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This paper presents a new perspective on the identification at infinity forthe intercept of the sample selection model as identification at the boundaryvia a transformation of the selection index. This perspective suggestsgeneralizations of estimation at infinity to kernel regression estimation atthe boundary and further to local linear estimation at the boundary. Theproposed kernel-type estimators with an estimated transformation are proven tobe nonparametric-rate consistent and asymptotically normal under mildregularity conditions. A fully data-driven method of selecting the optimalbandwidths for the estimators is developed. The Monte Carlo simulation showsthe desirable finite sample properties of the proposed estimators and bandwidthselection procedures.

On semiparametric estimation of the intercept of the sample selection model: a kernel approach