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Nonparametric Significance Testing

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Author Info
Lavergne, Pascal
Vuong, Quang

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Abstract

A procedure for testing the significance of a subset of explanatory variables in a nonparametric regression is proposed. Our test statistic uses the kernel method. Under the null hypothesis of no effect of the variables under test, we show that our test statistic has an nhp2/2 standard normal limiting distribution, where p2 is the dimension of the complete set of regressors. Our test is one-sided, consistent against all alternatives and detects local alternatives approaching the null at rate slower than n 1/2h p2/4. Our Monte-Carlo experiments indicate that it outperforms the test proposed by Fan and Li (1996, Econometrica 64, 865 890).

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File URL: http://journals.cambridge.org/abstract_S0266466600164059
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Publisher Info
Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 16 (2000)
Issue (Month): 04 (August)
Pages: 576-601
Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Handle: RePEc:cup:etheor:v:16:y:2000:i:04:p:576-601

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  1. Oliver Linton & Pedro Gozalo, 1995. "Testing Additivity in Generalized Nonparametric Regression Models," Cowles Foundation Discussion Papers 1106, Cowles Foundation, Yale University. [Downloadable!]
  2. Euvals, R. & Melenberg, B. & Soest, A. van, 1997. "Testing the predictive value of subjective labour supply data," Discussion Paper 25, Tilburg University, Center for Economic Research. [Downloadable!]
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This page was last updated on 2008-8-5.


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