Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10419/20442
Title: Forecasting Time Series Subject to Multiple Structural Breaks
Keywords: C11
C15
C53
ddc:330
structural breaks
forecasting
hierarchical hidden Markov chain model
Bayesian model averaging
Prognoseverfahren
Zeitreihenanalyse
Strukturbruch
Theorie
Issue Date: 16-Oct-2013
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.
URI: http://koha.mediu.edu.my:8181/xmlui/handle/10419/20442
Other Identifiers: http://hdl.handle.net/10419/20442
ppn:390563048
Appears in Collections:EconStor

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