Powerful nonparametric checks for quantile regression

Samuel Maistre, Pascal Lavergne and Valentin Patilea

No 14-501, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: We address the issue of lack-of-fit testing for a parametric quantile regression. We propose a simple test that involves one-dimensional kernel smoothing, so that the rate at which it detects local alternatives is independent of the number of covariates. The test has asymptotically gaussian critical values, and wild bootstrap can be applied to obtain more accurate ones in small samples. Our procedure appears to be competitive with existing ones in simulations. We illustrate the usefulness of our test on birthweight data.

Keywords: Goodness-of-fit test; U-statistics; Smoothing (search for similar items in EconPapers)
JEL-codes: C14 C52 (search for similar items in EconPapers)
Date: 2014-06
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:28289

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