Course Syllabus
ECON 310/STAT 376: Econometrics -- Fall 2024
Professor: Mahmoud El-Gamal
TAs: Yan-Yu Chiou and Grace Molina
Class: TR, 2:30—3:45 p.m., KRF 125
Lab: T, 7:00—8:15 p.m., SEW 207
Office hours: T 1:00—2:00 p.m., KRF 429, or by appointment
Course Description:
We study various regression methods for estimation and inference with special emphasis on (1) understanding mathematically when various methods work as desired and why, (2) conducting simple Monte Carlo simulations, and (3) replicating famous econometric studies using authors’ original data and regression methods. Lecture notes are provided using RMarkdown, and all student assignments and exams (all take-home) are likewise conducted in RMarkdown
Textbook (Optional; for mathematical proofs + homework exercises):
- Hansen, Bruce, Econometrics, Princeton University Press, 2022.
Useful R resources (Optional; for simulations and empirical exercises):
- R Markdown: The Definitive Guide
- Grolemund, Garrett and Hadley Wickham, R for Data Science
AI-Assisted Learning:
- I use Github Copilot in Rstudio (recommended for this course) as well as VS Code (with more features for more advanced students)
- I will show you how to use this AI resource effectively, and you will be allowed to use it (and other AI resources if you wish) in your homework and exam assignments
- If AI doesn't give you a satisfactory answer, you may also search Stack Overflow and other online resources
Tentative Syllabus: (Chapter references are to Hansen textbook, but only as guidelines. We will not cover chapters fully and will be supplementing the material)
- Week 01 -- Aug 29: Regression and Projection Basics (Ch. 2)
- Week 02 -- Sep. 03, 05: Conditional Expectations & Projection (Ch. 2)
- Week 03 -- Sep. 10, 12: Least Squares Regression & Best Linear Unbiased Estimator (Chs. 2-3)
- Week 04 -- Sep. 17, 19: BLUE, GLS, Heteroscedastcity-Consistent and Clustered SEs (Ch. 4)
- Week 05 -- Sep. 24, 26: Heteroscedasticity and Autocorrelation Consistent SEs + Inference (Chs. 5-7)
- Week 06 -- Oct. 01, 03: Diff in Diff, Hypothesis testing & Wald Test (Chs. 18, 7-9)
- Week 07 -- Oct. 08, 10: Inference, Restricted Estimation, Jackknife & Bootstrap (Chs. 9-10)
- Week 08 -- Oct. 17: Many Variables and Instruments & 2SLS (Chs. 11-12)
- Week 09 -- Oct. 22, 24: Linear GMM + Non-linear GMM & Optimization (Ch. 13)
- Week 10 -- Oct. 29, 31: LDVs: Multinomial & Nested Logit and Ordered Logit & Tobit (Chs. 25-27)
- Week 11 -- Nov. 05, 07: Panel Data and Dynamic Panels (Ch. 17)
- Week 12 -- Nov. 12, 14: Univariate Time Series: Frequency Domain and Time Domain (Ch. 14)
- Week 13 -- Nov. 19, 21: Multivariate Time Series: VAR + SVAR & Frequency Domain (Ch. 15)
- Week 14 -- Nov. 26: Unit Roots, Cointegration & VECM (Ch. 16)
- Week 15 -- Dec. 03, 05: Nonparametric & Polynomial + Quantile Regression (Chs. 19-20, 24)
Grading:
- Assignments (most likely four): 30%
- Two Midterm Exams: 40%
- Final Exam: 30%
Course Summary:
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