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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
    DOI: 10.1515/jbnst-2005-0606
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    References listed on IDEAS

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    1. 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.
    2. Konstantin Kholodilin, 2001. "Latent Leading and Coincident Factors Model with Markov-Switching Dynamics," Economics Bulletin, AccessEcon, vol. 3(7), pages 1-13.
    3. Konstantin A. Kholodilin, 2002. "Two Alternative Approaches to Modelling the Nonlinear Dynamics of the Composite Economic Indicator," Economics Bulletin, AccessEcon, vol. 3(26), pages 1-18.
    4. Chauvet, Marcelle, 2002. "The Brazilian Business and Growth Cycles," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 56(1), January.
    5. Ulrich Fritsche & Sabine Stephan, 2000. "Leading Indicators of German Business Cycles: An Assessment of Properties," Macroeconomics 0004005, University Library of Munich, Germany.
    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. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Fritsche Ulrich & Kuzin Vladimir, 2005. "Prediction of Business Cycle Turning Points in Germany / Prognose konjunktureller Wendepunkte in Deutschland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(1), pages 22-43, February.
    9. 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.
    10. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, July.
    11. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," The Journal of Business, University of Chicago Press, vol. 62(3), pages 369-391, July.
    12. 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 357-384, March.
    13. 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 969-996, November.
    14. repec:ebl:ecbull:v:3:y:2002:i:26:p:1-18 is not listed on IDEAS
    15. 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.
    16. 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.
    17. Marcelle Chauvet & Jeremy M. Piger, 2003. "Identifying business cycle turning points in real time," Review, Federal Reserve Bank of St. Louis, vol. 85(Mar), pages 47-61.
    18. Harm Bandholz, 2005. "New Composite Leading Indicators for Hungary and Poland," ifo Working Paper Series 3, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    19. repec:ebl:ecbull:v:3:y:2002:i:25:p:1-17 is not listed on IDEAS
    20. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
    21. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    22. 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.
    23. 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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    1. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    2. Shirly Siew-Ling Wong & Toh-Hao Tan & Shazali Abu Mansor & Venus Khim-Sen Liew, 2018. "Rethinking and Moving Beyond GDP: A New Measure of Sarawak Economy Panorama," International Business Research, Canadian Center of Science and Education, vol. 11(12), pages 127-133, December.
    3. 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.
    4. Heinrich, Markus & Carstensen, Kai & Reif, Magnus & Wolters, Maik, 2017. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168206, Verein für Socialpolitik / German Economic Association.
    5. Foltas, Alexander, 2023. "Quantifying priorities in business cycle reports: Analysis of recurring textual patterns around peaks and troughs," Working Papers 44, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    6. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    7. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions in Germany With Boosted Regression Trees," Macroeconomics and Finance Series 201505, University of Hamburg, Department of Socioeconomics.

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