Course Syllabus

Jump to Today

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):

Useful R resources (Optional; for simulations and empirical exercises):

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)
    • Sep. 03: Assignment 1 posted on Canvas
  • 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)
    • Sep 17: Assignment 1 due on Canvas; Assignment 2 posted on Canvas
  • 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)
    • Oct 1: Assignment 2 due on Canvas, Midterm 1 posted on Canvas
  • 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)
    • Oct 17: Midterm 1 due on Canvas; Assignment 3 posted on Canvas
  • 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)
    • Oct 31: Assignment 3 due on Canvas; Midterm 2 posted on Canvas
  • Week 11 -- Nov. 07: Panel Data and Dynamic Panels (Ch. 17) 
  • Week 12 -- Nov. 12, 14:  Univariate Time Series: Frequency Domain and Time Domain  (Ch. 14)
    • Nov 14: Midterm 2 due on Canvas; Assignment 4 posted on Canvas
  • Week 13 -- Nov. 19, 21:  Multivariate Time Series: VAR + SVAR &  Unit Roots, Cointegration & VECM (Chs. 15-16)
  • Week 14 -- Nov. 26:   No class (extended office hours 12:30--2:30)
    • Nov 26: Assignment 4 due on Canvas; Final Exam posted on Canvas
  • Week 15 -- Dec. 03, 05:  Nonparametric & Polynomial + Quantile Regression (Chs. 19-20, 24)
    • Dec 17:  Final Exam due on Canvas

Grading:

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

 

Course Summary:

Date Details Due