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eloualiche merged 1 commit into from
Jan 27, 2026
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Update README with performance and GPU information #273

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21 changes: 9 additions & 12 deletions README.md
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Expand Up @@ -6,7 +6,7 @@ This package estimates linear models with high dimensional categorical variables
The package is registered in the [`General`](https://github.com/JuliaRegistries/General) registry and so can be installed at the REPL with `] add FixedEffectModels`.

## Benchmarks
The objective of the package is similar to the Stata command [`reghdfe`](https://github.com/sergiocorreia/reghdfe) and the R packages [`lfe`](https://cran.r-project.org/web/packages/lfe/lfe.pdf) and [`fixest`](https://lrberge.github.io/fixest/). The package is much faster than `reghdfe` or `lfe`. It also tends to be a bit faster than the more recent `fixest` (depending on the exact command). For complicated models, `FixedEffectModels` can also run on Nvidia GPUs for even faster performances (see below)
The objective of the package is similar to the Stata command [`reghdfe`](https://github.com/sergiocorreia/reghdfe) and the R packages [`lfe`](https://cran.r-project.org/web/packages/lfe/lfe.pdf) and [`fixest`](https://lrberge.github.io/fixest/). The package is much faster than `reghdfe` (Stata) or `lfe` (R). It also tends to be a bit faster than the more recent `fixest` (R), especially when computing clustered standard errors. `FixedEffectModels` can also run on NVIDIA or Apple GPUs for even faster performances (see below)


![benchmark](http://www.matthieugomez.com/files/fixedeffectmodels_benchmark.png)
Expand Down Expand Up @@ -123,34 +123,31 @@ Finally, you can use [RegressionTables.jl](https://github.com/jmboehm/Regression
`FixedEffectModels` is multi-threaded. Use the option `nthreads` to select the number of threads to use in the estimation (defaults to `Threads.nthreads()`).

### GPUs
The package has an experimental support for GPUs. This can make the package an order of magnitude faster for complicated problems.
The package has an experimental support for GPUs. This can make the package 2x-5x faster for complicated problems.

If you have a Nvidia GPU, run `using CUDA` before `using FixedEffectModels`. Then, estimate a model with `method = :CUDA`.
If you have a NVIDIA GPU, run `using CUDA` before `using FixedEffectModels`. Then, estimate a model with `method = :CUDA`.
```julia
using CUDA, FixedEffectModels
using CUDA, FixedEffectModels, RDatasets
@assert CUDA.functional()
df = dataset("plm", "Cigar")
reg(df, @formula(Sales ~ NDI + fe(State) + fe(Year)), method = :CUDA)
```

The package also supports Apple GPUs with `Metal.jl`, although I could not find a way to get better performance
If you have an Apple-designed GPU, run `using Metal` before `using FixedEffectModels`. Then, estimate a model with `method = :Metal`.
```julia
using Metal, FixedEffectModels
using Metal, FixedEffectModels, RDatasets
@assert Metal.functional()
df = dataset("plm", "Cigar")
reg(df, @formula(Sales ~ NDI + fe(State) + fe(Year)), method = :Metal)
```



## Solution Method
## Solution method
Denote the model `y = X β + D θ + e` where X is a matrix with few columns and D is the design matrix from categorical variables. Estimates for `β`, along with their standard errors, are obtained in two steps:

1. `y, X` are regressed on `D` using the package [FixedEffects.jl](https://github.com/FixedEffects/FixedEffects.jl)
2. Estimates for `β`, along with their standard errors, are obtained by regressing the projected `y` on the projected `X` (an application of the Frisch Waugh-Lovell Theorem)
2. Estimates for `β`, along with their standard errors, are obtained by regressing the projected `y` on the projected `X` (an application of the Frisch Waugh-Lovell Theorem)
3. With the option `save = true`, estimates for the high dimensional fixed effects are obtained after regressing the residuals of the full model minus the residuals of the partialed out models on `D` using the package [FixedEffects.jl](https://github.com/FixedEffects/FixedEffects.jl)

# References
Here are some references for the solution method:

Baum, C. and Schaffer, M. (2013) *AVAR: Stata module to perform asymptotic covariance estimation for iid and non-iid data robust to heteroskedasticity, autocorrelation, 1- and 2-way clustering, and common cross-panel autocorrelated disturbances*. Statistical Software Components, Boston College Department of Economics.

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