DATA-DRIVEN RATE-OPTIMAL SPECIFICATION TESTING IN REGRESSION MODELS

Emmanuel Guerre and Pascal Lavergne
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Emmanuel Guerre: LSTA-Université Paris 6

Econometrics from University Library of Munich, Germany

Abstract: We propose new data-driven smooth tests for a parametric regression function. The smoothing parameter is selected through a new criterion that favors a large smoothing parameter under the null hypothesis. The resulting test is adaptive rate-optimal and consistent against Pitman local alternatives approaching the parametric model at a rate arbitrarily close to 1/\sqrt{n}. Asymptotic critical values come from the standard normal distribution and bootstrap can be used in small samples. A general formalization allows to consider a large class of linear smoothing methods, which can be tailored for detection of additive alternatives.

Keywords: Hypothesis testing; nonparametric adaptive tests; selection methods (search for similar items in EconPapers)
JEL-codes: C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2004-11-12
New Economics Papers: this item is included in nep-ecm
Note: Type of Document - pdf; pages: 30. Forthcoming in the Annals of Statistics
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0411008

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