Online Learning Materials
Online Learning Materials
General Econometrics
- My advisor Zhentao Shi has developed an amazing set of lecture notes for master and Ph.D. level econometrics courses.
- Guillaume A. Pouliot’s Optimization-Conscious Econometrics course is one of a kind that introduces frontier econometric methods relying on advancement of optimization.
- Paul Goldsmith-Pinkham’s Applied Empirical Methods course.
- Peter Hull’s Applied Econometrics Lecture Slides.
- Grant R. McDermott’s course materials in EC607 at U. Oregon are the best for introduction to statistical programming in R and data science for economics majors.
Mathematics / Statistics for Econometricians
- My Alma Mater CUHK Math Department’s course list. Most of the course materials are open access and constantly updated. I keep reviewing the notes after graduation and always learn something new.
- Larry Wasserman’s lecture notes in 36-705 Intermediate Statistics. This is an awesome starting point for gentle introduction to topics including concentration inequalities, empirical processes, high-dimensional statistics, minimax bounds, etc. before we dive into the classical textbooks like van de Vaart 98’, van de Vaart & Wellner 96’ & 23’, Wainwright 19’ and others. The course 36-708 Statistical Methods for Machine Learning
- Bodhisattva Sen’s lecture notes on empirical process theory, semiparametric statistics, nonparametric statistics and empirical Bayes are the best companions when we learn the corresponding topics. They are more gentle and easy-to-access versions of comprehensive textbooks like van de Vaart & Wellner 96’ & 23’, Bickel et al 93’, Giné & Nick, 16’.
High-dimensional Statistics
- Solutions to the exercises from High-Dimensional Statistics: A Non-Asymptotic Viewpoint by Martin J. Wainwright organized by Jiri Hron and Wessel Bruinsma.
- Machine Learning for Econometrics by Christophe Gaillac and Jérémy L’Hour is a very accessible introduction to high-dimensional methods for causal inference. Chapters 1 to 5 are particularly helpful for understanding the basic theory of Lasso.
- High-dimension statistics course by Philippe Rigollet at MIT.
Causal Inference
- Applied Causal Inference Powered by ML and AI by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis.
- A series of short nontechnical videos from Matt Masten about causal inference.
- Mixtape-Sessions for causal inference.
- Brady Neal - Causal Inference: Course on causal inference.
- DID has almost everything you want to know about Difference-in-difference and is constantly updated.
Bayesian and Empirical Bayes
- Bodhisattva Sen’s lecture notes on empirical Bayes.
- SI 2022 Methods Lectures - Empirical Bayes Methods, Theory and Application.
What keeps a Bayesian awake at night? Part1: Day time Part 2: Night time; By Wessel Bruinsma, Andrew Y. K. Foong, Richard E. Turner. - Chamberlain Seminar: Empirical Bayes: Methods and Applications by Roger Koenker and José Luis Montiel Olea.
Reinforcement Learning
- Chengchun Shi’s lecture notes on reinforcement learning.
- Steve Brunton offers courses on reinforcement learning, data science, math, etc.
Differential Privacy
- NBER SI 2020 Methods Lectures - Differential Privacy for Economists.
- Introductory Readings in Formal Privacy for Economists by John M. Abowd, Ian M. Schmutte, William Sexton, and Lars Vilhuber.
- Gautam Kamath’s course CS 860 - Algorithms for Private Data Analysis at University of Waterloo.
Optimization
- Lectures on Stochastic Programming: Modeling and Theory, Third Edition
Online Seminars
- The Gary Chamberlain Online Seminar in Econometrics
- Online Causal Inference Seminar
Technical Tutorials
- Gilles Castel’ posts on research workflow and taking notes using LaTeX are amazing. Pingbang Hu develops a similar setup for VS code users (Link). I build my own workflows based on their contributions.
- How to build an academic website? Check out this tutorial by Rob Williams.
Starred Github Repository
- https://github.com/zhan-gao?tab=stars
Advice on Academic Life
- Jonathan Dingel’s advice for Ph.D. Students and the links therein.
- Terrence Tao’s career advice.
- Weinberg, Steven. “Four golden lessons.” Nature 426, no. 6965 (2003): 389-389.
- Aaditya Ramdas: Checklists for Stat-ML PhD students.
- Paul Niehaus on Doing Research.
- Peter Norvig: Teach Yourself Programming in Ten Years.
- Nathan Marz: You should blog even if you have no readers.
Blogs and YouTube Channels
- My friend Rongpeng Li posts videos about mathematics, coding and any topics that are interesting for naively curious mind. Check the channel out.
- Richard Xu on Zhihu, and his WeChat Official Account 卢卡斯的岛.
- Takuya Matsuyama’s devaslife channel is the coolest one I found on YouTube. I learned a lot about the Vim editor, how to operate with terminals, web development, etc.