This is a PhD level course in Microeconometrics targeted at students conducting applied research (as opposed to econometricians).

In addition to traditional econometric approaches, this course draws connections to recent literature on machine learning.

It is based on some combination of

• Microeconometrics by Cameron and Trivedi
• Elements of Statistical Learning by Hastie, Tibshirani, and Friedman
• Other lectures borrowed/stolen from various sources

The goal is to provide an overview of a number of topics in Microeconometrics including:

1. Nonparametrics and Identification
   • k-NN, Kernels, Nadaraya-Watson
   • Bootstrap and Cross Validation
2. Model Selection and Penalized Regression
   • Ridge, Lasso, LAR, BIC, AIC
3. Treatment Effects and Selection
   • Potential Outcomes, LATE, Diff in Diff, RDD, MTE
4. Binary Discrete Choice (including endogeneity)
   • MLE, Special Regressors, Control Functions
5. Multinomial Discrete Choice
   • Logit, Nested Logit, Mixed Logit
6. Dynamic Discrete Choice
   • Rust Models (NFXP), Hotz+Miller (CCP)
7. Duration Models
8. Bayesian Methods
   • Empirical Bayes, MCMC, James-Stein

Over the course of the semester I expect each of my students to find at least two typos or other errors and fix them via a pull request.

You are free to use these notes. However, PLEASE CREATE A FORK.

You are welcome to submit pull requests/update to my notes as well.

Everything is distributed under Creative Commons Attribution Share Alike 4.0 (You can use it freely but you are expected to post source of derivative work).

Contact: [email protected]