DijunLiu1995's repositories
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.
Empirical-Method-in-Finance
Winter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices Class intro: Forecasting and Finance The random walk hypothesis Stationarity Time-varying volatility and General Least Squares Robust standard errors and OLS Topic 2: Time-dependence and predictability ARMA models The likelihood function, exact and conditional likelihood estimation Predictive regressions, autocorrelation robust standard errors The Campbell-Shiller decomposition Present value restrictions Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity Time-varying volatility in the data Realized Variance ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns Single- and multifactor models Economic factors: Models and data exploration Statistical factors: Principal Components Analysis Fama-MacBeth regressions and characteristics-based factors
codechella
Data, Code and other material for CodeChella concert
CommonOwnerReplication
Replication for Common Owner 1980-2017
corporate_governance
Scrapers used for corporate governance searching on Factiva and Nexis Uni. Designed for a Wilfrid Laurier University project (cancelled).
cpi
Quickly adjust U.S. dollars for inflation using the Consumer Price Index (CPI)
CrossSection
Code to accompany our paper Chen and Zimmermann (2020), "Open source cross-sectional asset pricing"
DiD_Codes
This repo contains the codes for Baker, Larcker, Wang - "How Much Should We Trust Staggered Difference-in-Differences Estimates?"
doshi-replicate
This repo replicates Doshi et al (2019) and extends the analysis with Stata and Python
EDGAR-Parsing
This repo contains all the code necessary to download, extract, and parse 13F filings on EDGAR.
EquityCharacteristics
Calculate U.S. equity (portfolio) characteristics
factiva-analytics-process
Dow Jones DNA collection, storage and analytics process.
factiva-rtf-db
Script to import Factiva RTF files into a database
Firm-Specific-Intangible-Capital
Firm-Specific Intangible Capital
Gamesmanship-and-Seasonality-in-Stock-Returns
Value Investing Research Awaiting Publication
identifying_price_informativeness
Replication Code for Identifying Price Informativeness
IPP_USLS
Data files and STATA codes for the project "Labor Share Decline and Intellectual Property Products Capital" by Dongya Koh, Raul Santaeulalia-Llopis, and Yu Zheng
lexpredict-lexnlp
LexNLP by LexPredict
lp_var_simul
Simulation study of Local Projections, VARs, and related estimators
openedgar
OpenEDGAR (openedgar.io)
python-causality-handbook
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.
ReplicationCrisis
Code for "Is There a Replication Crisis in Finance" by Jensen, Kelly and Pedersen (2021)
starter-hugo-academic
🎓 Hugo Academic Theme 创建一个学术网站. Easily create a beautiful academic résumé or educational website using Hugo, GitHub, and Netlify.
Technological-Innovation-Resource-Allocation-and-Growth-Replication-Kit
This repository provides the replication code and data for Kogan, L., Papanikolaou, D., Seru, A. and Stoffman, N., QJE 2017.
Value_Return_Predictability_Across_Asset_Classes_and_Commonalities_in_Risk_Premia
This repository contains data and replication code for the paper "Value Return Predictability Across Asset Classes and Commonalities in Risk Premia" forth coming "Review of Finance".