Two-step estimation of heteroskedastic sample selection models

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.