Alexander Fischer
I'm a former academic economist - that's why I like linear regression so much. Nowadays I work as a Data Scientist and spend most of my week working on online auctions at Trivago. After hours, I work and open source packages for regression modeling and inference in R and Python.
Trivago
Twitter handle –@s3alfisc
Git*hub|lab –Session
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 and randomization inference. 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 argue why there is a need for another regression package in Python, cover PyFixest's functionality and design philosophy, and discuss future development prospects.