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Maistre, Samuel
, Lavergne, Pascal and Patilea, Valentin
(2017)
Powerful nonparametric checks for quantile regression.
Journal of Statistical Planning and Inference, 180.
pp. 13-29.
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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.
| Item Type: | Article |
|---|---|
| Language: | English |
| Date: | January 2017 |
| Refereed: | Yes |
| Uncontrolled Keywords: | Goodness-of-fit test, U-statistics, Smoothing |
| JEL Classification: | C14 - Semiparametric and Nonparametric Methods C52 - Model Evaluation and Selection |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Site: | UT1 |
| Date Deposited: | 03 Nov 2016 10:58 |
| Last Modified: | 02 Apr 2021 15:54 |
| OAI Identifier: | oai:tse-fr.eu:31106 |
| URI: | https://publications.ut-capitole.fr/id/eprint/22444 |
Available Versions of this Item
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Powerful nonparametric checks for quantile regression. (deposited 09 Jul 2014 17:45)
- Powerful nonparametric checks for quantile regression. (deposited 03 Nov 2016 10:58) [Currently Displayed]
