Forecasting Time Series Subject to Multiple Structural Breaks

Mohammad Pesaran, Davide Pettenuzzo () and Allan Timmermann

No 1196, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: hierarchical hidden Markov chain model; forecasting; structural breaks; Bayesian model averaging (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2004-06
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Published - published in Review of Economic Studies, 2006, 73 (4), 1057-1084

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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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