Course Syllabus

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ECON 310/STAT 376: Econometrics -- Fall 2023

Professor: Mahmoud El-Gamal

TA: Yan-Yu Chiou

Class: TR, 2:30—3:45 p.m., KRF125

Lab: T, 7:00—8:15 p.m., KRF125

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 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 (for mathematical proofs + some empirical and homework exercises):

Useful R resources (for simulations and empirical exercises):

AI-Assisted Learning:

  • I use gptstudio (addin for OpenAI API to access various GPT models) as well as Github Copilot in Rstudio (recommended for this course)
    • Alternatively, you may use VS Code or any other IDE that you prefer
  • I will show you how to use these AI resources effectively, and you will be allowed to use them 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 to Hansen textbook. We will not cover chapters fully and will be supplementing the material)

  • Week 01 -- Aug 22, 24: Regression and Projection Basics (Ch. 2) 
  • Week 02 -- Aug. 29, 31: Conditional Expectations & Projection  (Ch. 2)
  • Week 03 -- Sep. 05, 07: Least Squares Regression & Best Linear Unbiased Estimator  (Chs. 2-3)
  • Week 04 -- Sep. 12, 14: BLUE, GLS, Heteroscedastcity-Consistent and Clustered SEs (Ch. 4)
  • Week 05 -- Sep. 19, 21: Heteroscedasticity and Autocorrelation Consistent SEs + Inference (Chs. 5-7)
  • Week 06 -- Sep. 26, 28: Diff in Diff, Hypothesis testing & Wald Test (Chs. 18, 7-9)
  • Week 07 -- Oct. 03, 05: Inference, Restricted Estimation, Jackknife & Bootstrap (Chs. 9-10)
  • Week 08 -- Oct. 12: Many Variables and Instruments & 2SLS  (Chs. 11-12)
  • Week 09 -- Oct. 17, 19: Linear GMM + Non-linear GMM & Optimization (Ch. 13)
  • Week 10 -- Oct. 24, 26: LDVs: Multinomial & Nested Logit and Ordered Logit & Tobit  (Chs. 25-27)
  • Week 11 -- Oct. 31, Nov. 02: Panel Data and Dynamic Panels (Ch. 17) 
  • Week 12 -- Nov. 07, 09:  Univariate Time Series: Frequency Domain and Time Domain  (Ch. 14)
  • Week 13 -- Nov. 14, 16:  Multivariate Time Series: VAR + SVAR & Frequency Domain  (Ch. 15)
  • Week 14 -- Nov. 21:   Unit Roots, Cointegration & VECM (Ch. 16)
  • Week 15 -- Nov. 28, 30:  Nonparametric & Polynomial + Quantile Regression (Chs. 19-20, 24)

Grading:

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

 

Course Summary:

Date Details Due