EFFICIENT SEMIPARAMETRIC SCORING ESTIMATION OF SAMPLE SELECTION MODELS

A semiparametric likelihood method is proposed for the estimation of s ample selection models. The method is a two-step semiparametric scorin g estimation procedure based on an index restriction and kernel estima tion. Under some regularity conditions, the estimator is root n-consis tent and asymptotically normal. The estimator is also asymptotically e fficient in the sense that its asymptotic covariance matrix attains th e semiparametric efficiency bound under the index restriction. For the binary choice sample selection model, it also attains the efficiency bound under the independence assumption. This method can be applied to the estimation of general sample selection models.