  Empirical economists are often deterred from the application of fixed effects
binary choice models mainly for two reasons: the incidental parameter problem
and the computational challenge even in moderately large panels. Using the
example of binary choice models with individual and time fixed effects, we show
how both issues can be alleviated by combining asymptotic bias corrections with
computational advances. Because unbalancedness is often encountered in applied
work, we investigate its consequences on the finite sample properties of
various (bias corrected) estimators. In simulation experiments we find that
analytical bias corrections perform particularly well, whereas split-panel
jackknife estimators can be severely biased in unbalanced panels.
