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Detecting turning points in global economic activity

Author

Listed:
  • Baumann, Ursel
  • Gómez-Salvador, Ramón
  • Seitz, Franz

Abstract

We present non-linear models to capture the turning points in global economic activity as well as in advanced and emerging economies from 1980 to 2017. We first estimate Markov Switching models within a univariate framework. These models support the relevance of three business cycle regimes (recessions, low growth and high growth) for economic activity at the global level and in advanced and emerging economies. In a second part, we find that the regimes of the Markov Switching models can be well explained with activity, survey and commodity price variables within a discrete choice framework, specifically multinomial logit models, therefore reinforcing the economic interpretation of the regimes. JEL Classification: C34, C35, E32

Suggested Citation

  • Baumann, Ursel & Gómez-Salvador, Ramón & Seitz, Franz, 2019. "Detecting turning points in global economic activity," Working Paper Series 2310, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20192310
    Note: 345263
    as

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    References listed on IDEAS

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    1. Fornari, Fabio & Lemke, Wolfgang, 2010. "Predicting recession probabilities with financial variables over multiple horizons," Working Paper Series 1255, European Central Bank.
    2. Bräuning, Falk & Ivashina, Victoria, 2020. "U.S. monetary policy and emerging market credit cycles," Journal of Monetary Economics, Elsevier, vol. 112(C), pages 57-76.
    3. Klaus Abberger & Wolfgang Nierhaus, 2010. "Markov-Switching and the Ifo Business Climate: the Ifo Business Cycle Traffic Lights," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-13.
    4. Christiansen, Charlotte & Eriksen, Jonas Nygaard & Møller, Stig Vinther, 2014. "Forecasting US recessions: The role of sentiment," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 459-468.
    5. Golinelli, Roberto & Parigi, Giuseppe, 2014. "Tracking world trade and GDP in real time," International Journal of Forecasting, Elsevier, vol. 30(4), pages 847-862.
    6. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    7. Laurent Ferrara & Clément Marsilli, 2019. "Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach," The World Economy, Wiley Blackwell, vol. 42(3), pages 846-875, March.
    8. Sebastian Fossati, 2015. "Forecasting US recessions with macro factors," Applied Economics, Taylor & Francis Journals, vol. 47(53), pages 5726-5738, November.
    9. Camacho, Maximo & Martinez-Martin, Jaime, 2015. "Monitoring the world business cycle," Economic Modelling, Elsevier, vol. 51(C), pages 617-625.
    10. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    11. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2012. "Combination schemes for turning point predictions," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 402-412.
    12. Anaya, Pablo & Hachula, Michael & Offermanns, Christian J., 2017. "Spillovers of U.S. unconventional monetary policy to emerging markets: The role of capital flows," Journal of International Money and Finance, Elsevier, vol. 73(PB), pages 275-295.
    13. Ivo Krznar, 2011. "Identifying Recession and Expansion Periods in Croatia," Working Papers 29, The Croatian National Bank, Croatia.
    14. Harding, Don, 2008. "Detecting and forecasting business cycle turning points," MPRA Paper 33583, University Library of Munich, Germany.
    15. Jens Boysen-Hogrefe, 2012. "A note on predicting recessions in the euro area using real M1," Economics Bulletin, AccessEcon, vol. 32(2), pages 1291-1301.
    16. Gerhard Bry & Charlotte Boschan, 1971. "Programmed Selection of Cyclical Turning Points," NBER Chapters, in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages 7-63, National Bureau of Economic Research, Inc.
    17. Chauvet, Marcelle & Potter, Simon, 2010. "Business cycle monitoring with structural changes," International Journal of Forecasting, Elsevier, vol. 26(4), pages 777-793, October.
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    More about this item

    Keywords

    global GDP; Markov switching; multinomial logit; turning points;
    All these keywords.

    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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