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Forecasting Time Series Subject to Multiple Structural Breaks

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  • M. Hashem Pesaran
  • Davide Pettenuzzo
  • Allan Timmermann

Abstract

This paper provides a new approach to forecasting time series that are subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks occurring 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 parameters from the meta-distribution that characterizes the stochastic break-point process. In an application to U.S. Treasury bill rates, we find that the method leads to better out-of-sample forecasts than a range of alternative methods. Copyright 2006, Wiley-Blackwell.

Suggested Citation

  • M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 1057-1084.
  • Handle: RePEc:oup:restud:v:73:y:2006:i:4:p:1057-1084
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    File URL: http://hdl.handle.net/10.1111/j.1467-937X.2006.00408.x
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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