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Global recessions and booms: What do probit models tell us?

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  • Baumann, Ursel
  • Gómez Salvador, Ramón
  • Seitz, Franz

Abstract

We present non-linear binary Probit models to capture the turning points in global economic activity as well as in advanced and emerging economies from 1980 to 2016. For that purpose, we use four different business cycle dating methods to identify the regimes (upswings, downswings). We find that especially activity-driven variables are important indicators for the turning points. Moreover, we identify similarities and differences between the different regions in this respect.

Suggested Citation

  • Baumann, Ursel & Gómez Salvador, Ramón & Seitz, Franz, 2018. "Global recessions and booms: What do probit models tell us?," Weidener Diskussionspapiere 61, University of Applied Sciences Amberg-Weiden (OTH).
    • Handle: RePEc:zbw:hawdps:61
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    References listed on IDEAS

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    1. Francesco Ravazzolo & Joaquin L. Vespignani, 2015. "A new monthly indicator of global real economic activity," Globalization Institute Working Papers 244, Federal Reserve Bank of Dallas.
    2. Layton, Allan P. & Katsuura, Masaki, 2001. "Comparison of regime switching, probit and logit models in dating and forecasting US business cycles," International Journal of Forecasting, Elsevier, vol. 17(3), pages 403-417.
    3. 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.
    4. Nyberg, Henri, 2014. "A Bivariate Autoregressive Probit Model: Business Cycle Linkages And Transmission Of Recession Probabilities," Macroeconomic Dynamics, Cambridge University Press, vol. 18(4), pages 838-862, June.
    5. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    6. Christian R. Proaño, 2017. "Detecting and Predicting Economic Accelerations, Recessions, and Normal Growth Periods in Real‐Time," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(1), pages 26-42, January.
    7. Harding, Don, 2008. "Detecting and forecasting business cycle turning points," MPRA Paper 33583, University Library of Munich, Germany.
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    Cited by:

    1. Yasmeen, Rizwana & Tao, Rui & Jie, Wanchen & Padda, Ihtsham Ul Haq & Shah, Wasi Ul Hassan, 2022. "The repercussions of business cycles on renewable & non-renewable energy consumption structure: Evidence from OECD countries," Renewable Energy, Elsevier, vol. 190(C), pages 572-583.

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    Keywords

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    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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