08-28, 13:55–14:25 (Europe/Berlin), Room 6
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.
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.
Fast High-Dimensional Fixed Effects Regression (and more!) with PyFixest
Category [Data Science and Visualization] –Statistics
Expected audience expertise: Domain –some
Expected audience expertise: Python –some
Public link to supporting material – Project Homepage / Git –
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.