Syllabus for ECON 626: Econometric Theory I

Author

Paul Schrimpf

Published

September 9, 2023

Basic Information

Instructor

Paul Schrimpf

112 Iona

paul.schrimpf@ubc.ca

Office Hours: Wednesday 12:30pm-1:30pm

TA

Yige Duan

yige.duan@gmail.com

Office Hours: Monxday 2-3pm

Time and Place

Lecture: Mondays and Wednesdays 11:00am - 12:20pm, Iona 633

Discussion: Friday 1:30pm - 2:50pm, Buchanan D209

Textbooks

Our primary textbook will be notes written by Kyunchul Song. You can find them on Canvas. Other recommended (but not required) textbooks include:

  • Bruce Hansen (2022) Probability and Statistics for Economists and Econometrics
  • Fumio Hayashi (2000) Econometrics
  • George Casella and Roger L. Berger (2002) Statistical Inference
  • Takeshi Amemiya (1985) Advanced Econometrics

Grading

Problem Sets (30%)

There will be 6-9 problem sets. A problem set with the lowest grade will be dropped from final grading.

Midterm Exam (30%)

Given in class on Wednesday, October 25. Closed books and notes. There will be no make-up exam for midterm. When a student is excused from midterm exam, his or her grade will be based on reweighting of the problem sets (30%) and the final exam (70%).

Final Exam (40%)

Closed books and notes.

Course Description

The course aims to equip students with introductory knowledge in probability, statistics, and econometrics so that students become familiar with basic concepts underlying empirical and methodological research in economics.

Outline

Part I: Basics

  • Probability
    • Random Variables, and Distributions
    • Conditional Expectations and Conditional Distributions
    • Family of Distributions
  • Basics of Inference - Estimation - Hypothesis Testing

Part II: Generalized Linear Model

  • Preliminaries of Projection Geometry
  • Generalized Linear Models and Gauss-Markov Theorem
  • Tests of Linear Hypothesis

Part III: Tools of Asymptotic Theory

  • Modes of Convergence
  • Slutsky’s Lemma/Continuous Mapping Theorem
  • Delta Methods

Part V: Linear Models with Endogeneity

  • Identification and Endogeneity
  • Identification through IV and Inference

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