Consistent Significance Testing for Nonparametric Regression.

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.