# are213

**Repository Path**: econometric/are213

## Basic Information

- **Project Name**: are213
- **Description**: https://github.com/borusyak/are213
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

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- **Created**: 2025-10-27
- **Last Updated**: 2025-10-27

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## README

# ARE213 PhD Applied Econometrics
Taught by me, [Kirill Borusyak](https://sites.google.com/view/borusyak/), at UC Berkeley's Department of Agricultural and Resource Economics, Fall 2024

Please email me at [k.borusyak@berkeley.edu](mailto:k.borusyak@berkeley.edu) if you notice typos, mistakes, or have other suggestions for how to improve the course.

Course outline:

A. Introduction: regression and causality 

- A1.	Key facts about regression

- A2.	Potential outcomes and randomized control trials

B. Selection on observables 

- B1.	Regression adjustment
  
- B2.	Matching and propensity score methods
  
- B3.	Doubly-robust methods

C. Panel data methods 

- C1.	Linear panel data methods recap
  
- C2.	Canonical difference-in-differences (DiD) and event studies
  
- C3.	DiD with staggered adoption
  
- C4.	Synthetic control methods and factor models

D. Instrumental variables (IVs) 

- D1.	IV idea and mechanics. Weak instruments
  
- D2.	IV with heterogeneous effects
  
- D3.	Formula instruments, recentering, spillovers, shift-share IV
  
- D4.	Examiner designs (“judge IVs”)

E. Regression discontinuity (RD) designs 

- E1.	Sharp RD designs
  
- E2.	RD extensions: fuzzy RD, spatial RD, RD extrapolation, and more

F. Miscellaneous topics: Models with multiplicative effects and Poisson regression; More on statistical inference

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Materials are distributed under the [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/deed.en) license.