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dc.contributor.authorAastveit, Knut Are
dc.contributor.authorRavazzolo, Francesco
dc.contributor.authorvan Dijk, Herman K.
dc.date.accessioned2018-04-25T12:51:57Z
dc.date.available2018-04-25T12:51:57Z
dc.date.issued2014
dc.identifier.isbn978-82-7553-840-4
dc.identifier.issn1502-8143
dc.identifier.urihttp://hdl.handle.net/11250/2495964
dc.description.abstractWe introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and other learning mechanisms. The combined density scheme is incorporated in a Bayesian Sequential Monte Carlo method which re-balances the set of nowcasted densities in each period using updated information on the time-varying weights. Experiments with simulated data show that CDN works particularly well in a situation of early data releases with relatively large data uncertainty and model incompleteness. Empirical results, based on US real-time data of 120 leading indicators, indicate that CDN gives more accurate density nowcasts of US GDP growth than a model selection strategy and other combination strategies throughout the quarter with relatively large gains for the two first months of the quarter. CDN also provides informative signals on model incompleteness during recent recessions. Focusing on the tails, CDN delivers probabilities of negative growth, that provide good signals for calling recessions and ending economic slumps in real time.nb_NO
dc.language.isoengnb_NO
dc.publisherNorges Banknb_NO
dc.relation.ispartofseriesWorking Papers;17/2014
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectJEL: C11nb_NO
dc.subjectJEL: C13nb_NO
dc.subjectJEL: C32nb_NO
dc.subjectJEL: C53nb_NO
dc.subjectJEL: E37nb_NO
dc.subjectBayesian filteringnb_NO
dc.subjectSMCnb_NO
dc.subjectsequential Monte Carlo nowcastingnb_NO
dc.subjectdensity forecast combinationnb_NO
dc.subjectsurvey forecastnb_NO
dc.subjectreal-time datanb_NO
dc.titleCombined Density Nowcasting in an Uncertain Economic Environmentnb_NO
dc.typeWorking papernb_NO
dc.description.versionpublishedVersionnb_NO
dc.subject.nsiVDP::Samfunnsvitenskap: 200::Økonomi: 210::Samfunnsøkonomi: 212nb_NO
dc.source.pagenumber36nb_NO


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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