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1998-06-01Buch DOI: 10.18452/3761
Nonparametric Significance Testing
dc.contributor.authorLavergne, Pascal
dc.contributor.authorVuong, Quang
dc.date.accessioned2017-06-15T22:02:18Z
dc.date.available2017-06-15T22:02:18Z
dc.date.created2006-03-15
dc.date.issued1998-06-01
dc.identifier.issn1436-1086
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4413
dc.description.abstractA 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).eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectHypothesis testingeng
dc.subjectKernel estimationeng
dc.subjectNested modelseng
dc.subject.ddc330 Wirtschaft
dc.titleNonparametric Significance Testing
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10060522
dc.identifier.doihttp://dx.doi.org/10.18452/3761
dc.subject.dnb17 Wirtschaft
local.edoc.container-titleSonderforschungsbereich 373: Quantification and Simulation of Economic Processes
local.edoc.pages30
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume1998
local.edoc.container-issue75
local.edoc.container-year1998
local.edoc.container-erstkatid2135319-0

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