Efficient Semiparametric Scoring Estimation of Sample Selection Models

Abstract

A semi parametric profile likelihood method is proposed for estimation of sample selection models. The method is a two step scoring semi parametric estimation procedure based on index formulation and kernel density estimation. Under some regularity conditions, the estimator is asymptotically normal. This method can be applied to estimation of general sample selection models with multiple regimes and sequential choice models with selectivity. For the binary choice sample selection model, the estimator is asymptotically efficiency in the sense that its asymptotic variance matrix attains the asymptotic bound of G. Chamberlain.