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dc.rights.licenseopenen_US
dc.contributor.authorCUCINA, Domenico
dc.contributor.authorRIZZO, Manuel
hal.structure.identifierGroupe de Recherche en Economie Théorique et Appliquée [GREThA]
dc.contributor.authorURSU, Eugen
IDREF: 228232201
dc.date.accessioned2020-07-03T09:41:11Z
dc.date.available2020-07-03T09:41:11Z
dc.date.conference2018
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/8545
dc.description.abstractEn.This paper develops a procedure for identifying multiregimePeriodic AutoRegressive (PAR) models. In each regime a possibly dif-ferent PAR model is built, for which changes can be due to the seasonalmeans, the autocorrelation structure or the variances. Number and lo-cations of changepoints which subdivide the time span are detected bymeans of Genetic Algorithms (GAs), that optimize an identification cri-terion. The method is evaluated by means of simulation studies, and isthen employed to analyze shrimp fishery data.
dc.language.isoENen_US
dc.subject.enSeasonality
dc.subject.enStructural changes
dc.subject.enGenetic algorithm
dc.title.enIdentification of multiregime periodic autotregressive models by genetic algorithms
dc.typeCommunication dans un congrès avec actesen_US
dc.subject.halÉconomie et finance quantitative [q-fin]en_US
bordeaux.hal.laboratoriesGroupe de Recherche en Economie Théorique et Appliquée (GREThA) - UMR 5113en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.countryesen_US
bordeaux.title.proceedingInternational Conference of Time Series and Forecasting
bordeaux.conference.cityGrenadeen_US
bordeaux.peerReviewedouien_US
hal.identifierhal-03187870
hal.version1
hal.date.transferred2021-04-01T12:27:48Z
hal.exporttrue
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=CUCINA,%20Domenico&RIZZO,%20Manuel&URSU,%20Eugen&rft.genre=proceeding


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