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Consistent Significance Testing for Nonparametric Regression

Jeff Racine
Journal of Business & Economic Statistics
Vol. 15, No. 3 (Jul., 1997), pp. 369-378
DOI: 10.2307/1392340
Stable URL: http://www.jstor.org/stable/1392340
Page Count: 10
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Consistent Significance Testing for Nonparametric Regression
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Abstract

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.

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