Abstract
We study the identification of binary choice models with fixed effects. We
provide a condition called sign saturation and show that this condition is
sufficient for the identification of the model. In particular, we can guarantee
identification even with bounded regressors. We also show that without this
condition, the model is never identified even if the errors are known to have
the logistic distribution. A test is provided to check the sign saturation
condition and can be implemented using existing algorithms for the maximum
score estimator.