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dc.contributor.authorTimmermann, Allanen_US
dc.contributor.authorPettenuzzo, Davideen_US
dc.contributor.authorPesaran, Mohammad Hashemen_US
dc.date.accessioned2009-01-28T15:53:30Z-
dc.date.available2009-01-28T15:53:30Z-
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/10419/18876-
dc.description.abstractThis paper provides a novel approach to forecasting time series subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks over the forecast horizon, taking account of the size and duration of past breaks (if any) by means of a hierarchical hidden Markov chain model. Predictions are formed by integrating over the hyper parameters from the meta distributions that characterize the stochastic break point process. In an application to US Treasury bill rates, we find that the method leads to better out-of-sample forecasts than alternative methods that ignore breaks, particularly at long horizons.en_US
dc.language.isoengen_US
dc.relation.ispartofseriesCESifo working papers 1237en_US
dc.subject.jelC53en_US
dc.subject.jelC11en_US
dc.subject.jelC15en_US
dc.subject.ddc330en_US
dc.subject.keywordstructural breaksen_US
dc.subject.keywordforecastingen_US
dc.subject.keywordhierarchical hidden Markov chain modelen_US
dc.subject.keywordBayesian model averagingen_US
dc.subject.stwPrognoseverfahrenen_US
dc.subject.stwZeitreihenanalyseen_US
dc.subject.stwStrukturbruchen_US
dc.subject.stwTheorieen_US
dc.titleForecasting time series subject to multiple structural breaksen_US
dc.typeWorking Paperen_US
dc.identifier.ppn393391035en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:CESifo Working Papers, CESifo Group Munich

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