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Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching

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  • Konstantin A. Kholodilin

    ()

    (DIW Berlin)

Abstract

This paper proposes a dynamic bi-factor model with Markov switching which detects and predicts turning points of the German business cycle. It estimates simultaneously the composite leading indicator (CLI) and composite coincident indicator (CCI) together with corresponding probabilities of a recession. According to the bi-factor model, CLI leads CCI by about 3 months at both peaks and troughs. The model-derived recession probabilities of CCI an CLI capture the turning points of the ECRI's and OECD's reference cycles much better than the dynamic single-factor model with Markov switching.

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Article provided by Justus-Liebig University Giessen, Department of Statistics and Economics in its journal Journal of Economics and Statistics.

Volume (Year): 225 (2005)
Issue (Month): 6 (November)
Pages: 653-674

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Handle: RePEc:jns:jbstat:v:225:y:2005:i:6:p:653-674
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Keywords: Forecasting turning points; composite coincident indicator; composite leading indicator; dynamic bifactor model; Markov switching;

Find related papers by JEL classification:

  • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
  • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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  1. Diebold & Rudebusch, . "Measuring Business Cycle: A Modern Perspective," Home Pages _061, University of Pennsylvania.
  2. Ulrich Fritsche & Sabine Stephan, 2002. "Leading Indicators of German Business Cycles - An Assessment of Properties," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 222(3), pages 289-315.
  3. Konstantin Kholodilin, 2001. "Latent Leading and Coincident Factors Model with Markov-Switching Dynamics," Economics Bulletin, AccessEcon, vol. 3(7), pages 1-13.
  4. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
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  6. Watanabe, Toshiaki, 2003. "Measuring Business Cycle Turning Points in Japan with a Dynamic Markov Switching Factor Model," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 21(1), pages 35-68, February.
  7. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-96, November.
  8. Marcelle Chauvet & Jeremy M. Piger, 2002. "Identifying business cycle turning points in real time," FRB Atlanta Working Paper 2002-27, Federal Reserve Bank of Atlanta.
  9. Kholodilin, Konstantin A. & Yao, Vincent W., 2005. "Measuring and predicting turning points using a dynamic bi-factor model," International Journal of Forecasting, Elsevier, vol. 21(3), pages 525-537.
  10. Konstantin, KHOLODILIN, 2002. "Two Alternative Approaches to Modelling the Nonlinear Dynamics of the Composite Economic Indicator," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2002027, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
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  12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
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  14. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
  15. Harm Bandholz, 2005. "New Composite Leading Indicators for Hungary and Poland," Ifo Working Paper Series Ifo Working Paper No. 3, Ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  16. Christian Dreger & Christian Schumacher, 2005. "Out-of-sample Performance of Leading Indicators for the German Business Cycle: Single vs. Combined Forecasts," Journal of Business Cycle Measurement and Analysis, OECD Publishing,Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 71-87.
  17. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  18. Sylvia Kaufmann, 2000. "Measuring business cycles with a dynamic Markov switching factor model: an assessment using Bayesian simulation methods," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 39-65.
  19. Phillips, Kerk L., 1991. "A two-country model of stochastic output with changes in regime," Journal of International Economics, Elsevier, vol. 31(1-2), pages 121-142, August.
  20. Chauvet, Marcelle, 2002. "The Brazilian Business and Growth Cycles," Revista Brasileira de Economia, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil), vol. 56(1), January.
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