Markov-Switching MIDAS Models

dc.contributor.authorGUERIN, Pierre
dc.contributor.authorMARCELLINO, Massimiliano
dc.date.accessioned2011-02-07T13:13:34Z
dc.date.available2011-02-07T13:13:34Z
dc.date.issued2011
dc.descriptionWe would like to thank Karol Ciszek, Matthieu Droumaguet, Laurent Ferrara, Eric Ghysels, Helmut Herwartz, Helmut Luetkepohl, Michael McCracken, Rossen Valkanov and seminar participants at the EUI, the MIDAS workshop at Goethe University Frankfurt and the 6th Eurostat Colloquium on Modern Tools for Business Cycle Analysis for useful comments on a previous draft.en
dc.description.abstractThis paper introduces a new regression model - Markov-switching mixed data sampling (MS-MIDAS) - that incorporates regime changes in the parameters of the mixed data sampling (MIDAS) models and allows for the use of mixed-frequency data in Markov-switching models. After a discussion of estimation and inference for MS-MIDAS, and a small sample simulation based evaluation, the MS-MIDAS model is applied to the prediction of the US and UK economic activity, in terms both of quantitative forecasts of the aggregate economic activity and of the prediction of the business cycle regimes. Both simulation and empirical results indicate that MSMIDAS is a very useful specification.en
dc.format.mimetypeapplication/pdf
dc.identifier.issn1725-6704
dc.identifier.urihttps://hdl.handle.net/1814/15644
dc.language.isoenen
dc.neeo.contributorGUÉRIN|Pierre|aut|
dc.neeo.contributorMARCELLINO|Massimiliano|aut|EUI70008
dc.relation.hasversionhttp://hdl.handle.net/1814/29183
dc.relation.ispartofseriesEUI ECOen
dc.relation.ispartofseries2011/03en
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBusiness cyclesen
dc.subjectMixed-frequency dataen
dc.subjectNon-linear modelsen
dc.subjectForecastingen
dc.subjectNowcastingen
dc.subjectC22en
dc.subjectC53en
dc.subjectE37en
dc.titleMarkov-Switching MIDAS Modelsen
dc.typeWorking Paperen
dspace.entity.typePublication
eui.subscribe.skiptrue
person.identifier.other26452
relation.isAuthorOfPublication40c8706f-96c5-4201-aba5-626a9232e5d7
relation.isAuthorOfPublication.latestForDiscovery40c8706f-96c5-4201-aba5-626a9232e5d7
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