Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing

Abstract

A new uniform expansion is introduced for sums of weighted kernel-based regression residuals from nonparametric or semiparametric models. This expansion is useful for deriving asymptotic properties of semiparametric estimators and test statistics with data-dependent bandwidths, random trimming, and estimated efficiency weights. Provided examples include a new estimator for a binary choice model with selection and an associated directional test for specification of this model’s average structural function. An appendix contains new results on uniform rates for kernel estimators and primitive sufficient conditions for high level assumptions commonly used in semiparametric estimation.