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Binary Choice Models with Fixed Effects
An R-package to estimate fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and computes average partial effects. Incidental parameter bias can be reduced with an asymptotic bias-correction proposed by Fernandez-Val (2009).
bife can be used to fit fixed effects binary choice
models (logit and probit) based on an unconditional maximum likelihood
approach. It is tailored for the fast estimation of binary choice models
with potentially many individual fixed effects. The routine is based on
a special pseudo demeaning algorithm derived by Stammann, Heiss, and
McFadden (2016). The estimates obtained are identical to the ones of
glm(), but the computation time of bife() is
much lower.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.