Mostly Harmless Fixed Effects Regression in Python with PyFixest
Alexander Fischer
This session introduces PyFixest, an open source Python library inspired by the "fixest" R package. PyFixest implements fast routines for the estimation of regression models with high-dimensional fixed effects, including OLS, IV, and Poisson regression. The library also provides tools for robust inference, including heteroscedasticity-robust and cluster robust standard errors, as well as the wild cluster bootstrap. Additionally, PyFixest implements several routines for difference-in-differences estimation with staggered treatment adoption.
PyFixest aims to faithfully replicate the core design principles of "fixest", offering post-estimation inference adjustments, user-friendly syntax for multiple estimations, and efficient post-processing capabilities. By making efficient use of jit-compilation, it is also one of the fastest solutions for regressions with high-dimensional fixed effects.
The presentation will cover PyFixest's functionality, design philosophy, and future development prospects.
Alexander Fischer
Affiliation: Trivago
Economist and Data Scientist. I spend most of my week working on online auctions at Trivago and open source packages for regression modeling and inference in R and Python.
visit the speaker at: Github
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