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A comparison of forecasting procedures for macroeconomic series: the contribution of structural break models

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Author Info

  • BAUWENS, Luc

    ()
    (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)

  • KOOP, Gary

    (University of Strathclyde, U.K)

  • KOROBILIS, Dimitris

    (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)

  • ROMBOUTS, Jeroen V. K.

    (Institute of Applied Economics at HEC Montréal, CIRANO, CIRPEE, Canada; Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium.)

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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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2011003.

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Date of creation: 01 Jan 2011
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Handle: RePEc:cor:louvco:2011003

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Keywords: forecasting; change-points; Markov switching; Bayesian inference;

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  1. Winfried Pohlmeier & Luc Bauwens & David Veredas, 2007. "High frequency financial econometrics. Recent developments," ULB Institutional Repository 2013/136223, ULB -- Universite Libre de Bruxelles.
  2. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, 01.
  3. Belleflamme,Paul & Peitz,Martin, 2010. "Industrial Organization," Cambridge Books, Cambridge University Press, number 9780521681599, October.
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Cited by:
  1. Gary Koop & Dimitris Korompilis, 2009. "UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?," Working Papers 0917, University of Strathclyde Business School, Department of Economics.
  2. Korobilis, Dimitris, 2009. "VAR forecasting using Bayesian variable selection," MPRA Paper 21124, University Library of Munich, Germany.
  3. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
  4. 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.
  5. Markku Lanne & Jani Luoto, 2014. "Noncausal Bayesian Vector Autoregression," CREATES Research Papers 2014-07, School of Economics and Management, University of Aarhus.
  6. 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.

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