Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10419/20442
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dc.creatorTimmermann, Allan-
dc.creatorPettenuzzo, Davide-
dc.creatorPesaran, Mohammad Hashem-
dc.date2004-
dc.date.accessioned2013-10-16T07:10:31Z-
dc.date.available2013-10-16T07:10:31Z-
dc.date.issued2013-10-16-
dc.identifierhttp://hdl.handle.net/10419/20442-
dc.identifierppn:390563048-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/10419/20442-
dc.descriptionThis 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.-
dc.languageeng-
dc.relationIZA Discussion paper series 1196-
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.subjectC11-
dc.subjectC15-
dc.subjectC53-
dc.subjectddc:330-
dc.subjectstructural breaks-
dc.subjectforecasting-
dc.subjecthierarchical hidden Markov chain model-
dc.subjectBayesian model averaging-
dc.subjectPrognoseverfahren-
dc.subjectZeitreihenanalyse-
dc.subjectStrukturbruch-
dc.subjectTheorie-
dc.titleForecasting Time Series Subject to Multiple Structural Breaks-
dc.typedoc-type:workingPaper-
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