Goodness-of- fit tests for multivariate copula-based time series models

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dc.contributor.authorBerghaus, Betina
dc.contributor.authorBücher, Axel
dc.date.accessioned2014-05-21T15:11:14Z
dc.date.available2014-05-21T15:11:14Z
dc.date.issued2014-05-21
dc.description.abstractIn recent years, stationary time series models based on copula functions became increasingly popular in econometrics to model nonlinear temporal and cross-sectional dependencies. Within these models, we consider the problem of testing the goodness-of-fit of the parametric form of the underlying copula. Our approach is based on a dependent multiplier bootstrap and it can be applied to any stationary, strongly mixing time series. The method extends recent i.i.d. results by Kojadinovic, Yan and Holmes [I. Kojadinovic, Y. Yan and M. Holmes, Fast large sample goodness-of- fit tests for copulas, Statistica Sinica 21 (2011), 841{871] and shares the same computational benefits compared to methods based on a parametric bootstrap. The finite-sample performance of our approach is investigated by Monte Carlo experiments for the case of copula-based Markovian time series models.en
dc.identifier.urihttp://hdl.handle.net/2003/33154
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15495
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;22/2014
dc.subjectempirical processen
dc.subjectsemiparametric copula modelen
dc.subjectranksen
dc.subjectpseudo-observationsen
dc.subjectmultivariate observationsen
dc.subjectMarkov chainen
dc.subjectmultiplier central limit theoremen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleGoodness-of- fit tests for multivariate copula-based time series modelsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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