Learning, forecasting and structural breaks

John Maheu and Stephen Gordon

Journal of Applied Econometrics, 2008, vol. 23, issue 5, 553-583

Abstract: 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.

Date: 2008
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Working Paper: Learning, Forecasting and Structural Breaks (2004)
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DOI: 10.1002/jae.1018

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