Course Syllabus

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ECON 310/STAT 376: Econometrics — Fall 2025

Professor: Mahmoud El-Gamal

TAs: TBA

Class: MW, 2:00—3:15 p.m., TBA

Lab: T, 7:00—8:15 p.m., TBA

Office hours: M 12:30—1:30 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):

Useful R Resources (Optional):

AI-Assisted Learning (Optional):

  • 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: (Lecture slides and codes under Files tab)

  • Week 01 -- Aug. 25, 27: Projection & Conditional Expectations basics (Ch. 2) 
  • Week 02 -- Sep. 03: Conditional Expectations & Projection  (Ch. 2)
    • Sep. 03: Assignment 1 posted on Canvas
  • Week 03 -- Sep. 8, 10: Least Squares Regression & Best Linear Unbiased Estimator  (Chs. 2-3)
  • Week 04 -- Sep. 15, 17: BLUE, GLS, Heteroscedastcity-Consistent and Clustered SEs (Ch. 4)
    • Sep 15: Assignment 1 due on Canvas; Assignment 2 posted on Canvas
  • Week 05 -- Sep.22, 24: Heteroscedasticity and Autocorrelation Consistent SEs + Inference (Chs. 5-7)
  • Week 06 -- Sep. 29, Oct. 01: Diff in Diff, Hypothesis testing & Wald Test (Chs. 18, 7-9)
    • Sep 29: Assignment 2 due on Canvas, Midterm 1 posted on Canvas
  • Week 07 -- Oct. 06, 08: Inference, Restricted Estimation, Jackknife & Bootstrap (Chs. 9-10)
  • Week 08 -- Oct. 15: Many Variables and Instruments & 2SLS  (Chs. 11-12)
    • Oct 15: Midterm 1 due on Canvas; Assignment 3 posted on Canvas
  • Week 09 -- Oct. 20, 22: Linear GMM + Non-linear GMM & Optimization (Ch. 13)
  • Week 10 -- Oct. 27, 29: LDVs: Multinomial & Nested Logit and Ordered Logit & Tobit  (Chs. 25-27)
    • Oct 29: Assignment 3 due on Canvas; Midterm 2 posted on Canvas
  • Week 11 -- Nov. 03, 05: Panel Data and Dynamic Panels (Ch. 17) 
  • Week 12 -- Nov. 10, 12:  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. 17, 19:  Multivariate Time Series: VAR + SVAR &  Unit Roots (Chs. 15-16)
  • Week 14 -- Nov. 24:   Cointegration & VECM (Chs. 15-16)
    • Nov 24: Assignment 4 due on Canvas; Final Exam posted on Canvas
  • Week 15 -- Dec. 01, 03:  Nonparametric & Polynomial + Quantile Regression (Chs. 19-20, 24)
    • Dec 16:  Final Exam due on Canvas

Grading:

  • Four Assignments: 30%
  • Two Midterm Exams: 40%
  • Final Exam: 30%

 

Course Summary:

Date Details Due