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bife

Binary Choice Models with Fixed Effects

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) <doi:10.1016/j.jeconom.2009.02.007>.

Functions (18)

bias_corr

Asymptotic bias correction for binary choice Models with fixed effects

bife

Efficiently fit binary choice models with fixed effects

bife_control

Set bife Control Parameters

coef.bife

Extract estimates of structural parameters or fixed effects

coef.bifeAPEs

Extract estimates of average partial effects

fitted.bife

Extract bife fitted values

get_APEs

Compute average partial effects for binary choice models with fixed effects

logLik.bife

Extract log-likelihood

predict.bife

Predict method for bife fits

print.bife

Print bife

print.bifeAPEs

Print bifeAPEs

print.summary.bife

Print summary.bife

print.summary.bifeAPEs

Print summary.bifeAPEs

psid

Female labor force participation

summary.bife

Summarizing models of class bife

summary.bifeAPEs

Summarizing models of class bifeAPEs

vcov.bife

Extract estimates of the covariance matrix

vcov.bifeAPEs

Extract estimates of the covariance matrix

bife

Binary Choice Models with Fixed Effects

v0.7.1
GPL (>= 2)
Authors
Amrei Stammann [aut, cre], Daniel Czarnowske [aut] (<https://orcid.org/0000-0002-0030-929X>), Florian Heiss [aut], Daniel McFadden [ctb]
Initial release

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