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dc.contributor.authorBessec, Marie*
dc.contributor.authorBouabdallah, Othman*
dc.date.accessioned2016-02-18T09:12:07Z
dc.date.available2016-02-18T09:12:07Z
dc.date.issued2015
dc.identifier.issn0305-9049
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/15384
dc.language.isoenen
dc.subjectMarkov-Switching
dc.subjectfactor models, mixed frequency data
dc.subjectGDP forecasting
dc.subject.ddc330.1en
dc.subject.classificationjelC.C1.C15en
dc.titleForecasting GDP over the business cycle in a multi-frequency and data-rich environment
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherBanque Centrale Européenne
dc.description.abstractenThis paper merges two specifications recently developed in the forecasting literature: the MS-MIDAS model (Guérin and Marcellino, 2013) and the factor-MIDAS model (Marcellino and Schumacher, 2010). The MS-factor MIDAS model that we introduce incorporates the information provided by a large data set consisting of mixed frequency variables and captures regime-switching behaviours. Monte Carlo simulations show that this specification tracks the dynamics of the process and predicts the regime switches successfully, both in-sample and out-of-sample. We apply this model to US data from 1959 to 2010 and properly detect recessions by exploiting the link between GDP growth and higher frequency financial variables.
dc.relation.isversionofjnlnameOxford Bulletin of Economics and Statistics
dc.relation.isversionofjnlvol77
dc.relation.isversionofjnlissue3
dc.relation.isversionofjnldate2015
dc.relation.isversionofjnlpages360-384
dc.relation.isversionofdoi10.1111/obes.12069
dc.relation.isversionofjnlpublisherBasil Blackwell
dc.subject.ddclabelThéorie économiqueen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2016-09-09T15:58:21Z
hal.person.labIds255365*
hal.person.labIds*
hal.identifierhal-01275760*


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