bife {bife}  R Documentation 
Efficiently fit binary choice models with fixed effects
Description
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.
Remark: The term fixed effect is used in econometrician's sense of having a full set of individual specific intercepts. All other parameters in the model are referred to as structural parameters.
Usage
bife(
formula,
data = list(),
model = c("logit", "probit"),
beta_start = NULL,
control = list(),
bias_corr = NULL,
tol_demeaning = NULL,
iter_demeaning = NULL,
tol_offset = NULL,
iter_offset = NULL
)
Arguments
formula 
an object of class 
data 
an object of class 
model 
the description of the error distribution and link function to be used in the model.
For 
beta_start 
an optional vector of starting values used for the structural parameters in the optimization algorithm. Default is zero for all structural parameters. 
control 
a named list of parameters for controlling the fitting process. See

bias_corr 
deprecated; see 
tol_demeaning , iter_demeaning , tol_offset , iter_offset 
deprecated; see 
Details
bife
drops all observations of crosssectional units (individuals) with
nonvarying response. This can de done because these observations do not contribute to the
identification of the structural parameters (perfect classification).
If bife
does not converge this is usually a sign of linear dependence between
one or more regressors and the fixed effects. In this case, you should carefully inspect
your model specification.
Value
The function bife
returns a named list of class "bife"
.
References
Stammann, A., F. Heiss, and D. McFadden (2016). "Estimating Fixed Effects Logit Models with Large Panel Data". Working paper.
Examples
# Load 'psid' dataset
library(bife)
dataset < psid
# Fit a static logit model
mod < bife(LFP ~ I(AGE^2) + log(INCH) + KID1 + KID2 + KID3 + factor(TIME)  ID, dataset)
summary(mod)