Testing semiparametric hypotheses in locally stationary processes

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dc.contributor.authorDette, Holger
dc.contributor.authorPreuß, Philip
dc.contributor.authorVetter, Mathias
dc.date.accessioned2011-03-23T13:49:38Z
dc.date.available2011-03-23T13:49:38Z
dc.date.issued2011-03-23
dc.description.abstractIn this paper we investigate the problem of testing semi parametric hypotheses in locally stationary processes. The proposed method is based on an empirical version of the L2-distance between the true time varying spectral density and its best approximation under the null hypothesis. As this approach only requires estimation of integrals of the time varying spectral density and its square, we do not have to choose a smoothing bandwidth for the local estimation of the spectral density - in contrast to most other procedures discussed in the literature. Asymptotic normality of the test statistic is derived both under the null hypothesis and the alternative. We also propose a bootstrap procedure to obtain critical values in the case of small sample sizes. Additionally, we investigate the finite sample properties of the new method and compare it with the currently available procedures by means of a simulation study. Finally, we illustrate the performance of the new test in a data example investigating log returns of the S&P 500.en
dc.identifier.urihttp://hdl.handle.net/2003/27663
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-13401
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;13/2011en
dc.subjectbootstrapen
dc.subjectgoodness-of- fit testsen
dc.subjectintegrated periodogramen
dc.subjectL2-distanceen
dc.subjectlocally stationary processesen
dc.subjectnon stationary processesen
dc.subjectsemi parametric modelsen
dc.subjectspectral densityen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleTesting semiparametric hypotheses in locally stationary processesen
dc.typeTextde
dc.type.publicationtypeworkingPaperen
dcterms.accessRightsopen access

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