Disclaimer: These notes are wriiten for future reference and NOT intended to be comprehensive and for publication. They are NOT original research. Materials are from various sources including textbooks, published papers and online posts, which are, to the best of my efforts, cited as references and acknowledged. They are incomplete and subject to active changes, and inevitably contain errors. Use them at your own risk.
I summarize reading and research notes by topics as below. Some of them serve for pedagogical purposes. There exist excellent writings on most topics covered here which are referred to in respective notes. In some research notes, instead of pursuing comprehensiveness or reinventing wheels, I try to adopt a just-in-time approach to demonstrate how technical tools are used in research with an emphasis on my personal (potentially biased or wrong) understanding.
These notes are hosted on this Github repository. Some of them overlap with posts in technical blogs. Feel free to clone for your own use. Any comments and contribution are welcomed. Please feel free to send pull requests on Github or emails to zhangao [at] usc [dot] com or gaozhan [dot] cuhk [at] gmail [dot] com.
Pedagogical Notes
- Notes on technical tools for econometrics research
- Notes on convex optimization
- Introduction to R for econometrics and data science
- We want it all: How to be multilingual in R, Python and Julia for econometrics
- My workflow
Research Notes
Econometric Methods
- Notes on optimization-conscious econometrics
- Based on Optimization-Conscious Econometrics by Guillaume A. Pouliot.
- Notes on privacy-conscious econometrics
- Notes on program evaluation with panel data: Diff-in-fiff, synthetic control and beyond
- Notes on high-dimensional methods
- Notes on (high-dimenional) IV
Short Technical Notes
Reference Lists
Computation Tricks
License
These works are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.