Learning, Forecasting and Structural Breaks

John Maheu and Stephen Gordon

Working Papers from University of Toronto, Department of Economics

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.

Keywords: Bayesian Model Averaging; Markov Chain Monte Carlo; Real GDP Growth; Phillip's Curve (search for similar items in EconPapers)
JEL-codes: C11 C22 C53 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2007-03-30
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://www.economics.utoronto.ca/public/workingPapers/tecipa-284.pdf Main Text (application/pdf)

Related works:
Journal Article: Learning, forecasting and structural breaks (2008)
Working Paper: Learning, Forecasting and Structural Breaks (2004)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tor:tecipa:tecipa-284

Access Statistics for this paper

More papers in Working Papers from University of Toronto, Department of Economics 150 St. George Street, Toronto, Ontario.
Bibliographic data for series maintained by RePEc Maintainer ().