2-STEP ESTIMATION OF HETEROSKEDASTIC SAMPLE SELECTION MODELS

This paper considers two-step estimation of a sample selection model i n which there is heteroskedasticity of unknown form in the latent erro rs. We propose an estimator which uses recent developments in nonparam etric regression estimation involving series approximations. The estim ator is shown to be consistent and asymptotically normally distributed under reasonable conditions. A small Monte Carlo experiment demonstra tes the usefulness of the estimator and highlights the bias inherent i n the usual Heckman (1979) estimator when there is heteroskedasticity.