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About
Capybara is a fast and small footprint software that provides efficient functions for demeaning variables before conducting a GLM estimation via Iteratively Weighted Least Squares (IWLS). This technique is particularly useful when estimating linear models with multiple group fixed effects.
The software can estimate GLMs from the Exponential Family and also Negative Binomial models but the focus will be the Poisson estimator because it is the one used for structural counterfactual analysis in International Trade. It is relevant to add that the IWLS estimator is equivalent with the PPML estimator from Santos-Silva et al. 2006
Tradition QR estimation can be unfeasible due to additional memory requirements. The method, which is based on Halperin 1962 article on vector projections offers important time and memory savings without compromising numerical stability in the estimation process.
The software heavily borrows from Gaure 20213 and Stammann 2018 works on the OLS and IWLS estimator with large k-way fixed effects (i.e., the Lfe and Alpaca packages). The differences are that Capybara uses an elementary approach and uses a minimal C++ code without parallelization, which achieves very good results considering its simplicity. I hope it is east to maintain.
The summary tables are nothing like R’s default and borrow from the Broom package and Stata outputs. The default summary from this package is a Markdown table that you can insert in RMarkdown/Quarto or copy and paste to Jupyter.
Demo
Estimating the coefficients of a gravity model with importer-time and exporter-time fixed effects.
library(capybara) mod <- feglm( trade ~ dist + lang + cntg + clny | exp_year + imp_year, trade_panel, family = poisson(link = "log") ) summary(mod)
Formula: trade ~ dist + lang + cntg + clny | exp_year + imp_year Family: Poisson Estimates: | | Estimate | Std. error | z value | Pr(> |z|) | |------|----------|------------|------------|------------| | dist | -0.0006 | 0.0000 | -9190.4389 | 0.0000 *** | | lang | -0.1187 | 0.0006 | -199.7562 | 0.0000 *** | | cntg | -1.3420 | 0.0005 | -2588.1870 | 0.0000 *** | | clny | -1.0226 | 0.0009 | -1134.1855 | 0.0000 *** | Significance codes: *** 99.9%; ** 99%; * 95%; . 90% Number of observations: Full 28566; Missing 0; Perfect classification 0 Number of Fisher Scoring iterations: 9
Installation
You can install the development version of capybara like so:
remotes::install_github("pachadotdev/capybara")
Examples
See the documentation in progress: https://pacha.dev/capybara.
Benchmarks
Median time for the different models in the book An Advanced Guide to Trade Policy Analysis.
| package | PPML | Trade Diversion | Endogeneity | Reverse Causality | Non-linear/Phasing Effects | Globalization |
|---|---|---|---|---|---|---|
| Alpaca | 282ms | 1.78s | 1.1s | 1.34s | 2.18s | 4.48s |
| Base R | 36.2s | 36.87s | 9.81m | 10.03m | 10.41m | 10.4m |
| Capybara | 159.2ms | 97.96ms | 81.38ms | 86.77ms | 104.69ms | 130.22ms |
| Fixest | 33.6ms | 191.04ms | 64.38ms | 75.2ms | 102.18ms | 162.28ms |
Memory allocation for the same models
| package | PPML | Trade Diversion | Endogeneity | Reverse Causality | Non-linear/Phasing Effects | Globalization |
|---|---|---|---|---|---|---|
| Alpaca | 282.78MB | 321.5MB | 270.4MB | 308MB | 366.5MB | 512.1MB |
| Base R | 2.73GB | 2.6GB | 11.9GB | 11.9GB | 11.9GB | 12GB |
| Capybara | 339.13MB | 196.3MB | 162.6MB | 169.1MB | 181.1MB | 239.9MB |
| Fixest | 44.79MB | 36.6MB | 28.1MB | 32.4MB | 41.1MB | 62.9MB |
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Continue reading: Introducing Capybara: Fast and Memory Efficient Fitting of Linear Models With High-Dimensional Fixed Effects
Capybara: A Robust Tool for GLM Estimation
Capybara represents a key advancement in software for estimating Generalized Linear Models (GLMs) from the Exponential Family and Negative Binomial models. Its key points of differentiation hinge on time and memory savings, minimal C++ code usage, and ease of maintenance. These facets of development have significant implications for its long-term adoption and use within the context of structural counterfactual analysis in International Trade, as well as other research fields that make broad use of GLMs.
Implications of Capybara’s Approaches
One of the most noteworthy underpinnings of Capybara is the efficiency it provides for demeaning variables before conducting a GLM estimation via Iteratively Weighted Least Squares (IWLS). This is highly advantageous when estimating linear models with multiple group fixed effects.
The speed and small memory footprint are particularly impressive, underlining the benefits of a Halperin 1962 vector projections-based method. Traditional QR estimation, which could become unfeasible due to additional memory requirements, is thus surpassed by Capybara. In doing this, Capybara fortifies itself as a tool that could provide significant benefits for research economies going forward.
Future Developments: A Potential Goldmine of Enhancements
The present differentiators between Capybara and other software packages such as Alpaca and Base R suggest promising potential for enhancements. Given its comparatively lower need for memory allocation and faster processing times, Capybara could evolve to cater to more elaborate statistical analyses without the risk of compromising numerical stability.
The output quality of summary tables generated by Capybara is also an advantage, being similar to those from the Broom package and Stata outputs. This feature might encourage adoption by those who prefer cleaner, easily interpretable outputs. Future additions to this feature could be more customizations and improvements in formatting functions.
Actionable Advice
For Researchers: If you deal with estimation of GLMs from the Exponential Family and Negative Binomial models or directly involved in structural counterfactual analysis, adopting Capybara can likely enhance your productivity. Its succinct and efficient approach can save time, and its use of iterations in lieu of larger memory requirements means it can function exceptionally well even on low-memory systems.
For Developers: Albeit being a revelation for exemplary estimation, Capybara still utilizes elementary C++ code without parallelization. Work on integrating parallel workflows into the system, or optimizing the C++ code further for speedier process execution, could bring out even better results.
Conclusion
As a compact and memory-efficient tool, Capybara has much to offer in GLM estimations and beyond. Its novelty lies in its lean nature, the amenability of its processes and its systematic simplicity. Adapting it for mainstream use and tailoring it meticulously could reshape the way we view GLM estimations – for the better.