This is a PhD level course in Applied Econometrics at NYU Stern.
The course is much more applied than my micro-metrics course (http://www.github.com/chrisconlon/micro-metrics)
In addition to traditional econometric approaches, this course draws connections to recent literature on machine learning.
The following textbooks may be useful for additional information:
- Econometric Analysis by Bill Greene
- Elements of Statistical Learning by Hastie, Tibshirani, and Friedman
- Other lectures borrowed/stolen from various sources
These are more advanced treatments:
The rough outline of the course is as follows:
- Introductory Time Series
- Panel Data I: Fixed Effects, Random Effects, Clustering
- Panel Data II: Dynamic Panel Data, Causal FE, Empirical Bayes
- Nonlinear Estimation: GMM and MLE
- Discrete Choice and Multinomial Choice
- Bayesian Methods and MCMC
- Treatment Effects and Potential Outcomes (Part I): Matching, Propensity Scores, LATE
- Treatment Effects and Potential Outcomes (Part II): Difference in Difference, RDD, etc.
- Nonparametric Methods: Kernels, KNN, Bootstrap
- Nonparametric Methods: Kernels, KNN, Bootstrap
- Machine Learning: Regulaarization, Model Selection, Data Reduction (Ridge, LASSO, PCA)
- Additional Topics Based on Interest
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]