Nonparametric significance testing
Pascal Lavergne and
No 1998,75, SFB 373 Discussion Papers from Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
A procedure for testing the signicance 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 a 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 detect local alternatives approaching the null at rate slower than n-1/2 h-p2/4. Our Monte-Carlo experiments indicate that it outperforms the test proposed by Fan and Li (1996).
Keywords: Hypothesis testing; Kernel estimation; Nested models (search for similar items in EconPapers)
JEL-codes: C52 C14 (search for similar items in EconPapers)
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Journal Article: NONPARAMETRIC SIGNIFICANCE TESTING (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb373:199875
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