Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/18876
Authors: 
Timmermann, Allan
Pettenuzzo, Davide
Pesaran, Mohammad Hashem
Year of Publication: 
2004
Series/Report no.: 
CESifo Working Paper 1237
Abstract: 
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.
Subjects: 
structural breaks
forecasting
hierarchical hidden Markov chain model
Bayesian model averaging
JEL: 
C53
C11
C15
Document Type: 
Working Paper

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