Two-step estimation of heteroskedastic sample selection models
提出一种两步估计方法，处理潜误差存在未知形式异方差的样本选择模型，使用非参数级数逼近，证明估计量的一致性和渐近正态性，蒙特卡洛实验显示其优于传统Heckman估计。
This paper considers two-step estimation of a sample selection model in which there is heteroskedasticity of unknown form in the latent errors. We propose an estimator which uses recent developments in nonparametric regression estimation involving series approximations. The estimator is shown to be consistent and asymptotically normally distributed under reasonable conditions. A small Monte Carlo experiment demonstrates the usefulness of the estimator and highlights the bias inherent in the usual Heckman (1979) estimator when there is heteroskedasticity.