Fast and user-friendly econometrics estimations: The R package fixest

2026-01-29

AI Summary

This study addresses the computational inefficiency and syntactic complexity of traditional econometric methods in high-dimensional fixed-effects models and complex specifications. The authors develop the R package fixest, which implements a C++-based fixed-point acceleration algorithm that substantially improves convergence speed for models with varying-slope fixed effects. The package features a unified, expressive formula interface supporting a wide range of estimators—including fixed effects, instrumental variables, and generalized linear models—alongside built-in robust standard error corrections and efficient inference tools. Demonstrating leading performance across the R, Python, and Julia ecosystems, fixest enables batch model estimation, automated generation of publication-ready regression tables and coefficient plots, and significantly enhances the productivity of empirical research.

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Abstract

fixest is an R package for fast and flexible econometric estimation, providing a comprehensive toolkit for applied researchers. The package particularly excels at fixed-effects estimation, supported by a novel fixed-point acceleration algorithm implemented in C++. This algorithm achieves rapid convergence across a broad class of data contexts and further enables estimation of complex models, including those with varying slopes, in a highly efficient manner. Beyond computational speed, fixest provides a unified syntax for a wide variety of models: ordinary least squares, instrumental variables, generalized linear models, maximum likelihood, and difference-in-differences estimators. An expressive formula interface enables multiple estimations, stepwise regressions, and variable interpolation in a single call, while users can make on-the-fly inference adjustments using a variety of built-in robust standard errors. Finally, fixest provides methods for publication-ready regression tables and coefficient plots. Benchmarks against leading alternatives in R, Python, and Julia demonstrate best-in-class performance, and the paper includes many worked examples illustrating the core functionality.

Problem

• econometrics
• fixed-effects
• estimation
• computational efficiency
• user-friendly

Innovation

• fixed-effects estimation
• fixed-point acceleration
• efficient econometrics
• unified modeling syntax
• robust standard errors