Popular GitHub Repositories for Econometrics

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.

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

LightweightMMM ? is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.

DoubleML - Double Machine Learning in Python

Machine learning based causal inference/uplift in Python

Books and Papers in Mathematics, Econometrics, Machine Learning, Finance etc for different levels that can be useful for Data Scientists, Developers and everyone whoo is interesting in STEM.

Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms

Forecasting, anomaly detection, predictive analytics, econometrics and much more

R code and Realized Volatility (RV) series set for fitting NN-based-HAR models to multinational RV series.

DoubleML-Serverless - Distributed Double Machine Learning with a Serverless Architecture

Out-Of-Sample Time Series Forecasting: OOS introduces a comprehensive framework for time series forecasting with traditional econometric and modern machine learning techniques.

Решение задач по анализу временных рядов: детекция пиков QRS на сигналах ЭКГ с помощью ML, прогнозирование заболеваемости COVID с помощью LSTM и др.

Improved Mortality Forecasts using Artificial Intelligence.

The software includes a dynamic bayesian network with genetic feature space selection, includes 5 econometric data.frames with 263 time series.

Hybridization approach for time-series forecasting

A new Echo State Network library

Bayesian Market Segmentation Algorithm for Hedonic Analysis

End to end natural language processing, machine learning and data engineering pipelines for a social media text based cryptocurrency uncertainty index.

Univariate time series forecasting in Python of commodity prices using the Autoregressive Integrated Moving Average (ARIMA) model.