The R package fixest stands out as one of the fastest and most efficient tools available for econometric fixed-effects estimation, often outperforming established alternatives by orders of magnitude. This speed advantage is crucial for empirical researchers handling large datasets with complex fixed-effects structures, such as multiple categorical effects in panel data or micro-econometric applications.
Short answer: fixest is highly efficient because it combines advanced computational algorithms implemented in C++, smart handling of multiple fixed-effects, and integration with generalized linear models, enabling fast, scalable, and flexible estimation that outpaces other popular packages in R, Stata, and Julia.
Speed and Computational Efficiency
According to benchmarks reported on the fixest GitHub repository and accompanying documentation, fixest consistently delivers faster estimation times compared to other widely used methods. The comparisons include R packages like felm (from lfe), glmmML, alpaca, and MASS, as well as Stata commands like reghdfe and ppmlhdfe, and even Julia’s FixedEffectModels package. These benchmarks were conducted on several model types—ordinary least squares (OLS), Poisson, negative binomial, and logit models—with multiple fixed-effects and challenging data that mimic real-world complexities.
One of the key reasons for fixest’s speed is that it is implemented primarily in C++ (about 17% of the codebase), which allows it to perform intensive matrix computations and iterative procedures more efficiently than pure R implementations. This low-level programming advantage is combined with algorithmic innovations tailored to fixed-effects models, such as optimized demeaning and projection techniques that handle multiple fixed-effects without explicitly creating large dummy variable matrices, which can be computationally prohibitive.
Moreover, fixest uses clever convergence strategies for fixed-effects that can be slow to estimate in traditional approaches, especially in micro-level data with many groups (e.g., employee and firm fixed-effects). By improving convergence speed and stability, fixest reduces the number of iterations needed, further enhancing performance.
Flexibility with Multiple Fixed-Effects and Model Types
Beyond speed, fixest is designed to handle multiple fixed-effects seamlessly across a broad class of models. It supports estimation under OLS as well as generalized linear models (GLMs) like Poisson, negative binomial, and logit. This versatility is particularly valuable because many empirical applications require nonlinear models with fixed-effects, and other packages may not support these or may do so inefficiently.
The package allows users to specify multiple fixed-effects simply and efficiently, enabling complex panel or cross-sectional analyses without cumbersome data manipulation. This user-friendly interface does not come at the cost of performance, as the underlying computations remain optimized.
Comparison to Other Packages and Software
The benchmarking studies referenced in the fixest GitHub repository show that in OLS fixed-effects estimation, fixest outperforms felm, reghdfe (Stata), and FixedEffectModels (Julia) by a significant margin. For GLMs, fixest similarly beats glmmML and alpaca in R, and Stata’s ppmlhdfe and nbreg commands.
The benchmarking setup involved estimating models with one continuous variable and three fixed-effects, replicating estimations ten times to average computing times. Notably, the “difficult” benchmark scenario involved data generated to slow fixed-effects convergence—a common practical challenge—where fixest’s speed advantage was even more pronounced.
These comparisons underscore fixest’s role as a state-of-the-art tool that combines speed, accuracy, and flexibility. Although inspired by the foundational work of these other packages, fixest extends their capabilities and improves computational efficiency.
Integration and Usability
fixest integrates well with the R ecosystem, making it easy to install from CRAN or from a specialized R-universe repository for the latest development versions. Its syntax is user-friendly, allowing researchers to specify models with multiple fixed-effects and various families (e.g., Poisson, negative binomial) with straightforward commands.
The package also provides extensive documentation and examples, facilitating adoption by applied economists and social scientists. The development team acknowledges the contributions of other packages and encourages users to consider them for features not yet covered in fixest, reflecting a collaborative spirit in the R econometrics community.
Broader Context: Why Efficiency Matters in Fixed-Effects Estimation
Fixed-effects models are central in econometrics for controlling unobserved heterogeneity. However, including multiple high-dimensional fixed-effects can dramatically increase computational burden, especially with large datasets common in labor economics, industrial organization, and development economics.
Traditional methods that rely on dummy variable expansions or naive demeaning become infeasible or slow as the number of groups grows. Advanced packages like fixest solve this problem by using algorithms that implicitly handle fixed-effects through efficient matrix operations without explicit dummy creation, enabling researchers to fit models on millions of observations and thousands of fixed-effects groups.
This efficiency opens doors to more ambitious empirical strategies, such as including multiple layers of fixed-effects simultaneously (e.g., individual, firm, and time), allowing for richer controls and more credible causal inference.
Takeaway
fixest’s combination of C++-backed computational speed, flexible support for multiple fixed-effects and GLMs, and user-friendly interface makes it a leading tool in econometric fixed-effects estimation. Its ability to dramatically reduce computing time—often by orders of magnitude—means researchers can tackle large, complex datasets with confidence and agility. As empirical work increasingly relies on big data and sophisticated fixed-effects structures, tools like fixest will be essential for advancing applied economics and social science research.
For researchers interested in fixed-effects estimation, fixest offers a compelling blend of speed, accuracy, and ease of use, backed by rigorous benchmarking against top alternatives. Its continued development and active community promise ongoing improvements and new features.
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For further reading and to explore the package:
- The official fixest GitHub page provides benchmarking details and installation instructions (github.com/lrberge/fixest) - Comprehensive benchmarks comparing fixest to felm, reghdfe, and others are available on the GitHub repository - CRAN hosts the stable fixest releases (cran.r-project.org/package=fixest) - Discussions and tutorials on r-bloggers.com often cover fixest alongside other econometric tools - For understanding fixed-effects models and their computational challenges, econometrics textbooks and papers by Berge (2018) and others are recommended
These resources collectively illustrate why fixest is currently the fastest and most efficient R package for fixed-effects estimation in econometrics.
