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
- Extremum Estimation I: MLE and M-estimators
- Extremum Estimation II: GMM Estimators
- Delta Method and Bootstrap
- Intro to Nonparametrics: kNN, kernels, etc.
- Model Selection and Validation / Intro to ML: Lasso, Ridge, PCA.
- Program Evaluation I: Potential Outcomes and Selection
- Program Evaluation II: Matching, Propensity Scores, and LATE Theorem
- Program Evaluation III: Difference in Difference, Regression Discontinuity
- Program Evaluation IV: Synthetic Control and Marginal Treatment Effects
- Discrete Choice: Multiple Discrete Choice, and Discrete Choice w Endogeneity
- Advanced Machine Learning: Boosting, Bagging, and Decision Trees
- Advanced Panel Data: Causal FE? Dynamic Panel
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]