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The role of oscillatory modes in US business cycles

Author

Listed:
  • Andreas Groth
  • M. Ghil

    (IGPP - Institute of Geophysics and Planetary Physics [Los Angeles] - UCLA - University of California [Los Angeles] - UC - University of California, AOS - Department of Atmospheric and Oceanic Sciences [Los Angeles] - UCLA - University of California [Los Angeles] - UC - University of California, LMD - Laboratoire de Météorologie Dynamique (UMR 8539) - UPMC - Université Pierre et Marie Curie - Paris 6 - INSU - CNRS - Institut national des sciences de l'Univers - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - Département des Géosciences - ENS Paris - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres)

  • Stéphane Hallegatte

    (CIRED - centre international de recherche sur l'environnement et le développement - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique)

  • Patrice Dumas

    (CIRED - centre international de recherche sur l'environnement et le développement - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique)

Abstract

We apply multivariate singular spectrum analysis to the study of US business cycle dynamics. This method provides a robust way to identify and reconstruct oscillations, whether intermittent or modulated. We show such oscillations to be associated with comovements across the entire economy. The problem of spurious cycles generated by the use of detrending filters is addressed and we present a Monte Carlo test to extract significant oscillations. The behavior of the US economy is shown to change significantly from one phase of the business cycle to another: the recession phase is dominated by a five-year mode, while the expansion phase exhibits more complex dynamics, with higher-frequency modes coming into play. We show that the variations so identified cannot be generated by random shocks alone, as assumed in "real" business-cycle models, and that endogenous, deterministically generated variability has to be involved. © OECD 2015.

Suggested Citation

  • Andreas Groth & M. Ghil & Stéphane Hallegatte & Patrice Dumas, 2015. "The role of oscillatory modes in US business cycles," Post-Print hal-01239779, HAL.
  • Handle: RePEc:hal:journl:hal-01239779
    DOI: 10.1787/jbcma-2015-5jrs0lv715wl
    Note: View the original document on HAL open archive server: https://enpc.hal.science/hal-01239779
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    References listed on IDEAS

    as
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    Citations

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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; JEL classification: C15; C60; E32;
    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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