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dc.contributor.authorBardet, Jean-Marc*
dc.contributor.authorKengne, William Chakry*
dc.contributor.authorWintenberger, Olivier*
dc.date.accessioned2010-09-06T14:57:49Z
dc.date.available2010-09-06T14:57:49Z
dc.date.issued2012
dc.identifier.issn1935-7524
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/4699
dc.language.isoenen
dc.subjectChange detection
dc.subjectCausal processes
dc.subjectQuasi-maximum likelihood estimator
dc.subjectModel selection by penalized likelihood
dc.subjectARCH(∞) processes
dc.subjectAR(∞) processes
dc.subject.ddc519en
dc.titleMultiple breaks detection in general causal time series using penalized quasi-likelihood
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherUniversité Panthéon-Sorbonne - Paris I;France
dc.description.abstractenThis paper is devoted to the off-line multiple breaks detection for a general class of models. The observations are supposed to fit a parametric causal process (such as classical models AR(∞), ARCH(∞) or TARCH(∞)) with distinct parameters on multiple periods. The number and dates of breaks, and the different parameters on each period are estimated using a quasi-likelihood contrast penalized by the number of distinct periods. For a convenient choice of the regularization parameter in the penalty term, the consistency of the estimator is proved when the moment order r of the process satisfies r≥2. If r≥4, the length of each approximative segment tends to infinity at the same rate as the length of the true segment and the parameters estimators on each segment are asymptotically normal. Compared to the existing literature, we added the fact that a dependence is possible over distinct periods. To be robust to this dependence, the chosen regularization parameter in the penalty term is larger than the ones from BIC approach. We detail our results which notably improve the existing ones for the AR(∞), ARCH(∞) and TARCH(∞) models. For the practical applications (when n is not too large) we use a data-driven procedure based on the slope estimation to choose the penalty term. The procedure is implemented using the dynamic programming algorithm. It is an O(n2) complexity algorithm that we apply on AR(1), AR(2), GARCH(1,1) and TARCH(1) processes and on the FTSE index data.
dc.relation.isversionofjnlnameElectronic Journal of Statistics
dc.relation.isversionofjnlvol6
dc.relation.isversionofjnldate2012
dc.relation.isversionofjnlpages435-477
dc.relation.isversionofdoihttp://dx.doi.org/10.1214/12-EJS680
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherInstitute of Mathematical Statistics
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2018-01-19T15:44:28Z
hal.person.labIds92163*
hal.person.labIds92163*
hal.person.labIds60*


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