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

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  • Kholodilin Konstantin A.

    () (DIW Berlin, Königin-Luise-Str. 5, D-14195 Berlin, Germany)

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 and 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.

Suggested Citation

  • Kholodilin Konstantin A., 2005. "Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(6), pages 653-674, December.
  • Handle: RePEc:jns:jbstat:v:225:y:2005:i:6:p:653-674
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    References listed on IDEAS

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    Cited by:

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    2. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, November.
    3. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.

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