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A comparison of Forecasting Procedures for Macroeconomic Series: The Contribution of Structural Break Models

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Author Info

  • Luc Bauwens

    ()
    (Université catholique de Louvain)

  • Gary Koop

    ()
    (Department of Economics, University of Strathclyde)

  • Dimitris Korobilis

    ()
    (Universite Catholique de Louvain)

  • Jeroen Rombouts

    ()
    (HEC Montréal (École des Hautes Études Commerciales) (Business School) and Center for Operations Research and Econometrics (CORE) ECORE)

Abstract

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

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File URL: http://www.strath.ac.uk/media/departments/economics/researchdiscussionpapers/2011/11-13_Final.pdf
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Bibliographic Info

Paper provided by University of Strathclyde Business School, Department of Economics in its series Working Papers with number 1113.

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Length: 47 pages
Date of creation: Apr 2011
Date of revision:
Handle: RePEc:str:wpaper:1113

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Keywords: Forecasting; change-points; Markov switching; Bayesian inference.;

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References

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  1. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2009. "Macroeconomic Forecasting and Structural Change," Working Papers ECARES 2009_020, ULB -- Universite Libre de Bruxelles.
  2. Belleflamme,Paul & Peitz,Martin, 2010. "Industrial Organization," Cambridge Books, Cambridge University Press, number 9780521681599, April.
  3. Winfried Pohlmeier & Luc Bauwens & David Veredas, 2007. "High frequency financial econometrics. Recent developments," ULB Institutional Repository 2013/136223, ULB -- Universite Libre de Bruxelles.
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Cited by:
  1. KOROBILIS, Dimitris, 2011. "VAR forecasting using Bayesian variable selection," CORE Discussion Papers 2011022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Koop, Gary & Korobilis, Dimitris, 2009. "UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?," SIRE Discussion Papers 2009-40, Scottish Institute for Research in Economics (SIRE).
  3. Kirsten Thompson & Renee van Eyden & Rangan Gupta, 2013. "Testing the Out-of-Sample Forecasting Ability of a Financial Conditions Index for South Africa," Working Papers 201383, University of Pretoria, Department of Economics.
  4. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
  5. Todd E.Clark & Taeyoung Doh, 2011. "A Bayesian evaluation of alternative models of trend inflation," Research Working Paper RWP 11-16, Federal Reserve Bank of Kansas City.
  6. Markku Lanne & Jani Luoto, 2014. "Noncausal Bayesian Vector Autoregression," CREATES Research Papers 2014-07, School of Economics and Management, University of Aarhus.

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