precision plots for / comparison of
measurement methods / Create an HTML or a Markdown document / including
Stata output / Update: The Skillings-Mack Test (Friedman / test when there
http://www.stata-journal.com/software/sj16-4/
Stata Journal volume 16, issue 4 / Update: Importing financial data /
Update: Downloads the presidential approval / poll results from The
American Presidency / Project / Update: Importing U.S. exchange rate data
/ from the federal reserve and standardizing / country names across
http://www.stata-journal.com/software/sj16-3/
Stata Journal volume 16, issue 3 / A sparser, speedier reshape / Shading
zones on time series and other plots / Update: Quantile plots / Update:
Creating LaTeX documents from within / Stata using texdoc / Update:
Conducting interrupted time-series / analysis for single- and
http://www.stata-journal.com/software/sj16-2/
Stata Journal volume 16, issue 2 / An open source routing machine to
calculate / the travel time and distances / Stata tip 126: Handling
irregularly spaced / high-frequency transactions data / Update: Spineplots
for two-way categorical / data / One-, two-, and three-way bar charts for
http://www.stata-journal.com/software/sj16-1/
Stata Journal volume 16, issue 1 / Update: Easy management of complex /
spell data / Download Statistical Software Components / hits over time for
user-written packages / Update: Error-correction-based cointegration /
tests for panel data / Update: Generalized maximum entropy / estimation of
http://www.stata-journal.com/software/sj15-4/
Stata Journal volume 15, issue 4 / EORTC QLQ-C30 descriptive analysis / A
set of utilities for managing missing values / Update: Numbers of present
and missing values / Update: Drop variables (observations) that are / all
missing / Update: Double, diagonal, and polar smoothing / Update:
http://www.stata-journal.com/software/sj15-3/
Stata Journal volume 15, issue 3 / Update: Report number(s) of distinct /
observations or values / Examine n>=2 Stata datasets prior to combining /
Record linkage using Stata: Preprocessing, / linking, and reviewing
utilities / Calculate driving distance and travel time / using the
http://www.stata-journal.com/software/sj15-2/
Stata Journal volume 15, issue 2 / Update: Finding variable names /
gpsbound: Routine for importing and / verifying geographical information
from / a user provided shapefile / Update: The chi-square goodness-of-fit
/ test for count data models / graphlog: Creating log files with /
http://www.stata-journal.com/software/sj15-1/
Stata Journal volume 15, issue 1 / Easy management of complex spell data /
Update: Plotting regression coefficients / and other estimates / Extending
Stata by using the Maxima / computer algebra system / Update: Variable
selection in linear / regression / Two-part models / Compute two-sided
http://www.stata-journal.com/software/sj14-4/
Stata Journal volume 14, issue 4 / txttool: Utilities for text analysis in
Stata / Plotting regression coefficients and other / estimates /
Collecting and organizing Stata graphs / The chi-square goodness-of-fit
test for count / data models / Update: Transform covariate to approximate
http://www.stata-journal.com/software/sj14-2/
Stata Journal volume 14, issue 2 / Update: Generating the finest partition
that / is coarser than two given partitions / Importing Chinese historical
stock market / quotations from NetEase / Speaking Stata: Self and others /
Update: Making regression tables from stored / estimates / Update:
http://www.stata-journal.com/software/sj13-4/
Stata Journal volume 13, issue 4 / Dealing with identifier variables in
data / management and analysis / A tool to generate or replace a variable
/ Generating the finest partition that is / coarser than two given
partitions / Update: Respondent-driven sampling / Update: Estimating the
http://www.stata-journal.com/software/sj13-3/
Stata Journal volume 13, issue 3 / Financial portfolio selection using the
multifactor / capital asset pricing model and imported options / data /
marginscontplot: Plotting the marginal effects of / continuous predictors
/ Update: Fit a linear model with two high-dimensional / fixed effects /
http://www.stata-journal.com/software/sj13-2/
Stata Journal volume 13, issue 2 / Update: Standardizing anthropometric
measures in / children and adolescents with functions for egen / Importing
U.S. Exchange Rate Data from the Federal / Reserve and standardizing
country names across / datasets / Update: Making spatial analysis
http://www.stata-journal.com/software/sj13-1/
Stata Journal volume 13, issue 1 / kmlmap: A Stata command for producing
Google's / Keyhole Markup Language / Update: Decomposition of effects in
nonlinear / probability models with the KHB method / Versatile sample size
calculation using simulation / Trial sequential boundaries for cumulative
http://www.stata-journal.com/software/sj12-4/
Stata Journal volume 12, issue 4 / Update: Importing financial data / HTML
output in Stata / Graphical augmentations to the funnel plot to / assess
the impact of a new study on an existing / meta-analysis / Update: A
programmer's command to build / formatted statistical tables / Update:
http://www.stata-journal.com/software/sj12-3/
Stata Journal volume 12, issue 3 / Diagnostics for multiple imputation in
Stata / The Chen-Shapiro test for normality / Apportionment methods /
Adjusting for age effects in cross-sectional / distributions / A review of
Stata commands for fixed-effects / estimation in normal linear models /
http://www.stata-journal.com/software/sj12-2/
Stata Journal volume 12, issue 2 / Update: Speaking Stata: Distinct
observations / Speaking Stata: Transforming the time axis / Update:
Boosted regression (boosting): An / introductory tutorial and a Stata
plugin / Update: A Stata package for the estimation of the / dose-response
http://www.stata-journal.com/software/sj11-4/
Stata Journal volume 11, issue 4 / Managing the U.S. Census 2000 and World
Development / Indicators databases for statistical analysis in Stata /
Importing financial data / Update: Bootstrap replication size calculator /
Update: Fit a linear model with two high-dimensional / fixed effects /
http://www.stata-journal.com/software/sj11-3/
Stata Journal volume 11, issue 3 / Stata tip 102: Highlighting specific
bars / Update: Measurement error plugin package / Update: Tabulate and
plot results after flexible / modeling of a quantitative covariate /
Logistic quantile regression in Stata / Nonparametric bounds for the
http://www.stata-journal.com/software/sj11-2/
Stata Journal volume 11, issue 2 / Update: Multivariate random-effects
meta-regression / Update: Implementing weak-instrument robust tests for /
a general class of instrumental-variables models / Fitting fully observed
recursive mixed-process models / with cmp / poisson: Some convergence
http://www.stata-journal.com/software/sj11-1/
Stata Journal volume 11, issue 1 / Stata utilities for geocoding and
generating travel / time and travel distance information / Speaking Stata:
MMXI and all that: Roman numerals / in Stata / Visualization of social
networks in Stata using / multidimensional scaling / Update: Maximum
http://www.stata-journal.com/software/sj10-4/
Stata Journal volume 10, issue 4 / Update: A Stata utility for merging
cross-country / data from multiple sources / Update: Finding variable
names / Graphing subsets / Update: Quantile plots, generalized / Update:
Correlation with confidence intervals / Update: Concordance correlation
http://www.stata-journal.com/software/sj10-3/
Stata Journal volume 10, issue 3 / Translation from narrative text to
standard codes / variables with Stata / Update: Projection of power and
events in clinical / trials with a time-to-event outcome / Update: Fuzzy
set creation, testing, and reduction / An introduction to maximum entropy
http://www.stata-journal.com/software/sj10-2/
Stata Journal volume 10, issue 2 / Finding variables / Update:
Goodness-of-fit test for a logistic regression / model estimated using
survey sample data / Update: MM-robust regression / Resampling variance
estimation for complex survey data / Optimal power transformation via
http://www.stata-journal.com/software/sj10-1/
Stata Journal volume 10, issue 1 / Using the world developing indicators
database / for statistical analysis in Stata / Update: Speaking Stata:
Graphing model diagnostics / Update: Double, diagonal, and polar smoothing
/ riskplot: A graphical aid to investigate the / effect of multiple
http://www.stata-journal.com/software/sj9-3/
Stata Journal volume 9, issue 3 / Graphical representatin of multivariate
data using / Chernoff faces / Update: Multiple imputation of missing
values / Confirmatory factor analysis / Implementing weak instrument
robust tests for a / general class of instrumental variables models / A
http://www.stata-journal.com/software/sj9-2/
Stata Journal volume 9, issue 2 / Update: Contour enhanced funnel plots
for / meta-analysis / Updated tests for bias in meta-analysis / Update:
metan: fixed- and random-effects / meta-analysis / Update: GLS for trend
estimation of summarized / dose-response data / Update: Multiple
http://www.stata-journal.com/software/sj8-4/
Stata Journal volume 8, issue 4 / Map chains of events / Report number(s)
of distinct observations or values / Update: Drop variables (observations)
that are all / missing / Update: Graphical representation of interactions
/ Update: Meta-regression in Stata (revised) / Update: Concordance
http://www.stata-journal.com/software/sj7-3/
Stata Journal volume 7, issue 3 / Stem-and-leaf displays / Update:
Concordance correlation coefficient and / associated measures, tests, and
graphs / Robust standard errors for panel regressions with /
cross-sectional dependence / Estimating parameters of dichotomous and
http://www.stata-journal.com/software/sj7-2/
Stata Journal volume 7, issue 2 / Update: Generalized Lorenz curves and
related graphs / Update: Making regression tables simplified / Update:
Generalized ordered logit/partial / proportional odds models for ordinal
dependent variables / Fit population-averaged panel-data models using /
http://www.stata-journal.com/software/sj7-1/
Stata Journal volume 7, issue 1 / File filtering in Stata: handling
complex data / formats and navigating log files efficiently / Rasch
analysis / Multivariable regression spline models / mhbounds - Sensitivity
Analysis for Average / Treatment Effects
http://www.stata-journal.com/software/sj6-4/
Stata Journal volume 6, issue 4 / Update: Generalized Lorenz curves and
related graphs / Update: Quantile plots, generalized / Update:
Confidence intervals for rank statistics: / Somers' D and extensions /
Update: Do-it-yourself shuffling and the number of runs / under
http://www.stata-journal.com/software/sj6-3/
Stata Journal volume 6, issue 3 / Graphical representation of interactions
/ Graphs for all seasons / Update: Confidence intervals for rank
statistics: / Somers' D and extensions / Update: Tests and confidence sets
with correct / size in the simultaneous equations model with / potentially
http://www.stata-journal.com/software/sj6-2/
Stata Journal volume 6, issue 2 / Update: Maximum R-squared and pure error
/ lack-of-fit test / Update: Concordance correlation coefficient / and
associated measures, tests, and graphs / Update: Multivariate probit
regression using / simulated maximum likelihood / Calculation of
http://www.stata-journal.com/software/sj6-1/
Stata Journal volume 6, issue 1 / Speaking Stata: Time of day / Update:
Bin smoothing and summary on scatter / plots / Update: Exact and
cumulative Poisson probability / GLS for trend estimation of summarized /
dose-response data / Generalized ordered logit/partial proportional / odds
http://www.stata-journal.com/software/sj5-4/
Stata Journal volume 5, issue 4 / Update: Numbers of present and missing
values / Update: Renaming variables, multiply and / systematically /
Double, diagonal, and polar smoothing / Update: Model selection using
akaike information / criterion / Update: Instrumental variables and GMM:
http://www.stata-journal.com/software/sj5-3/
Stata Journal volume 5, issue 3 / A multivariable scatterplot smoother /
Distribution function plots / Quantile plots, generalized / Logistic
regression when binary outcome is measured / with uncertainty / Tests for
seasonal data via Edwards and Walters & / Elwood tests / Confidence
http://www.stata-journal.com/software/sj5-2/
Stata Journal volume 5, issue 2 / Value label utilities: labeldup and
labelrename / Multilingual datasets / Stata in Space: Econometric analysis
of spatially / explicit raster data / Data inspection using biplots /
Module for density probability plots / Symmetric nearest neighbor
http://www.stata-journal.com/software/sj5-1/
Stata Journal volume 5, issue 1 / Sampling without replacement: absolute
sample / sizes and all observations / Further processing of estimation
results: Basic / with matrices / Stratified test for trend across ordered
groups / A menu-driven facility for complex sample size / calculation
http://www.stata-journal.com/software/sj4-3/
Stata Journal volume 4, issue 3 / Lean mainstream schemes for Stata 8
graphics / Graphing confidence ellipses: An update of ellip / for Stata 8
/ Marginal effects of the tobit model / Confidence intervals and p-values
for delivery to / the end user / Computing interaction effects and
http://www.stata-journal.com/software/sj4-2/
Stata Journal volume 4, issue 2 / Lean mainstream schemes for Stata 8
graphics / Submenu and dialogs for meta-analysis commands / Hardy-Weinberg
equilibrium test in case-control / studies / Update to residual
diagnostics for cross-section / time-series regression models / Estimation
http://www.stata-journal.com/software/sj3-4/
Stata Journal volume 3, issue 4 / Distribution function plots / Tests for
publication bias in meta-analysis / Numbers of present and missing values
/ Instrumental variables, bootstrapping, and / generalized linear models /
Regression-calibration method for fitting generalized / linear models with
http://www.stata-journal.com/software/sj3-3/
Stata Journal volume 3, issue 3 / Lean mainstream schemes for Stata 8
graphics / B-splines and splines parameterized by their values / at
reference points on the x-axis / somersd -- Confidence intervals for
nonparametric / statistics and their differences / Robust confidence
http://www.stata-journal.com/software/sj2-4/
Stata Journal volume 2, issue 4 / Update to Kornbrot's rank difference
test / Update to least likely observations / Using Aalen's linear hazards
model to / investigate time-varying effects in the / proportional hazards
regression model / Two-graph receiver operating characteristic /
http://www.stata-journal.com/software/sj2-1/
Stata Journal volume 2, issue 1 / Adaptive quadrature for generalized
linear mixed models / Analysis of quantitative traits using regression and
/ log-linear modeling when phase is unknown.
http://www.stata-journal.com/software/sj1-1/
Stata Journal volume 1, issue 1 / Sort a list of items / Generalized
Lorenz curves and related graphs: an update / Flexible parametric
alternatives to the Cox model, and / more / Predicted probabilities for
count models / Haplotype analysis in population-based association /
http://www.stata.com/stb/stb61/
STB-61 May 2001 / Contrasts for categorical variables: update / Patterns
of missing values / Simulating disease status and censored age / Violin
plots for Stata 6 and 7 / Quantile plots, generalized: update to Stata 7.0
/ Update to metabias to work under version 7 / Update of metatrim to work
http://www.stata.com/stb/stb59/
STB-59 January 2001 / Contrasts for categorical variables: update /
Renaming variables: changing suffixes / labjl: Adding numerical codes to
value labels / listjl: List one variable in a condensed form / Sampling
without replacement: absolute sample sizes and / keeping all observations
http://www.stata.com/stb/stb58/
STB-58 November 2000 / Update to a program for saving a model fit as a
dataset / Simulating two- and three-generation families / A turnip graph
engine / Tests for publication bias in meta-analysis: erratum /
Nonparametric trim and fill analysis of publication bias / in
http://www.stata.com/stb/stb57/
STB-57 September 2000 / Extensions to generate, extended: corrections /
Update to changing numeric variables to string / Utility for time series
data / Update of tests for publication bias in meta-analysis / Haplotype
frequency estimation using an EM algorithm and / log-linear modeling /
http://www.stata.com/stb/stb56/
STB-56 July 2000 / Describing variables in memory / Yet more matrix
commands / Changing numeric variables to string / Graphing point estimate
and confidence intervals / Update of galbr / Update of metainf / Update of
metap / Menus for epidemiological statistics / Summary statistics report
http://www.stata.com/stb/stb55/
STB-55 May 2000 / Update of the byvar command / Comparing several methods
of measuring the same quantity / Loglinear modeling using iterative
proportional fitting / Test for autoregressive cond. heteroskedasticity in
/ regression error distribution / Tests for serial correlation in
http://www.stata.com/stb/stb54/
STB-54 March 2000 / Contrasts for categorical variables: update / ICD-9
diagnostic and procedure code utility / Removing duplicate observations in
a dataset / An update to drawing Venn diagrams / Overlaying graphs /
Metadata for user-written contributions to Stata / Automated outbreak det.
http://www.stata.com/stb/stb52/
STB-52 November 1999 / Changing string variables to numeric: correction /
Alternative ranking procedures: update / Using categorical variables in
Stata / Changing the order of variables in a dataset / Update to resample
/ Metadata for user-written contributions to Stata / Exact c.i.s for odds
http://www.stata.com/stb/stb47/
STB-47 January 1999 / Drawing Venn diagrams / Assessing goodness-of-fit of
age-specific ref. intervals / Assessing influence of a single study in
meta-anal. est. / Multiple regression with missing obs. for some variables
/ Two-stage linear constrained estimation / Pairwise comparisons of means,
http://www.stata.com/stb/stb46/
STB-46 November 1998 / Dialog box window for browsing, editing, & entering
obs. / Quantiles of the studentized range distribution / Correction to
labgraph / Graphing confidence ellipses / Violin plots / Correction to the
adjust command / Right, left, and uncensored Poisson regression /
http://www.stata.com/stb/stb45/
STB-45 September 1998 / Digamma and trigamma functions / A tool for
exploring Stata datasets (Windows & Mac only) / Joining episodes in
multi-record survival time data / labgraph: placing text labels on two-way
graphs / A set of 3D-programs / Graphical representation of follow-up by
http://www.stata.com/stb/stb44/
STB-44 July 1998 / Collapsing datasets to frequencies / Tests for
publication bias in meta-analysis / metan -- an alternative meta-analysis
command / Moving summaries / Continuation-ratio models for ordinal
response data / Windmeijer's goodness-of-fit test for logistic regression
http://www.stata.com/stb/stb42/
STB-42 March 1998 / Capturing comments from data dictionaries / A
graphical procedure to test equality of variances / New syntax and output
for meta-analysis command / Adjusted pop. attrib. fractions from logistic
regression / Cumulative meta-analysis / Meta-analysis regression /
http://www.stata.com/stb/stb41/
STB-41 January 1998 / Detection and deletion of duplicate observations /
Corrections to condraw.ado / An adaptive variable span running line
smoother / Expansion and display of if expressions / Timing portions of a
program / Tests for publication bias in meta-analysis / Assessing
http://www.stata.com/stb/stb39/
STB-39 September 1997 / Some new matrix commands / Using expressions in
Stata commands / Discrete time proportional hazards regression /
Newey-West std. err. for probit, logit, & poisson models
http://www.stata.com/stb/stb38/
STB-38 July 1997 / An enhancement of reshape / Age-specific reference
intervals for normally dist. data / Fixed and random-effects
meta-analysis, with graphics / Interquantile and simultaneous-quantile
regression / Routines to maximize a function / Enhancements of
http://www.stata.com/stb/stb35/
STB-35 January 1997 / Automatic recording of definitions / Binomial
smoothing plot / Graphical assess. of the Cox model prop. haz. assumption
/ Programming utility: Numeric lists / A dialog box layout manager for
Stata / Logistic regression: Standardized coef. and partial corr. /
http://www.stata.com/stb/stb32/
STB-32 July 1996 / Accrue statistics across a by varlist / Reading EpiInfo
datasets into Stata / Mislabeled in STB - see sed10 / Patterns of missing
data / Inference about correlations using the Fisher z-transform / Testing
dependent correlation coefficients / Maximum likelihood complementary
http://www.stata.com/stb/stb30/
STB-30 March 1996 / Online documentation for _result() contents / An even
more enhanced for command / An improved command for paired t-tests /
Graphical assessment of linear trend / Nonparametric regression: kernel,
ASH-WARPing, and k-NN
http://www.stata.com/stb/stb28/
STB-28 November 1995 / A utility for surveying Stata-format data sets /
Comparing two Stata data sets / Finding an observation number / Modified
t-tests / Random number generators / Maximum likelihood ridge regression
http://www.stata.com/stb/stb25/
STB-25 May 1995 / Calculate nice numbers for labeling or drawing grid
lines / Create TeX tables from data / Comparing observations within a data
file / Fractional polynomial utilities / Variance inflation factors and
variance-decomp. prop. / Robust tests for equality of variance /
http://www.stata.com/stb/stb22/
STB-22 November 1994 / Bringing large data sets into memory / Sort in
descending order / Save a subset of the current data set / Reading public
use microdata samples into Stata / Fractional polynomials (update) / The
overlapping coefficient & an improved rank-sum test / Mult. comparisons of
http://www.stata.com/stb/stb16/
STB-16 November 1993 / Interactively list values of variables /
Generalized linear models / Kernel density estimators using Stata /
Equation solving by bisection / Graphing functions / A suite of programs
for time-series regression / Computerized index for the STB
http://www.stata.com/stb/stb15/
STB-15 November 1993 / Five data sets for teaching / Incorp. Stata-created
PostScript files into TeX/LaTeX / Calculating U.S. marginal income tax
rates / A suite of programs for time-series regression
http://www.stata.com/stb/stb13/
STB-13 May 1993 / Selecting claims from medical claims data bases / Name
extraction and string utilities / Program debugging command / Stata and
Lotus(tm) 123 / Printing Stata log files / Correlation coefficients with
significance levels / Regression standard errors in clustered samples /
http://www.stata.com/stb/stb10/
STB-10 November 1992 / Brier score decomposition / Similarity coefficients
for 2 x 2 binary data: update / Extended tabulate utilities / Is a
transformation of the dependent variable necessary / Is a transformation
of an independent variable necessary / Smoothed partial residual plots for
http://www.stata.com/stb/stb9/
STB-9 September 1992 / Infiling data: Automatic dictionary creation /
Hyperbolic regression analysis in biomedical applications / Huber
exponential regression / Similarity coefficients for 2 x 2 binary data /
Confidence limits in bivariate linear regression / Quantile regression
http://www.stata.com/stb/stb8/
STB-8 July 1992 / Importing and exporting text files with Stata /
Resistant nonlinear smoothing using Stata / Nonlinear regression command,
bug fix / Centile estimation command / Performing loglinear analysis of
cross-classif.; UPDATE / Calc. of defiance goodness-of-fit stat. after
http://www.stata.com/stb/stb7/
STB-7 May 1992 / Utility to reverse variable coding / Command to unblock
data sets / An ANOVA blocking utility / Stata icon for Microsoft Windows
3.1 / Calculating person-years and incidence rates / 3x3 matched
case-control tests / Resistant smoothing using Stata / Nonlinear
http://www.stata.com/stb/stb5/
STB-5 January 1991 / Automatic command logging (DOS only) / Creating a
grouping variable for data sets / A utility to document beginning and
ending variable dates / Partial residual graphs for linear regression /
Printing graphs and creating WordPerfect graph files / Customizing a Stata
http://www.stata.com/stb/stb4/
STB-4 November 1991 / Automatic command logging (DOS only) / Duplicate
value identification / Printing a series of Stata graphs (DOS only) /
Single factor repeated measures ANOVA / Enhanced logistic regression
program / Bootstrap programming
http://www.stata.com/stb/stb3/
STB-3 September 1991 / Lowess smoothing / Biomedical analysis with Stata:
radioimmunoassay calc. / Resistant normality check and outlier
identification / Enhancement of the Stata collapse command / Nonlinear
regression (derivative free) / Shapiro-Wilk and Shapiro-Francia tests for
http://www.stata.com/stb/stb2/
STB-2 July 1991 / Date calculators / Stata graphics in MS Word and
Wordperfect / Crude 3-D graphics / 3-D contour plot command / Triangle
plot for soil texture / Bailey-Makeham survival model / Variable
transformation by SKTEST / Examination of variables prior to
http://www.stata.com/stb/stb1/
STB-1 May 1991 / Gphpen and colour PostScript / Poisson regression with
rates / Stata and the 4 R's of EDA / Nonlinear regression (derivative
free) / Exact and cumulative Poisson probability / Skewness and kurtosis
test of normality / Extensions to logit command / Actuarial or life-table
https://myweb.uiowa.edu/fboehmke/stata/
Stata programs and utilities written by Frederick J. Boehmke. / Frederick
J. Boehmke, University of Iowa / Below are some Stata programs and
utilities I have written. / See http://www.fredboehmke.net/methods for
more information. / / Estimate duration models with sample selection. /
http://personalpages.manchester.ac.uk/staff/mark.lunt/
Stata programs developed by Mark Lunt / Here are some programs I have
developed for data analysis and / management in stata. I would appreciate
being informed of any / problems you may have with this software, and
particularly the help / files (at mark.lunt@manchester.ac.uk). / Merging
http://taxsim.nber.org/stata/
National Bureau of Economic Research Taxsim program / For information see
/ http://taxsim.nber.org / To install Stata .ado interfaces to Taxsim use
the command: / net from https://taxsim.nber.org/stata / with 2021 state
laws. Computation-as-a-service. / Same as taxsim35 but does computation on
http://www.homepages.ucl.ac.uk/~ucakjpr/stata/
Materials by Patrick Royston / (Some of these programs are the work of
several people.) / These are the {cmd:latest versions} of my software.
Some may be less well tested, and some may even / have bugs. If you have
problems with a program, please contact me at j.royston@ucl.ac.uk. /
https://staskolenikov.net/stata/
Stata programs by Stas Kolenikov / This site contains the Stata programs
written by Stas Kolenikov, / skolenik@gmail.com / / You can use these
programs at your own risk. The author is not / responsible for any mishaps
that may be caused by these programs, / as most of them are to be
http://www.kripfganz.de/stata/
Stata packages by Sebastian Kripfganz / Sebastian Kripfganz,
www.kripfganz.de / The following community-contributed commands can be
freely used at your / own risk. The authors do not assume responsibility
for any unintended / consequences caused by the use of these programs. / I
https://tdmize.github.io/data/
2025-06-30 / Stata packages for marginal effects, IRT models, mediation,
and more / Trenton D. Mize, Purdue University / Details and examples for
each package at www.trentonmize.com/software / : plots of standardized
imbalance statistics / : graphing graph scheme for clean default plots / :
http://www.stata.com/users/mcleves/
Materials by Mario A. Cleves / Materials created by Mario A. Cleves while
working at StataCorp / Median test for K independent samples / Robust test
for the equality of variances / Graph median and mean survival times /
Logit reg. when outcome is measured with uncertainty / ROC commands
http://www.stata.com/users/rcong/
Materials by Ronna Cong / Materials created by Ronna Cong while working at
StataCorp / Treatment regression / Generate diagnostic statistics after
clogit (version 1.1.1) / Truncated regression (updates to STB-52 sg122)
http://www.stata.com/users/ddrukker/
Materials by David Drukker / Materials created by David Drukker while
working at StataCorp / Box-Cox Regression models / do-file to perform
replication / Files for replicating results discussed in the note / "A
comment on Verifying the Solution from a Nonlinear / Solver: A Case
http://www.stata.com/users/wgould/
Materials by Bill Gould, StataCorp / Materials created by Bill Gould while
working at StataCorp / automatic updating of ado-files work in progress /
Mata talk given at Stata user group meetings in 2005 / Mata talk for the
2005 NASUG / Calculate area under ROC after stcox / Install and uninstall
http://www.stata.com/users/wguan/
Materials by Weihua Guan / Materials created by Weihua Guan while working
at StataCorp / perform interval regression with heteroskedasticity /
calculates DFBETAs after regress with the constant term
http://www.stata.com/users/rgutierrez/
Materials by Roberto G. Gutierrez / Materials created by Roberto G.
Gutierrez while working at StataCorp / is a set of utilities for random
number generation / generates likelihood scores after clogit / generates
random deviates from the binomial / distribution / calculates coverage
http://www.stata.com/users/jhardin/
GLM & Extensions / Generalized Linear Models and Extensions / / James W.
Hardin and Joseph Hilbe / (2001), StataPress / Display Akaike's
information criteria / Download the datasets used in the text / Generate
correlated binary outcomes / Fit generalized additive models (requires
http://www.stata.com/users/ymarchenko/
Materials by Yulia Marchenko, StataCorp / Materials created by Yulia
Marchenko while working at StataCorp / perform Deming regression /
standardized coefficients for multiply-imputed data / produce power,
sample-size, and other curves for the / log-rank test
http://www.stata.com/users/jpitblado/
Materials by Jeff Pitblado, StataCorp / Materials created by Jeff Pitblado
while working at StataCorp / Packages identified by (version #) use tools
that are not available prior to / Stata #. / Survey talk for the 2005
NASUG (version 9) / Survey talk for the 2006 Italy SUG (version 9) /
http://www.stata.com/users/proyston/
Materials by Patrick Royston, Imperial College, London. / Patrick Royston
<p.royston@ic.ac.uk> is a biostatistician at the Imperial / College
School of Medicine, London, and a frequent contributor to the Stata /
Technical Bulletin. His areas of interest include regression modelling
http://staskolenikov.net/stata/
Stata programs by Stas Kolenikov / This site contains the Stata programs
written by Stas Kolenikov, / skolenik@gmail.com / / You can use these
programs at your own risk. The author is not / responsible for any mishaps
that may be caused by these programs, / as most of them are to be
http://www.graunt.cat/stata/
User-written commands by the Laboratori d'Estadistica Aplicada (UAB) /
This site provides user-written commands and other materials for use with
Stata. / Agreement: Bland-Altman & Passing-Bablok methods / All Possible
Subsets: linear, logistic & Cox regression / Goodness of fit Chi-squared
http://fmwww.bc.edu/RePEc/bocode/a/
module to estimate models with two fixed effects / module to compute
unbiased IV regression / module for scatter plot with linear and/or
quadratic fit, automatically annotated / module to perform Augmented ARDL
Cointegration Analysis / module to provide Gradient Solver for Ahlfeldt &
http://fmwww.bc.edu/RePEc/bocode/b/
module to account for changes when X2 is added to a base model with X1 /
module to plot two graph types which are rooted in Bland-Altman plots
using journal and paper percentiles / module to implement a backward
procedure with a Rasch model / module to make daily backup of important
http://fmwww.bc.edu/RePEc/bocode/c/
module to implement machine learning classification in Stata / module to
cache all other Stata commands / module to generate calendar / module to
estimate proportions and means after survey data have been calibrated to
population totals / module for inverse regression and calibration / module
http://fmwww.bc.edu/RePEc/bocode/d/
module to create network visualizations using D3.js to view in browser /
module to produce terrible dad jokes / module to provide utilities for
directed acyclic graphs / module to fit a Generalized Beta (Type 2)
distribution to grouped data via ML / module to fit a Dagum distribution
http://fmwww.bc.edu/RePEc/bocode/e/
module to estimate endogenous attribute attendance models / module to
compute Extended Sample Autocorrelation Function / module to perform
extreme bound analysis / module to perform Entropy reweighting to create
balanced samples / module to perform entropy balancing / module to perform
http://fmwww.bc.edu/RePEc/bocode/f/
module for the estimation of marginal effects with transformed covariates
/ module to score Foot and Ankle Ability Measure / module for plots for
each subset with rest of the data as backdrop / module to extract factor
values from a label variable created by parmest / module to merge a list
http://fmwww.bc.edu/RePEc/bocode/g/
module to provide graphics schemes for http://fivethirtyeight.com / module
for generalised additive models / module to perform game-theoretic
calculations / module to fit a two-parameter gamma distribution / module
to compute the value of the symmetrical gamma function / module providing
http://fmwww.bc.edu/RePEc/bocode/h/
module to perform Hadri panel unit root test / module to compute
Homoskedastic Adjustment Inflation Factors for model selection / module to
randomly produce haikus / module to compute homoskedastic adjustment
inflation factors for model selection / module for computing half-normal
http://fmwww.bc.edu/RePEc/bocode/i/
module to provide individual decomposition of inequality measures / module
to import International Aid Transparency Initiative data / module to
compute measures of interaction contrast (biological interaction) / module
to compute Interaction Effects in Linear and Generalized Linear Models /
http://fmwww.bc.edu/RePEc/bocode/k/
module to compute Krippendorff's Alpha-Reliability / module to estimate
Krippendorff's alpha for nominal variables / module to compute confidence
intervals for the kappa statistic / module to graph examples of
distributions of varying kurtosis / module to produce Generalizations of
http://fmwww.bc.edu/RePEc/bocode/l/
module to automatically manage datasets obtained from US Census 2000 and
World Development Indicators databases / module to produce syntax to label
variables and values, given a data dictionary / module to report numeric
variables with values lacking value labels / module to list value labels /
http://fmwww.bc.edu/RePEc/bocode/m/
module to implement interpoint distance distribution analysis / module to
unabbreviate Global Macro Lists / module to compute the macroF evaluation
criterion for multi-class outcomes / module to perform Dickey-Fuller test
on panel data / module to create dot plot for summarizing pooled estimates
http://fmwww.bc.edu/RePEc/bocode/n/
module to convert NAICS codes to Fama-French industry classifications /
module to generate Excel list of name mismatches / module to identify and
adjust outliers of a variable assumed to follow a negative binomial
distribution / module to generate graph command (and optionally graph)
http://fmwww.bc.edu/RePEc/bocode/o/
module to compute the Blinder-Oaxaca decomposition / module to compute
decompositions of outcome differentials / module to compute the
Blinder-Oaxaca decomposition / module to identify differences in values
across observations for a variable / module to display observations of
http://fmwww.bc.edu/RePEc/bocode/p/
module to calculate confidence limits of a regression coefficient from the
p-value / module to convert between minutes per mile and miles per hour /
module to perform Page's L trend test for ordered alternatives / module to
create paired datasets from individual-per-row data / module for plots of
http://fmwww.bc.edu/RePEc/bocode/q/
module to perform the Quantile Autoregression (QAR) unit root test
proposed by Koenker and Xiao (JASA, 2004) / module to perform quadratic
assignment procedure / module to perform Quantile Autoregressive
Distributed-Lag (QARDL) estimation / module to generate quantile-quantile
http://fmwww.bc.edu/RePEc/bocode/r/
module to compute r-squared measures for models estimated by mixed /
module to compute McKelvey & Zavoina's R2 / module for computing
Nakagawa's R-squared statistic for multilevel mixed-effects linear
regression / module to compute several fit statistics for count data
http://fmwww.bc.edu/RePEc/bocode/s/
module to provide survey to survey imputation tool / Sequence analysis
distance measures / module to provide commands and mata functions devoted
to unit level small area estimation / module to drop variables if and only
if varnames specified in full / module to compute sample size for an
http://fmwww.bc.edu/RePEc/bocode/t/
module to report Mean Comparison for variables between two groups with
formatted table output in DOCX file / module to perform Tukey's Two-Way
Analysis by Medians / module to compute diagnostic metrics and predictive
values from 2-by-2 tables / module to export tabstat results to Excel with
http://fmwww.bc.edu/RePEc/bocode/v/
module to compute mediation effect in SEM / module for downloading daily
share values and assets balances of Chile's unemployment insurance funds
and pension system / module to provide several functionalities for dealing
with codes from the Portuguese Classification of Economic Activities (CAE)
http://fmwww.bc.edu/RePEc/bocode/w/
module to produce waffle charts using percent or proportion variables /
module to produce waffle plots / module to calculate the maximum mean
square error (MSE) of a point estimator of the mean / module to perform a
two-sample Wasserstein Distance test for equality of distributions /
http://fmwww.bc.edu/RePEc/bocode/x/
module to input an extended version of the auto data / module to transform
the logit scores into probabilities / module to compute standardized
differences for stratified comparisons via R / module to tabulate
differences in predicted responses after restricted cubic spline models /
http://fmwww.bc.edu/RePEc/bocode/z/
module to calculate Zivot-Andrews unit root test in presence of structural
break / module to Recoding multiple responses into binary variables /
module to estimate zero inflated negative binomial model on count data /
module to estimate zero inflated Poisson model on count data / module to
https://raw.githubusercontent.com/rdpackages/rdrobust/master/stata/
RDROBUST: Inference and graphical procedures using local polynomial and
partitioning regression methods. / https://rdpackages.github.io/rdrobust /
https://raw.githubusercontent.com/nppackages/nprobust/master/stata/
NPROBUST: Estimation and inference using kernel density and local
polynomial regression methods. / https://nppackages.github.io/nprobust /
https://raw.githubusercontent.com/nppackages/lpdensity/master/stata/
LPDENSITY: Estimation and inference using local polynomial
distribution/density regression methods. /
https://nppackages.github.io/lpdensity / /
https://www.rogernewsonresources.org.uk/stata16/
Stata 16 packages written by Roger Newson / These can be used by users
with Stata Version 16 or above. / The latest version of a package can
usually be downloaded from SSC-Ideas. / Add in data from a disk or frame
dataset using a foreign key / Create a basis of B-splines or reference
https://www.rogernewsonresources.org.uk/stata14/
Stata 14 packages written by Roger Newson / These can be used by users
with Stata Version 14 or above. / The latest version of a package can
usually be downloaded from SSC-Ideas. / Execute commands from a file,
creating a log file / Multiple versions of dolog for executing
https://www.rogernewsonresources.org.uk/stata13/
Stata 13 packages written by Roger Newson / These can be used by users
with Stata Version 13 or above. / The latest version of a package can
usually be downloaded from SSC-Ideas. / Template do-files for inputting
CPRD datasets into Stata / Converting CPRD entity string data of any
https://www.rogernewsonresources.org.uk/stata12/
Stata 12 packages written by Roger Newson / These can be used by users
with Stata Version 12 or above. / The latest version of a package can
usually be downloaded from SSC-Ideas. / Execute commands from a file,
creating a log file / Multiple versions of dolog for executing
https://www.rogernewsonresources.org.uk/papers/
Papers written by Roger Newson / These are pre-publication drafts,
post-publication updates, published papers / or unpublished papers written
by Roger Newson. Each package contains / a paper as an ancillary file,
which the user can download to his/her / current directory by typing / net
https://stats.oarc.ucla.edu/stat/stata/ado/analysis/
Welcome to UCLA Academic Technology Services Stata programs. / These
programs include tools for data analysis. / These include programs from
the Stata Technical Bulletin, / courtesy of, and copyright, Stata
Corporation. / For more information about these programs, see / our web
https://stats.oarc.ucla.edu/stat/stata/ado/teach/
Welcome to UCLA Academic Technology Services Stata programs. / These
programs include teaching tools. / For more information about these
programs, see / our web page at http://www.ats.ucla.edu/stat/stata/ /
Teaching Tools on Univariate Distributions / How "N" and "conf. level"
http://www.rogernewsonresources.org.uk/stata16/
Stata 16 packages written by Roger Newson / These can be used by users
with Stata Version 16 or above. / The latest version of a package can
usually be downloaded from SSC-Ideas. / Add in data from a disk or frame
dataset using a foreign key / Create a basis of B-splines or reference
http://www.rogernewsonresources.org.uk/stata14/
Stata 14 packages written by Roger Newson / These can be used by users
with Stata Version 14 or above. / The latest version of a package can
usually be downloaded from SSC-Ideas. / Execute commands from a file,
creating a log file / Multiple versions of dolog for executing
http://www.rogernewsonresources.org.uk/stata13/
Stata 13 packages written by Roger Newson / These can be used by users
with Stata Version 13 or above. / The latest version of a package can
usually be downloaded from SSC-Ideas. / Template do-files for inputting
CPRD datasets into Stata / Converting CPRD entity string data of any
http://www.rogernewsonresources.org.uk/stata12/
Stata 12 packages written by Roger Newson / These can be used by users
with Stata Version 12 or above. / The latest version of a package can
usually be downloaded from SSC-Ideas. / Execute commands from a file,
creating a log file / Multiple versions of dolog for executing
http://www.rogernewsonresources.org.uk/papers/
Papers written by Roger Newson / These are pre-publication drafts,
post-publication updates, published papers / or unpublished papers written
by Roger Newson. Each package contains / a paper as an ancillary file,
which the user can download to his/her / current directory by typing / net
http://digital.cgdev.org/doc/stata/MO/Misc/
Center for Global Deveopment / Welcome to Mead Over's page on STATA
Programs and Utilities at the CGD Stata repository / Here is my {browse
"http://www.cgdev.org/expert/mead-over/":homepage.} / Email {browse
"mailto:movercgdev.org":MOverCGDev.Org} if you observe any problems. /
(end of search)We can see that a new Stata window pops up on our computer, and we can click on the different options that are shown to look at the documentation for all these commands. Try it yourself in the code cell below!
In the following modules, whenever there is a command which confuses you, feel free to write search command or help command to redirect to the documentation for reference.
Note: These commands have to be used on your Stata console!
In the next module, we will expand on our knowledge of locals, as well as globals, another type of variable.
3.6 Wrap-up Table
| Command | Function |
|---|---|
describe |
Provides the characteristics of our dataset including the number of observations and variables, and variable types |
summarize |
Calculates and provides a variety of summary statistics of the general dataset or specific variables |
help |
Provides information on each command including its definition, syntax, and the options associated with the command |
if-conditions |
Used to verify a condition before executing a command. If conditions make use of logical and conditional operators and are preceded by the desired command |
sort |
Used to sort the observations of the data set into ascending order |
detail |
Provides additional statistics, including skewness, kurtosis, the four smallest and four largest values, and various percentile |
display |
Displays strings and values of scalar expressions |
search |
Can be used to find useful commands |
while |
A type of loop that iterates until a condition is met |
forvalues |
A type of for-loop that iterates across a range of numbers |
foreach |
A type of for-loop that iterates across a list of items |
References
PDF documentation in Stata
Stata Interface tour
One-way tables of summary statistics
Two-way tables of summary statistics