Course Syllabus
ECON 310/STAT 376: Econometrics -- Fall 2022
Professor: Mahmoud El-Gamal
TA: Kristof Kutasi
Class: TR, 2:30-3:45 p.m., KRF105
Lab: T, 7:00--8:15 p.m., KRF105
Office hours: T 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 + some empirical and homework exercises):
- Hansen, Bruce, Econometrics, Princeton University Press, 2022.
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.)
- Week 01 -- Aug 23, 25: Regression and Projection (Ch. 2)
- Week 02 -- Aug. 30, Sept. 1: Conditional Expectations & Projection (Ch. 2)
- Week 03 -- Sep. 06, 08: Least Squares Regression & Best Linear Unbiased Estimator (Chs. 2-3)
- Week 04 -- Sep. 13, 15: GLS, Heteroscedastcity, Clustered SEs & Serial Correlation (Ch. 4)
- Week 05 -- Sep. 20, 22: More on Robust Standard Errors + Intro to Inference (Chs. 5-7)
- Week 06 -- Sep. 27, 29: Diff in Diff, Hypothesis testing & Wald Test (Chs. 18, 7-9)
- Week 07 -- Oct. 04, 06: Inference, Restricted Estimation, Jackknife & Bootstrap (Chs. 9-10)
- Week 08 -- Oct. 13: Many Variables and Instruments & 2SLS (Chs. 11-12)
- Week 09 -- Oct. 18, 20: Linear GMM + Non-linear GMM & Optimization (Ch. 13)
- Week 10 -- Oct. 25, 27: LDVs: Multinomial & Nested Logit and Ordered Logit & Tobit (Chs. 25-27)
- Week 11 -- Nov. 01, 03: Panel Data and Dynamic Panels (Ch. 17)
- Week 12 -- Nov. 08, 10: Univariate Time Series: Frequency Domain and Time Domain (Ch. 14)
- Week 13 -- Nov. 15, 17: Multivariate Time Series: VAR + SVAR & Frequency Domain (Ch. 15)
- Week 14 -- Nov. 22: Unit Roots, Cointegration & VECM (Ch. 16)
- Week 15 -- Nov. 29, Dec. 1: 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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