Estimates 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)
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.