PoissonFE

A Julia package to estimate Poisson regression with fixed effects. The fixed effects are not estimated, similar to Stata's command xtpoisson y x, fe. Consequently, estimation should be faster than GLM.jl for problems with many (thousands) of fixed effects. The package only supports one variable defining the fixed effects. Standard errors are computed following Wooldridge (1999).

Installation

In a Julia repl:

]add https://github.com/ew-git/PoissonFE.jl

Example

The outcomes y and the predictors X should have the same Float type. The array X should be an n x k matrix where n is the number of observations and k is the number of parameters, excluding the fixed effects.

using PoissonFE
y = [1.0, 0.0, 0.0, 0.0, 7.0, 1.0, 0.0, 1.0, 0.0, 0.0, 6.0,
2.0, 3.0, 0.0, 1.0, 6.0, 0.0, 7.0, 0.0, 21.0]
id = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
X = [0.398106 0.496961; -0.612026 -0.224875; 0.34112 -1.11714;
-1.12936 -0.394995; 1.43302 1.54983; 1.9804 -0.743514; -0.367221 -2.33171;
-1.04413 0.812245; 0.56972 -0.501311; -0.135055 -0.510887;
2.40162 -1.21536; -0.03924 -0.0225586; 0.689739 0.701239;
0.0280022 -0.587482; -0.743273 -0.606728; 0.188792 1.09664;
-1.80496 -0.24751; 1.46555 -0.159902; 0.153253 -0.625778;
2.17261 0.900435]
m = PoissonFEModel(y, X, id)
coefs, se = fit(m)
# coefs are the coefficients of x1 and x2, [0.9245, 0.8694]
# se are the standard errors of the coefs, [0.0135, 0.1276]