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Forecasting Time Series Subject to Multiple Structural Breaks

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  • Pesaran, M Hashem
  • Pettenuzzo, Davide
  • Timmermann, Allan G

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 Info

Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 4636.

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Date of creation: Sep 2004
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Handle: RePEc:cpr:ceprdp:4636

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Keywords: Bayesian model averaging; forecasting; hierarchical hidden Markov Chain Model; structural breaks;

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  1. James H. Stock & Mark W. Watson, 1994. "Evidence on Structural Instability in Macroeconomic Time Series Relations," NBER Technical Working Papers 0164, National Bureau of Economic Research, Inc.
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  13. BAI, Jushan & PERRON, Pierre, 1998. "Computation and Analysis of Multiple Structural-Change Models," Cahiers de recherche, Universite de Montreal, Departement de sciences economiques 9807, Universite de Montreal, Departement de sciences economiques.
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  22. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, Elsevier, vol. 135(1-2), pages 31-53.
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