Testing nonparametric hypotheses for stationary processes by estimating minimal distances

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dc.contributor.authorDette, Holger
dc.contributor.authorKinsvater, Tatjana
dc.contributor.authorVetter, Mathias
dc.date.accessioned2010-05-07T10:22:02Z
dc.date.available2010-05-07T10:22:02Z
dc.date.issued2010-05-07T10:22:02Z
dc.description.abstractIn this paper new tests for nonparametric hypotheses in stationary processes are proposed. Our approach is based on an estimate of the L^2-distance between the spectral density matrix and its best approximation under the null hypothesis. We explain the main idea in the problem of testing for a constant spectral density matrix and in the problem of comparing the spectral densities of several correlated stationary time series. The method is based on direct estimation of integrals of the spectral density matrix and does not require the specification of smoothing parameters. We show that the limit distribution of the proposed test statistic is normal and investigate the finite sample properties of the resulting tests by means of a small simulation study.en
dc.identifier.urihttp://hdl.handle.net/2003/27161
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-8551
dc.language.isoen
dc.relation.ispartofseriesDiscussion Paper / SFB 823 ; 16/2010en
dc.subjectgoodness-of- fit testen
dc.subjectintegrated periodogramen
dc.subjectL^2-distanceen
dc.subjectspectral densityen
dc.subjectstationary processen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleTesting nonparametric hypotheses for stationary processes by estimating minimal distancesen
dc.typeText
dc.type.publicationtypereport
dcterms.accessRightsopen access

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Sonderforschungsbereich (SFB) 823