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dc.contributor.authorBessec, Marie*
dc.date.accessioned2015-06-23T08:35:38Z
dc.date.available2015-06-23T08:35:38Z
dc.date.issued2015-05
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/15246
dc.language.isoenen
dc.subjectMarkov-Switchingen
dc.subjectmixed frequency dataen
dc.subjectbusiness cyclesen
dc.subject.ddc339en
dc.subject.classificationjelC.C2.C22en
dc.subject.classificationjelE.E3.E32en
dc.subject.classificationjelE.E3.E37en
dc.titleRevisiting the transitional dynamics of business-cycle phases with mixed frequency dataen
dc.typeCommunication / Conférence
dc.description.abstractenThis paper introduces a Markov-Switching model where transition probabilities depend on higher frequency indicators and their lags, through polynomial weighting schemes. The MSV-MIDAS model is estimated via maximum likel ihood methods. The estimation relies on a slightly modified version of Hamilton’s recursive filter. We use Monte Carlo simulations to assess the robustness of the estimation procedure and related test-statistics. The results show that ML provides accurate estimates, but they suggest some caution in the tests on the parameters involved in the transition probabilities. We apply this new model to the detection and forecast of business cycle turning points. We properly detect recessions in United States and United Kingdom by exploiting the link between GDP growth and higher frequency variables from financial and energy markets. Spread term is a particularly useful indicator to predict recessions in the United States, while stock returns have the strongest explanatory power around British turning points.en
dc.identifier.citationpages34en
dc.subject.ddclabelMacroéconomieen
dc.relation.conftitleAFSE 2015 64th Congressen
dc.relation.confdate2015-06
dc.relation.confcityRennesen
dc.relation.confcountryFranceen
dc.relation.forthcomingnonen
dc.description.halcandidateoui
dc.description.audienceInternational
dc.relation.IsversionofjnlpeerreviewedNon
dc.relation.Isversionofjnlpeerreviewednon
dcterms.audienceInternational
hal.person.labIds163511*
hal.identifierhal-01276824*


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