EFFICIENT SEMIPARAMETRIC SCORING ESTIMATION OF SAMPLE SELECTION MODELS
提出一种基于指数限制和核估计的两步半参数评分估计方法，用于估计样本选择模型，该估计量在正则条件下具有根号n一致性和渐近正态性，并达到半参数效率界。
A semiparametric likelihood method is proposed for the estimation of sample selection models. The method is a two-step semiparametric scoring estimation procedure based on an index restriction and kernel estimation. Under some regularity conditions, the estimator is square-root n -consistent and asymptotically normal. The estimator is also asymptotically efficient in the sense that its asymptotic covariance matrix attains the 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.