Learning, forecasting and structural breaks
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.Download Info
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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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Related research
Keywords:Other versions of this item:
- John M. Maheu & Stephen Gordon, 2004. "Learning, Forecasting and Structural Breaks," Cahiers de recherche 0422, CIRPEE.
- John M Maheu & Stephen Gordon, 2007. "Learning, Forecasting and Structural Breaks," Working Papers tecipa-284, University of Toronto, Department of Economics.
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
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Citations
Blog mentions
As found by EconAcademics.org, the blog aggregator for Economics research:- Economic growth and convergence
by Stephen Gordon in Worthwhile Canadian Initiative on 2009-12-24 11:00:00 - Economic growth and convergence
by Stephen in Worthwhile Canadian Initiative on 2006-03-26 01:24:17
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