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Discussion Paper Details

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Title: Forecasting Time Series Subject to Multiple Structural Breaks

Author(s): M Hashem Pesaran, Davide Pettenuzzo and Allan Timmermann

Publication Date: September 2004

Keyword(s): Bayesian model averaging, forecasting, hierarchical hidden Markov Chain Model and structural breaks

Programme Area(s): Financial Economics

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.

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Bibliographic Reference

Pesaran, M, Pettenuzzo, D and Timmermann, A. 2004. 'Forecasting Time Series Subject to Multiple Structural Breaks'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=4636