Forecasting Time Series Subject to Multiple Structural Breaks

Mohammad Pesaran, Davide Pettenuzzo (dpettenu@brandeis.edu) and Allan Timmermann

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

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 characterise 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.

Keywords: structural breaks; forecasting; hierarchical hidden Markov Chain model; Bayesian model averaging (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 (search for similar items in EconPapers)
Pages: 43
Date: 2004-06
New Economics Papers: this item is included in nep-ecm
Note: EM
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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Related works:
Journal Article: Forecasting Time Series Subject to Multiple Structural Breaks (2006)
Working Paper: Forecasting Time Series Subject to Multiple Structural Breaks (2004)
Working Paper: Forecasting Time Series Subject to Multiple Structural Breaks (2004)
Working Paper: Forecasting Time Series Subject to Multiple Structural Breaks (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:0433

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