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The Role of Oscillatory Modes in U.S. Business Cycles

Author

Listed:
  • Andreas Groth

    (Geosciences Department, Ecole Normale Supérieure, Paris, France, Environmental Research & Teaching Institute, Ecole Normale Supérieure)

  • Michael Ghil

    (Geosciences Department, Ecole Normale Supérieure, Paris, France, Environmental Research & Teaching Institute, Ecole Normale Supérieure, Paris, France Department of Atmospheric & Oceanic Sciences and Institute of Geophysics & Planetary Physics, University of California)

  • Stéphane Hallegatte

    (Centre International de Recherche sur l'Environnement et le Développement, Nogent-sur-Marne, France, Ecole Nationale de la Météorologie, Météo France)

  • Patrice Dumas

    (Centre International de Recherche sur l'Environnement et le Développement, Nogent-sur-Marne)

Abstract

We apply the advanced time-and-frequency-domain method of singular spectrum analysis to study business cycle dynamics in a set of nine U.S. macroeconomic indicators. This method provides a robust way to identify and reconstruct shared oscillations, whether intermittent or modulated. We address the problem of spurious cycles generated by the use of detrending filters and present a Monte Carlo test to extract significant oscillations. Finally, we demonstrate that the behavior of the U.S. economy changes significantly between episodes of growth and recession; these variations cannot be generated by random shocks alone, in the absence of endogenous variability.

Suggested Citation

  • Andreas Groth & Michael Ghil & Stéphane Hallegatte & Patrice Dumas, 2012. "The Role of Oscillatory Modes in U.S. Business Cycles," Working Papers 2012.26, Fondazione Eni Enrico Mattei.
    • Handle: RePEc:fem:femwpa:2012.26
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    References listed on IDEAS

    as
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    4. Hallegatte, Stéphane & Ghil, Michael & Dumas, Patrice & Hourcade, Jean-Charles, 2008. "Business cycles, bifurcations and chaos in a neo-classical model with investment dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 67(1), pages 57-77, July.
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    11. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-247, July-Sept.
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    15. Chang-Jin Kim & Charles R. Nelson, 1999. "Has The U.S. Economy Become More Stable? A Bayesian Approach Based On A Markov-Switching Model Of The Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 608-616, November.
    16. Cogley, Timothy & Nason, James M., 1995. "Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research," Journal of Economic Dynamics and Control, Elsevier, vol. 19(1-2), pages 253-278.
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    Cited by:

    1. Andreas Groth & Patrice Dumas & Michael Ghil & Stéphane Hallegatte, 2015. "Impacts of Natural Disasters on a Dynamic Economy," Post-Print hal-01678074, HAL.
    2. Karlo Kauko & Eero Tölö, 2019. "Banking Crisis Prediction with Differenced Relative Credit," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot GmbH, Berlin, vol. 65(4), pages 277-297.
    3. Vivien Sainte Fare Garnot & Andreas Groth & Michael Ghil, 2018. "Coupled Climate-Economic Modes in the Sahel's Interannual Variability," Post-Print hal-01855370, HAL.
    4. Lisa Sella & Gianna Vivaldo & Andreas Groth & Michael Ghil, 2016. "Economic Cycles and Their Synchronization: A Comparison of Cyclic Modes in Three European Countries," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 25-48, September.
    5. Donya Rahmani & Damien Fay, 2022. "A state‐dependent linear recurrent formula with application to time series with structural breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 43-63, January.
    6. Lisa Sella & Gianna Vivaldo & Andreas Groth & Michael Ghil, 2016. "Economic Cycles and Their Synchronization: A Comparison of Cyclic Modes in Three European Countries," Post-Print hal-01701122, HAL.
    7. Sainte Fare Garnot, Vivien & Groth, Andreas & Ghil, Michael, 2018. "Coupled Climate-Economic Modes in the Sahel's Interannual Variability," Ecological Economics, Elsevier, vol. 153(C), pages 111-123.
    8. Škare, Marinko & Porada-Rochoń, Małgorzata, 2020. "Multi-channel singular-spectrum analysis of financial cycles in ten developed economies for 1970–2018," Journal of Business Research, Elsevier, vol. 112(C), pages 567-575.
    9. Juan B'ogalo & Pilar Poncela & Eva Senra, 2020. "Understanding fluctuations through Multivariate Circulant Singular Spectrum Analysis," Papers 2007.07561, arXiv.org, revised Aug 2023.
    10. Hicham M. Hachem, 2017. "How Moderate was the Great Moderation and how Destabilizing is Secular Stagnation? Fiscal and monetary policy implications based on åvidence from US macro data," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 226-236, June.

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    More about this item

    Keywords

    Advanced Spectral Methods; Comovements; Frequency Domain; Monte Carlo testing; Time Domain;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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