Lapenta, Elia and Lavergne, Pascal (2022) Encompassing Tests for Nonparametric Regressions. TSE Working Paper, n. 22-1332, Toulouse

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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
Place of Publication: Toulouse
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)
Institution: Université Toulouse 1 Capitole
Site: UT1
Date Deposited: 05 May 2022 11:13
Last Modified: 12 Feb 2025 13:37
OAI Identifier: oai:tse-fr.eu:126888
URI: https://publications.ut-capitole.fr/id/eprint/45336

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