Course Syllabus

ECON 310/STAT 376: Econometrics -- Fall 2021

Professor: Mahmoud El-Gamal

TA: Suguru Otani and Yan-Yu Chiou

Class: TR, 2:30-3:45, KRF105

Lab: T, 7:00--8:15 p.m., KRF105

Office hours: TR 1:00--2:00 p.m. KRF429 or by appointment

Course Description: 

We study various regression methods for estimation and inference with special emphasis on (1) understanding and proving mathematically when various methods work as desired and why, (2) learning how to conduct simple Monte Carlo simulations to complement mathematical results, and (3) replicating famous econometric studies using authors’ original data and regression methods. Lecture notes are provided using R Markdown, and all student assignments and exams are likewise conducted in R Markdown — typesetting mathematical solutions to exercises in LaTeX, and writing/modifying R code for simulation and estimation exercises.

Textbook (for mathematical derivations and proofs + empirical examples and homework exercises):

• Hansen, Bruce, Econometrics, June 2021
  • Cached  on 'Canvas > Files' with author's permission on July 23, 2021; from: https://www.ssc.wisc.edu/~bhansen/econometrics/

Useful R resources (for simulations and empirical exercises):

• R Markdown: The Definitive Guide
• Grolemund, Garrett and Hadley Wickham, R for Data Science

Tentative Syllabus: (chapter references to Hansen textbook, we may not cover all chapters fully, and we will be supplementing the material in the book for some weeks)

(Posted draft codes and lecture notes will be revised and/or rearranged week to week, hyperlinked in syllabus and/or retrievable under 'Canvas > Files' tab)

• Week 01 -- Aug 24, 26: Regression and Projection (Ch. 2) 
• Week 02 -- Aug. 31, Sept. 2: Conditional Expectations & Projection  (Ch. 2)
• Week 03 -- Sep. 07, 09: Least Squares Regression & Best Linear Unbiased Estimator  (Chs. 2-3)
• Week 04 -- Sep. 14, 16: Generalized Least Squares, Heteroscedastcity and Clustered SEs (Ch. 4)
• Week 05 -- Sep. 21, 23: Asymptotics, Hypothesis testing & Restricted Estimation (Chs. 7-9)
• Week 06 -- Sep. 28, 30: Asymptotics, Hypothesis testing & Restricted Estimation (Chs. 7-9)
• Week 07 -- Oct. 05, 07: More on Asymptotics & Jackknife, Bootstrap and Subsampling (Ch. 10+)
• Week 08 -- Oct. 14: Many Variables and Instruments & 2SLS  + Difference in Difference (Chs. 11-12, 18)
• Week 09 -- Oct. 19, 21: Generalized Method of Moments (GMM)   (Ch. 13)
• Week 10 -- Oct. 26, 28: GMM and Nonlinear Optimization  (Ch. 13+)
• Week 11 -- Nov. 02, 04: Univariate Time Series (ARMA models, forecasting)  (Ch. 14)
• Week 12 -- Nov. 09, 11:  Multivariate Time Series (VAR, SVAR, impulse response functions) (Ch. 15)
• Week 13 -- Nov. 16, 18:  Unit Roots & Cointegration (Ch. 16) 
• Week 14 -- Nov. 23:   Panel Data (Ch. 17)
• Week 15 -- Nov. 30, Dec. 2:  Dynamic Panels + Nonparametric Regression (Ch2. 17, 19)

Grading:

• Assignments (most likely four): 30%
• Two Midterm Exams: 40%
• Final Exam: 30%