NONPARAMETRIC KERNEL ESTIMATION FOR SEMIPARAMETRIC MODELS

This paper presents a number of consistency results for nonparametric kernel estimators of density and regression functions and their deriva tives. These results are particularly useful in semiparametric estimat ion and testing problems that rely on preliminary nonparametric estima tors, as in Andrews (1994, Econometrica 62, 43-72). The results allow for near-epoch dependent, nonidentically distributed random variables, data-dependent bandwidth sequences, preliminary estimation of paramet ers (e.g., nonparametric regression based on residuals), and nonparame tric regression on index functions.