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Lapenta, Elia
and Lavergne, Pascal
(2022)
Encompassing Tests for Nonparametric Regressions.
TSE Working Paper, n. 22-1332
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Abstract
We set up a formal framework to characterize encompassing of nonparametric mod-els through the L2 distance. We contrast it to previous literature on the comparison of nonparametric regression models. We then develop testing procedures for the encom-passing hypothesis that are fully nonparametric. Our test statistics depend on kernel regression, raising the issue of bandwidth’s choice. We investigate two alternative ap-proaches to obtain a “small bias property” for our test statistics. We show the validity of a wild bootstrap method, and we illustrate the attractive features of our tests for small and moderate samples.
| Item Type: | Monograph (Working Paper) |
|---|---|
| Language: | English |
| Date: | 31 January 2022 |
| Uncontrolled Keywords: | Encompassing, Nonparametric Regression, Bootstrap, Bias Correction, Locally Robust Statistic. |
| JEL Classification: | C0 - General C12 - Hypothesis Testing C14 - Semiparametric and Nonparametric Methods |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Site: | UT1 |
| Date Deposited: | 05 May 2022 11:13 |
| Last Modified: | 05 May 2022 11:13 |
| OAI Identifier: | oai:tse-fr.eu:126888 |
| URI: | https://publications.ut-capitole.fr/id/eprint/45336 |
