Specification Curve is a Python package that performs specification curve analysis: exploring how a coefficient varies under multiple different specifications of a statistical model.
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Updated
May 16, 2024 - Python
Specification Curve is a Python package that performs specification curve analysis: exploring how a coefficient varies under multiple different specifications of a statistical model.
Statsmodels: statistical modeling and econometrics in Python
Work on Bayesian growth mixture models including hidden Markov chains and softmax regressions for representing latent class memberships.
Official mirror of the actively maintained repo on sourceforge
Quantifying the concentration of banking from 2003-2023 by seeing trends in consolidated assets and merger and acquisition activity of large commercial banks
This work is an approach using machine learning to attempt to reproduce the results of the paper by Biocard and Jusot, 'Milieu d’origine, situation sociale et parcours tabagique en France.' This project uses neural networks.
Econometrics lecture notes with examples using the Julia language
Материалы по курсам Эконометрика, Эконометрика-2, Анализ временных рядов, Анализ панельных данных в МГИМО МИД России
Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
A sample of the works I elaborated by performing Data and Econometric Analysis; as well as my collection of written publications.
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 …
Notebooks for Applied Causal Inference Powered by ML and AI
An R-package of teaching financial machine learning
tldr; If you have a 2-4GB dataset and you need to estimate a (generalized) linear model with a large number of fixed effects, this package is for you.
A comprehensive guide to applied econometrics and causal inference in R. Discusses Randomized Controlled Trials, Instrumental Variables, Regression Discontinuity Design, and Difference-in-Differences.
A Python Package for retrieving Federal Reserve Economic Data at Scale and feeding it to OpenAI
Lightning ⚡️ fast forecasting with statistical and econometric models.
Advanced and Fast Data Transformation in R
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