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Learning, forecasting and structural breaks

  • John M. Maheu

    (Department of Economics, University, of Toronto, Canada)

  • Stephen Gordon

    (Département d'économique and CIRPÉE, Université Laval, Quebec City, Canada)

We provide a general methodology for forecasting in the presence of structural breaks induced by unpredictable changes to model parameters. Bayesian methods of learning and model comparison are used to derive a predictive density that takes into account the possibility that a break will occur before the next observation. Estimates for the posterior distribution of the most recent break are generated as a by-product of our procedure. We discuss the importance of using priors that accurately reflect the econometrician's opinions as to what constitutes a plausible forecast. Several applications to macroeconomic time-series data demonstrate the usefulness of our procedure. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://qed.econ.queensu.ca:80/jae/2008-v23.5/
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 23 (2008)
Issue (Month): 5 ()
Pages: 553-583

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Handle: RePEc:jae:japmet:v:23:y:2008:i:5:p:553-583
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