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This study introduces capybara, an R package for efficient generalized linear model (GLM) estimation with high-dimensional fixed effects. It significantly reduces computation time and memory usage for complex economic models.

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Area of Science:

  • Econometrics
  • Computational Statistics
  • Software Development

Background:

  • Generalized linear models (GLMs) are crucial for analyzing complex datasets.
  • Estimating GLMs with high-dimensional fixed effects presents significant computational challenges.
  • Existing methods often require excessive memory and computation time.

Purpose of the Study:

  • Introduce capybara, an R package designed for computationally efficient GLM estimation.
  • Provide a memory-efficient solution for models with high-dimensional fixed effects.
  • Make complex econometric models computationally tractable on standard hardware.

Main Methods:

  • Implement algorithms combining the Frisch-Waugh-Lovell (FWL) theorem with alternating projections.
  • Develop an R package (capybara) for practical application of these methods.
  • Benchmark performance against traditional dummy variable approaches.

Main Results:

  • capybara achieves 95-99% reduction in computation time compared to base R.
  • Estimation is memory-efficient, using only 33 MB for a large gravity model.
  • Numerical accuracy is maintained to 5 decimal places.
  • A complex gravity model with 3,200 fixed effects was estimated in 6 seconds.

Conclusions:

  • capybara offers a computationally efficient and memory-saving solution for GLMs with high-dimensional fixed effects.
  • The package is particularly beneficial for trade and labor economics research.
  • Enables the estimation of previously infeasible models on standard computing resources.