Change point detection in autoregressive models with no moment assumptions

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dc.contributor.authorAkashi, Fumiya
dc.contributor.authorDette, Holger
dc.contributor.authorLiu, Yan
dc.date.accessioned2016-11-23T12:47:34Z
dc.date.available2016-11-23T12:47:34Z
dc.date.issued2016
dc.description.abstractIn this paper we consider the problem of detecting a change in the parameters of an autoregressive process, where the moments of the innovation process do not necessarily exist. An empirical likelihood ratio test for the existence of a change point is proposed and its asymptotic properties are studied. In contrast to other work on change point tests using empirical likelihood, we do not assume knowledge of the location of the change point. In particular, we prove that the maximizer of the empirical likelihood is a consistent estimator for the parameters of the autoregressive model in the case of no change point and derive the limiting distribution of the corresponding test statistic under the null hypothesis. We also establish consistency of the new test. A nice feature of the method consists in the fact that the resulting test is asymptotically distribution free and does not require an estimate of the long run variance. The asymptotic properties of the test are investigated by means of a small simulation study, which demonstrates good finite sample properties of the proposed method.en
dc.identifier.urihttp://hdl.handle.net/2003/35389
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17430
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;73, 2016en
dc.subjectempirical likelihooden
dc.subjectautoregressive processesen
dc.subjectinfinite varianceen
dc.subjectchange point analysisen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleChange point detection in autoregressive models with no moment assumptionsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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