Abstract
Binary choice models, in which the dependent variable takes on the values of 0 or 1, are widely used in the various fields of social and natural sciences. These models are usually estimated by the probit or logit maximum likelihood method. However, unlike standard regression models, these estimators are inconsistent if the distribution of the error term is not correctly specified.
Recently, a great deal of effort has been spent by econometricians and statisticians to find out distribution-free or semiparametric estimation methods of binary choice models. This paper summarizes important developments of these methods.
