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NONPARAMETRIC SIGNIFICANCE TESTING

Published online by Cambridge University Press:  01 August 2000

Pascal Lavergne  and
Quang Vuong
Pascal Lavergne
Affiliation:
INRA-ESR
Quang Vuong
Affiliation:
University of Southern California and INRA-ESR
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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).

Type
Research Article
Information
Econometric Theory , Volume 16 , Issue 4 , August 2000 , pp. 576 - 601
Copyright
© 2000 Cambridge University Press

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