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dc.rights.licenseopenen_US
dc.contributor.authorURSU, Eugen
IDREF: 228232201
hal.structure.identifierGroupe de Recherche en Economie Théorique et Appliquée [GREThA]
dc.contributor.authorPEREAU, Jean-Christophe
IDREF: 086314629
dc.date.accessioned2020-02-19T21:17:33Z
dc.date.available2020-02-19T21:17:33Z
dc.date.issued2014
dc.identifier.issn3783758en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/3590
dc.description.abstractEnThis paper develops a robust estimation and identification method for periodic vector autoregressive models (hereafter PVAR) with linear constraints set on parameters for a given season. Since the least squares estimators are extremely sensitive to additive outliers, this paper suggests a robust estimation based on residual autocovariances (RA) and analyses the asymptotic properties of these RA estimates. To identify the optimal order of the PVAR, this paper also uses a genetic algorithm with Bayes information criterion (BIC). The proposed procedure is applied to a small simulation study for PVAR models in the case of four seasons. Empirical results show that the robust estimators perform better than the least squares estimators when the contamination rate of the additive outliers is at random or at fixed positions.
dc.language.isoENen_US
dc.subject.enGenetic Algorithms
dc.subject.enIdentification
dc.subject.enPeriodic Time Series
dc.subject.enRobust Estimation
dc.subject.enParameter Constraints
dc.title.enRobust modelling of periodic vector autoregressive time series
dc.title.alternativeJ. Stat. Plann. Inferenceen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.jspi.2014.07.005en_US
dc.subject.halÉconomie et finance quantitative [q-fin]en_US
bordeaux.journalJournal of Statistical Planning and Inferenceen_US
bordeaux.page93-106en_US
bordeaux.volume155en_US
bordeaux.hal.laboratoriesGroupe de Recherche en Economie Théorique et Appliquée (GREThA) - UMR 5113en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-02485115
hal.version1
hal.date.transferred2020-02-19T21:17:37Z
hal.exporttrue
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