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dc.contributor.authorBillio, Monica
dc.contributor.authorCasarin, Roberto
dc.contributor.authorRavazzolo, Francesco
dc.contributor.authorvan Dijk, Herman K.
dc.description.abstractUsing a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying combination strategies are introduced. In particular, a weight dynamics driven by the past performance of the predictive densities is considered and the use of learning mechanisms. The approach is assessed using statistical and utility-based performance measures forevaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.nb_NO
dc.publisherNorges Banknb_NO
dc.relation.ispartofseriesWorking Papers;29/2010
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.subjectJEL: C11nb_NO
dc.subjectJEL: C15nb_NO
dc.subjectJEL: C53nb_NO
dc.subjectJEL: E37nb_NO
dc.subjectBayesian filteringnb_NO
dc.subjectsequential Monte Carlonb_NO
dc.subjectdensity forecast combinationnb_NO
dc.subjectsurvey forecastnb_NO
dc.titleCombining Predictive Densities Using Bayesian Filtering with Applications to Us Economics Datanb_NO
dc.typeWorking papernb_NO
dc.subject.nsiVDP::Samfunnsvitenskap: 200::Økonomi: 210::Samfunnsøkonomi: 212nb_NO

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal