Learning, Forecasting and Structural Breaks
John Maheu () and
Working Papers from University of Toronto, Department of Economics
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.
Keywords: Bayesian Model Averaging; Markov Chain Monte Carlo; Real GDP Growth; Phillip's Curve (search for similar items in EconPapers)
JEL-codes: C53 C22 C11 (search for similar items in EconPapers)
Pages: 42 pages
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets and nep-for
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https://www.economics.utoronto.ca/public/workingPapers/tecipa-284.pdf Main Text (application/pdf)
Journal Article: Learning, forecasting and structural breaks (2008)
Working Paper: Learning, Forecasting and Structural Breaks (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:tor:tecipa:tecipa-284
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