Detecting long-range dependence in non-stationary time series

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dc.contributor.authorPreuß, Philip
dc.contributor.authorSen, Kemal
dc.contributor.authorDette, Holger
dc.date.accessioned2013-12-18T16:13:39Z
dc.date.available2013-12-18T16:13:39Z
dc.date.issued2013-12-18
dc.description.abstractAn important problem in time series analysis is the discrimination between non-stationarity and longrange dependence. Most of the literature considers the problem of testing specificc parametric hypotheses of non-stationarity (such as a change in the mean) against long-range dependent stationary alternatives. In this paper we suggest a simple nonparametric approach, which can be used to test the null-hypothesis of a general non-stationary short-memory against the alternative of a non-stationary long-memory process. This test is working in the spectral domain and uses a sieve of approximating tvFARIMA models to estimate the time varying long-range dependence parameter nonparametrically. We prove uniform consistency of this estimate and asymptotic normality of an averaged version. These results yield a simple test (based on the quantiles of the standard normal distribution), and it is demonstrated in a simulation study that - despite of its nonparametric nature - the new test outperforms the currently available methods, which are constructed to discriminate between speci fic parametric hypotheses of non-stationarity short- and stationarity long-range dependence.en
dc.identifier.urihttp://hdl.handle.net/2003/31550
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-13182
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;50/2013
dc.subjectspectral densityen
dc.subjectsieve methoden
dc.subjectlocally stationary processen
dc.subjectintegrated periodogramen
dc.subjectempirical spectral measureen
dc.subjectgoodness-of- fit testsen
dc.subjectnon-stationary processesen
dc.subjectlong-memoryen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleDetecting long-range dependence in non-stationary time seriesen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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Sonderforschungsbereich (SFB) 823