QingWei2018
I am a Ph.D. in Economics from Clemson University, My research focus is on two-sided markets
Clemson University
Pinned Repositories
BLP-Demand-Estimation
Python code for BLP (Berry, Levinsohn and Pakes) method of structural demand estimation using the random-coefficients logit model. Code for estimation of demand and supply-side moment jointly is also provided.
deploying-machine-learning-models
Code for the online course "Deployment of Machine Learning Models"
EconML
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.
essay2
supply side analysis of ride-sharing on-demand services, a semi-nonparametric IV approach. evidence from NYC June 2016 data.
gitworkshop
inclass workshop for how to use git
hadoop-python-01-workshop
Introduction to Hadoop Workshop
kaggle_JPX_stock_prediction_competition
JPX Tokyo Stock Exchange Prediction- my solution
kaggle_otto_rs
3rd place solution for the OTTO – Multi-Objective Recommender System competition
two_sided_market_entry_simulation
This is the simulation code for my dissertation paper
uberpy
A python wrapper for Uber's API.
QingWei2018's Repositories
QingWei2018/BLP-Demand-Estimation
Python code for BLP (Berry, Levinsohn and Pakes) method of structural demand estimation using the random-coefficients logit model. Code for estimation of demand and supply-side moment jointly is also provided.
QingWei2018/deploying-machine-learning-models
Code for the online course "Deployment of Machine Learning Models"
QingWei2018/EconML
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.
QingWei2018/essay2
supply side analysis of ride-sharing on-demand services, a semi-nonparametric IV approach. evidence from NYC June 2016 data.
QingWei2018/gitworkshop
inclass workshop for how to use git
QingWei2018/hadoop-python-01-workshop
Introduction to Hadoop Workshop
QingWei2018/kaggle_JPX_stock_prediction_competition
JPX Tokyo Stock Exchange Prediction- my solution
QingWei2018/kaggle_otto_rs
3rd place solution for the OTTO – Multi-Objective Recommender System competition
QingWei2018/two_sided_market_entry_simulation
This is the simulation code for my dissertation paper
QingWei2018/uberpy
A python wrapper for Uber's API.
QingWei2018/machine-learning
Content for Udacity's Machine Learning curriculum
QingWei2018/pyomo
The main repository for the Pyomo optimization modeling software.
QingWei2018/ResEcon703
Topics in Advanced Econometrics (ResEcon 703). University of Massachusetts Amherst. Taught by Matt Woerman
QingWei2018/selenium
A browser automation framework and ecosystem.
QingWei2018/STATA-NPIV
This project provides STATA commands for nonparametric estimation of instrumental variable models
QingWei2018/tushare
TuShare is a utility for crawling historical data of China stocks
QingWei2018/uberpy-1
Code to gather data from Uber's API
QingWei2018/Wrapper-of-realtors-historical-price
a python wrapper of www.realtors historical prices