A Significance Test for Covariates in Nonparametric Regression

Pascal Lavergne, Samuel Maistre and Valentin Patilea

No 14-502, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: We consider testing the significance of a subset of covariates in a nonparamet- ric regression. These covariates can be continuous and/or discrete. We propose a new kernel-based test that smoothes only over the covariates appearing under the null hypothesis, so that the curse of dimensionality is mitigated. The test statistic is asymptotically pivotal and the rate of which the test detects local alternatives depends only on the dimension of the covariates under the null hy- pothesis. We show the validity of wild bootstrap for the test. In small samples, our test is competitive compared to existing procedures.

Keywords: Testing; Bootstrap; Kernel Smoothing; U−statistic (search for similar items in EconPapers)
JEL-codes: C14 C52 (search for similar items in EconPapers)
Date: 2014-03
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:28290

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