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The role of oscillatory modes in US business cycles

Author

Listed:
  • Andreas Groth
  • Michael Ghil
  • Stéphane Hallegatte
  • Patrice Dumas

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.

Suggested Citation

  • Andreas Groth & Michael Ghil & Stéphane Hallegatte & Patrice Dumas, 2015. "The role of oscillatory modes in US business cycles," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2015(1), pages 63-81.
  • Handle: RePEc:oec:stdkab:5jrs0lv715wl
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    File URL: http://dx.doi.org/10.1787/jbcma-2015-5jrs0lv715wl
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    References listed on IDEAS

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    1. Canova, Fabio, 1998. "Detrending and business cycle facts: A user's guide," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 533-540, May.
    2. Canova, Fabio, 1998. "Detrending and business cycle facts," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 475-512, May.
    3. 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.
    4. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, June.
    5. 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.
    6. de Carvalho, Miguel & Rodrigues, Paulo C. & Rua, António, 2012. "Tracking the US business cycle with a singular spectrum analysis," Economics Letters, Elsevier, vol. 114(1), pages 32-35.
    7. French, Mark W & Sichel, Daniel E, 1993. "Cyclical Patterns in the Variance of Economic Activity," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 113-119, January.
    8. Hallegatte, Stéphane & Ghil, Michael, 2008. "Natural disasters impacting a macroeconomic model with endogenous dynamics," Ecological Economics, Elsevier, vol. 68(1-2), pages 582-592, December.
    9. Chiarella,Carl & Flaschel,Peter & Franke,Reiner, 2011. "Foundations for a Disequilibrium Theory of the Business Cycle," Cambridge Books, Cambridge University Press, number 9780521369923.
    10. Kim, Chang-Jin & Nelson, Charles R & Piger, Jeremy, 2004. "The Less-Volatile U.S. Economy: A Bayesian Investigation of Timing, Breadth, and Potential Explanations," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 80-93, January.
    11. 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.
    12. 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. 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.
    2. Andreas Groth & Patrice Dumas & Michael Ghil & Stéphane Hallegatte, 2015. "Impacts of Natural Disasters on a Dynamic Economy," Post-Print hal-01678074, HAL.
    3. repec:spr:jbuscr:v:12:y:2016:i:1:d:10.1007_s41549-016-0003-4 is not listed on IDEAS

    More about this item

    Keywords

    Advanced spectral methods; comovements; frequency domain; Monte Carlo testing; time domain;

    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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