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Using the Dynamic Bi-Factor Model with Markov Switching to Predict the Cyclical Turns in the Large European Economies

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Author Info
Konstantin A. Kholodilin (DIW Berlin)

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Abstract

The appropriately selected leading indicators can substantially improve the forecasting of the peaks and troughs of the business cycle. Using the novel methodology of the dynamic bi-factor model with Markov switching and the data for three largest European economies (France, Germany, and UK) we construct composite leading indicator (CLI) and composite coincident indicator (CCI) as well as corresponding recession probabilities. We estimate also a rival model of the Markov-switching VAR in order to see, which of the two models brings better outcomes. The recession dates derived from these models are compared to three reference chronologies: those of OECD and ECRI (growth cycles) and those obtained with quarterly Bry-Boschan procedure (classical cycles). Dynamic bi-factor model and MSVAR appear to predict the cyclical turning points equally well without systematic superiority of one model over another

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Paper provided by Money Macro and Finance Research Group in its series Money Macro and Finance (MMF) Research Group Conference 2006 with number 13.

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Date of creation: 02 Feb 2007
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Handle: RePEc:mmf:mmfc06:13

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Related research
Keywords: Forecasting turning points composite

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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. 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)
  2. 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:
  3. 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:
  4. 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)
  5. 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.
  6. 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!]
  7. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22. [Downloadable!] (restricted)
  8. 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.
  9. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March. [Downloadable!] (restricted)
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  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. Konstantin Kholodilin, 2001. "Latent Leading and Coincident Factors Model with Markov-Switching Dynamics," Economics Bulletin, Economics Bulletin, vol. 3, pages 1-13. [Downloadable!]
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