Nonparametric Hypothesis Testing with Parametric Rates of Convergence
提出用核方法估计回归函数的期望导数，证明估计量渐近正态且n一致，标准误与参数估计相当，并通过实例展示其有效性。
Nonparametric estimators are frequently criticized for their poor performance in small samples. In this paper, the author considers using kernel methods for the estimation of the expected derivatives of a regression function. The proposed estimators are shown to be asymptotically normal and n-consistent. As a consequence, their standard errors are comparable to parametric estimates. An empirical example demonstrates the facility of the approach. Copyright 1991 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.