I teach PhD Micro-Econometrics at Kansas State University. This is a second year PhD course that aims to introduce important econometric tools used in empirical micro research.
I was also the TA for the corresponding course at Boston University for 4 years. See below for links to some of my discussion slides.
Textbooks:
Joshua D. Angrist and Jörn-Steffen Pischke, Mostly Harmless Econometrics.
J . M. Wooldridge, Econometric Analysis of Cross Section and Panel Data.
A. Colin Cameron and Pravin K. Trivedi, Microeconometrics: Methods and Applications.
Course Outline:
1. Causality and potential outcomes framework
2. Instrumental variables and LATE
3. SUTVA, incidental parameters problem, improving precision, randomization schemes, bad controls
4. Difference-in-differences
5. Event study, Synthetic controls
6. Clustering standard errors
7. Propensity scores and Matching
8. Weighting regressions
9. Oster bias correction
10. Multiple hypothesis testing
11. Regression discontinuity
12. Selection models
13. Handling zeros and effects of measurement units
14. Discrete choice models
15. Count data
16. Numerical optimization methods
"Numerical Methods in Economics" by Kenneth Judd, Chapter 4
17. Miscellaneous topics (if time permits): Randomization inference, bootstrapping standard errors, Bartik instruments, Judge instruments, simulation methods, nonparametrics, control functions.
I was the TA for Graduate Microeconometrics (2nd year PhD course) from Fall 2019-2022 at Boston University. The slides I made for some of my weekly discussions can be found below. They all borrow from various sources, and I've tried my best to list all the references.
Weighting, Oster bias correction: Summarizes Solon-Haider-Wooldridge (2015) and Oster (2019)
Clustering standard errors: MacKinnon and Webb (2019), Abadie-Athey-Imbens-Wooldridge (2017)
Nonparametric identification (with applications in auctions)
Auction - Applications I : Ben Rosa (RAND)
Auction - Applications 2: Athey, Levin and Seira (2011)