Course Syllabus

ECON 310/STAT 376: Econometrics -- Fall 2020

Professor: Mahmoud El-Gamal

TA: Suguru Otani

Class: TR, 3:10--4:30 p.m., KRF125

Lab: T, 7:00--8:04 p.m., KRF125

Office hours: TR 2:00--3:00 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.

Mode of delivery: To the extent possible, classes and labs will be held in face-to-face mode. Students who will be out of Houston or U.S.A. may join classes and labs live on Zoom or catch up with Zoom recordings. Office hours will also be held in person and via Zoom. Students who cannot come to regular office hours can email instructor to set up a mutually convenient meeting time.

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

• Hansen, Bruce, Econometrics, February 2020
  • Cached  on 'Canvas > Files' with author's permission on May 1, 2020, from: https://www.ssc.wisc.edu/~bhansen/econometrics/

Useful R resources (for simulations and empirical exercises):

• R Markdown: The Definitive Guide
• DataCamp (free for students, useful for learning/remembering R skills)
• 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)

• Week 01 -- Aug 25, 27: Projection (Ch. 2) -- Thursday class canceled (Hurricane Laura)
• Week 02 -- Sep. 01, 03: Conditional Expectations and Linear algebra review  (Chs. 2-3)
• Week 03 -- Sep. 08, 10: Best linear predictor and Least squares Regression  (Chs. 3-4)
• Week 04 -- Sep. 15, 17: Normal regression, Asymptotics and Hypothesis testing (Chs. 5-9)
• Week 05 -- Sep. 22, 24: Restricted estimation & Jackknife and Bootstrap (Chs. 9-10)
• Week 06 -- Sep. 29, Oct. 01: Subsampling and Inference &  Review (Ch. 10)
• Week 07 -- Oct. 06, 08: Many Variables and Instruments & 2SLS   (Chs. 11-12)
• Week 08 -- Oct. 13, 15: Generalized Method of Moments (GMM)   (Ch. 13)
• Week 09 -- Oct. 20, 22: Nonlinear Optimization and Univariate Time Series, Time Domain (Ch. 14+)
• Week 10 -- Oct. 27, 29: Time Series, Frequency Domain and Multivariate Time Series (Ch. 15+)
• Week 11 -- Nov 03, 05: Unit Roots & Cointegration and Panel Data (Chs. 16-17)
• Week 12 -- Nov 10, 12: Panel Data, Dynamic Panels & Diff in Diff (Chs. 17-18) 
• Week 13 -- Nov 17, 19: Nonparametric Regression & Polynomial & Spline Regression (Chs. 19-20)

Grading:

• Assignments (most likely five): 40%
• Midterm Exam: 30%
• Final Exam: 30%