Course Description

Econometrics II (CMU course number 73-374) is an advanced undergraduate course on Econometrics with a focus on methods used in practice in contemporary empirical economic research. Topics include causal inference, panel data, nonlinear methods, and time series, with a focus on real data applications and code examples in R. As the name suggests, the course is designed to be a successor to Econometrics I, and so presumes familiarity with probability and statistics, linear regression, and the R statistical programming language. The primary texts for the course are Introductory Econometrics: A Modern Approach (4th edition) by Jeffrey Wooldridge and Mastering ’Metrics by Joshua Angrist and Jörn-Steffen Pishke, with some coverage of additional topics not covered in these texts including causal graphs (Lectures 6 and 7) and nonparametric regression (Lecture 15).

Course Materials

The following files, derived from the lecture slides for the course and containing both text and R code, are provided as-is, as a resource for students and researchers interested in the topics. Additional course materials, including syllabi, problem sets, practice problems, and project assignments, may be available upon request. If you have questions, comments, or criticisms of the material, please contact me.

  1. Introduction and Regression
  2. Multivariate Regression
  3. Misspecification in Linear Models
  4. Inference and Causality
  5. Causality II: Experiments and Control
  6. Causality III: Structural Models
  7. Control and Instrumental Variables
  8. Instrumental Variables: Potential Outcomes Approach
  9. Multivariate Instrumental Variables
  10. More Applications of Instrumental Variables
  11. Panel Data, Event Studies, and Difference in Differences
  12. Panel Data II: Unobserved Effects
  13. Nonlinear Estimation
  14. Nonlinear Estimation II: Inference
  15. Nonparametric Regression
  16. Regression Discontinuity
  17. Maximum Likelihood and Binary Choice
  18. Maximum Likelihood II: Discrete Outcome Models
  19. Replication
  20. Time Series
  21. Time Series II: Inference
  22. Nonstationary Time Series
  23. (Optional Topic) Cointegration