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Learning, forecasting and structural breaks

✍ Scribed by John M. Maheu; Stephen Gordon

Publisher: John Wiley and Sons
Year: 2008
Tongue: English
Weight: 762 KB
Volume: 23
Category: Article
ISSN: 0883-7252
DOI: 10.1002/jae.1018

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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