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dc.contributor.authorAastveit, Knut Are
dc.contributor.authorMcAlinn, Kenichiro
dc.contributor.authorNakajima, Jouchi
dc.contributor.authorWest, Mike
dc.date.accessioned2019-12-19T13:19:44Z
dc.date.available2019-12-19T13:19:44Z
dc.date.created2019-12-17T09:34:40Z
dc.date.issued2019
dc.identifier.isbn978-82-8379-068-9
dc.identifier.issn1502-819-0
dc.identifier.urihttp://hdl.handle.net/11250/2634167
dc.descriptionAccepted and published in Journal of the American Statistical Associationnb_NO
dc.description.abstractWe present new methodology and a case study in use of a class of Bayesian predictive synthesis (BPS) models for multivariate time series forecasting. This extends the foundational BPS framework to the multivariate setting, with detailed application in the topical and challenging context of multi-step macroeconomic forecasting in a monetary policy setting. BPS evaluates–sequentially and adaptively over time– varying forecast biases and facets of miscalibration of individual forecast densities for multiple time series, and– critically– their time-varying interdependencies. We define BPS methodology for a new class of dynamic multivariate latent factor models implied by BPS theory. Structured dynamic latent factor BPS is here motivated by the application context– sequential forecasting of multiple US macroeconomic time series with forecasts generated from several traditional econometric time series models. The case study highlights the potential of BPS to improve of forecasts of multiple series at multiple forecast horizons, and its use in learning dynamic relationships among forecasting models or agents.nb_NO
dc.language.isoengnb_NO
dc.publisherNorges Banknb_NO
dc.titleMultivariate Bayesian Predictive Synthesis in Macroeconomic Forecastingnb_NO
dc.typeWorking papernb_NO
dc.description.versionsubmittedVersionnb_NO
dc.identifier.cristin1761624
cristin.unitcode158,3,0,0
cristin.unitnameInstitutt for samfunnsøkonomi
cristin.ispublishedfalse
cristin.fulltextpreprint
cristin.qualitycode2


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