Consistent Significance Testing for Nonparametric Regression
提出一种基于非参数偏导数估计的显著性检验框架，适用于个体和联合检验，对模型设定错误稳健，并通过自助法实现。模拟和汇率应用表明其优于错误设定的参数模型。
This article presents a framework for individual and joint tests of significance employing nonparametric estimation procedures. The proposed test is based on nonparametric estimates of partial derivatives, is robust to functional misspecification for general classes of models, and employs nested pivotal bootstrapping procedures. Two simulations and one application are considered to examine size and power relative to misspecified parametric models, and to test for the linear unpredictability of exchange-rate movements for G7 currencies.