shizelong1985 Goto Github PK

ebci_matlab

Matlab code for robust empirical Bayes confidence intervals

ec4308-credit-default-risk

Prediction of Credit Default Risk (Boolean) using Machine Learning and Deep Learning methods such as PCA, PLS, Regression Trees, Random Forest, Boosted Trees, ANN, CNN, LSTM

ecom025_2020

Financial Econometrics module (MSc level)

econ-690-highfrequencyeconometrics

高频计量课件及codeCode and documents from Econ 690 at Duke

econ220e-final-project

Final project for Econ 220E at UCSD, replicates results from Feng, Giglio, Xiu (2020) Journal of Finance in R

econ3070_f2022

CU Boulder Economics 3070 Fall 2022

econ3535_s2022

econ410_macroeconomic_theory

ECON410: Macroeconomic_Theory

econ5170

Econ5170@CUHK: Computational Methods in Economics (2020 Spring).

econ_407_fall2020

Course material for ECON407, Fall 2020

econ_4750_notes

econ_data_science

R在经济学中的应用

econ_layers

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.

econometric-algorithms

Popular Econometrics content for students and researchers who wants to learn about regression analysis (in STATA/Python/R), how to test hypothesis and perform statistical tests.

econometric_theory_slides

Slides for A Primer in Econometric Theory

econometrics-playground

Fun repository of exploratory code related to miscellaneous econometric methods.

econometrics_at_scale

econometricsresources

经济学相关专业资料集

econometricsslides

This is the repository for the slides used in the Seattle University Econometrics course

eiglearn

Perturbing Eigenvalues with Residual Learning in GCN

elias_2016a

Replication code for Elias (2016), "Asset Pricing with Expectation Shocks", Journal of Economic Dynamics and Control.

embeddingportfolio

A repository for portfolio allocation based on embedding data representation

emdfm

R package for Dynamic Factor Models estimation and forecast evaluation, using the Expectation Maximization algorithm

empecon

Empirical Economics with R

empirical-asset-pricing

empirical-asset-pricing-a-2021

This repo contains mainly codes for homework from PhD course at INSEAD

empirical-asset-pricing-via-machine-learning-evidence-from-the-german-stock-market

Machine learning methods for identifing investment factors

empirical-finance

A tutorial of empirical finance (It will be not updated)

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