Name: Zhan Gao
Type: User
Company: University of Southern California
Bio: Ph.D. Student at Department of Economics, University of Southern California
Location: Los Angeles, CA, US
Blog: https://zhan-gao.github.io
Zhan Gao's Projects
Set of solutions for the Advanced R programming book
Prototype code for paper: Adversarial Generalized Method of Moments, Greg Lewis and Vasilis Syrgkanis
This is the accompanying repository for "On LASSO for Predictive Regression".
A PhD course in Applied Econometrics and Panel Data
PhD Applied Econometrics class taught at UC Berkeley
Bubble test based on Phillips, Shi and Yu (2015) and the MultipleBubbles package.
The replication data and files for Liangjun Su, Zhentao Shi and Peter Phillips (2015): โIdentifying Latent Structures in Panel Dataโ
My Cambridge Lecture Notes
A Python package for modular causal inference analysis and model evaluations
Working repository for Causal Tree and extensions
companion R package for Gao and Pesaran (2023) Identification and estimation of categorical coefficient regression model
A package implements Classifier-Lasso
:notebook:Solutions to Introduction to Algorithms
This is the accompanying repository for "Implementing Convex Optimization in R: Two Econometric Examples"
a curated list of R tutorials for Data Science, NLP and Machine Learning
Difference in Differences with Multiple Periods
Keeping track of what is going on with the latest DiD innovations.
R-code to compare some staggered did methods
R package to find the difference-in-transports estimator
Econ 5170 @CUHK: Computational Methods in Economics (2017 Spring)
Summer preparatory course for MSc students
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
Forecasting Inflation in a data-rich environment: the benefits of machine learning methods
Use ggplot2 to plot China map, and provide a JSON file of coordinates of major Chinese cities
Sensitivity analysis in GMM
Generalized Random Forests
Set of R functions for high-dimensional econometrics