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Forecasting Time Series Subject to Multiple Structural Breaks

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dc.creator Timmermann, Allan
dc.creator Pettenuzzo, Davide
dc.creator Pesaran, Mohammad Hashem
dc.date 2004
dc.date.accessioned 2013-10-16T07:10:31Z
dc.date.available 2013-10-16T07:10:31Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/20442
dc.identifier ppn:390563048
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/20442
dc.description This 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.language eng
dc.relation IZA Discussion paper series 1196
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject C11
dc.subject C15
dc.subject C53
dc.subject ddc:330
dc.subject structural breaks
dc.subject forecasting
dc.subject hierarchical hidden Markov chain model
dc.subject Bayesian model averaging
dc.subject Prognoseverfahren
dc.subject Zeitreihenanalyse
dc.subject Strukturbruch
dc.subject Theorie
dc.title Forecasting Time Series Subject to Multiple Structural Breaks
dc.type doc-type:workingPaper

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