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Forecasting the Turns of German Business Cycle : Dynamic Bi-factor Model with Markov Switching

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Author Info
Konstantin A. Kholodilin

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Abstract

In this paper a dynamic bi-factor model with Markov switching is proposed to measure and predict 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 being in recession. According to the bi-factor model, on average, CLI leads CCI by 3 months at both peaks and troughs. The model-derived recession probabilities of CCI and those of CLI with a lag of 2-3 months capture the turning points of the ECRI's and OECD's reference cycle much better than the dynamic single-factor model with Markov switching.

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Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 494.

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Length: 28 p.
Date of creation: 2005
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Publication status: Published in: Jahrbücher für Nationalökonomie und Statistik 225 (2005), 6, 653-674
Handle: RePEc:diw:diwwpp:dp494

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Related research
Keywords: Forecasting turning points Composite coincident indicator Composite leading indicator Dynamic bi-factor model Markov-switching

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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: General - - - General

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  1. Ulrich Fritsche & Sabine Stephan, 2000. "Leading Indicators of German Business Cycles: An Assessment of Properties," Macroeconomics 0004005, EconWPA. [Downloadable!]
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  2. 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. [Downloadable!] (restricted)
  3. Konstantin A. Kholodilin, 2002. "Two Alternative Approaches to Modelling the Nonlinear Dynamics of the Composite Economic Indicator," Economics Bulletin, Economics Bulletin, vol. 3(25), pages 1-17. [Downloadable!]
    Other versions:
  4. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February. [Downloadable!] (restricted)
    Other versions:
  5. 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. [Downloadable!] (restricted)
  6. 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.
  7. Marcelle Chauvet, 2002. "The Brazilian Business and Growth Cycles," Revista Brasileira de Economia, Graduate School of Economics, Getulio Vargas Foundation (Brazil), vol. 56(1), April. [Downloadable!]
  8. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22. [Downloadable!] (restricted)
  9. 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.
  10. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," Journal of Business, University of Chicago Press, vol. 62(3), pages 369-91, July. [Downloadable!] (restricted)
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  11. Harm Bandholz, 2005. "New Composite Leading Indicators for Hungary and Poland," Ifo Working Paper Series Ifo Working Paper No. 3, Ifo Institute for Economic Research at the University of Munich. [Downloadable!]
  12. James H. Stock & Mark W. Watson, 1988. "A Probability Model of The Coincident Economic Indicators," NBER Working Papers 2772, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  13. 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. [Downloadable!] (restricted)
  14. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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  15. Konstantin Kholodilin, 2001. "Latent Leading and Coincident Factors Model with Markov-Switching Dynamics," Economics Bulletin, Economics Bulletin, vol. 3, pages 1-13. [Downloadable!]
  16. Marcelle Chauvet & Jeremy M. Piger, 2003. "Identifying business cycle turning points in real time," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 47-61. [Downloadable!]
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