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Economic Cycles and Their Synchronization: A Comparison of Cyclic Modes in Three European Countries

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Abstract

The present work applies singular spectrum analysis (SSA) to the study of macroeconomic fluctuations in three European countries: Italy, The Netherlands, and the United Kingdom. This advanced spectral method provides valuable spatial and frequency information for multivariate data sets and goes far beyond the classical forms of time domain analysis. In particular, SSA enables us to identify dominant cycles that characterize the deterministic behavior of each time series separately, as well as their shared behavior. We demonstrate its usefulness by analyzing several fundamental indicators of the three countries’ real aggregate economy in a univariate, as well as a multivariate setting. Since business cycles are international phenomena, which show common characteristics across countries, our aim is to uncover supranational behavior within the set of representative European economies selected herein. Finally, the analysis is extended to include several indicators from the U.S. economy, in order to examine its influence on the European economies under study and their interrelationships.

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Notes

  1. Quarterly national accounts are compiled according to the European System of Accounts (ESA95). Data are available from EUROSTAT at http://epp.eurostat.ec.europa.eu.

  2. In 2008, the ratio of imports to GDP was around 28 % for Italy, 34 % for the UK, and 77 % for The Netherlands. The exports were about 28, 29, and 84 % of GDP, respectively.

  3. In 2000, the UK exported to the U.S. 15.4 % of its total exports and imported from it 13.2 % of its total imports, while Italy exported 14.5 % of its total to Germany and imported 17.7 % from it, cf. Feenstra et al. (2005).

  4. For instance, Italian collective agreements involve very complex firing procedures for firms that have more than 15 workers. As a consequence, both medium- and large-size firms are strongly discouraged from modifying their employment level.

  5. All series are expressed in constant year-2000 Euros. They are seasonally adjusted and corrected by working days, following the TRAMO-SEATS procedure (Maravall 2005).

  6. Plotting the eigenvalues against their rank, as originally proposed by Vautard and Ghil (1989), is more useful in distinguishing between signal and noise. In identifying and statistically testing for oscillatory modes, as we do here, it is more informative to plot the eigenvalues versus the associated frequency of the corresponding eigenvectors (cf. Allen and Smith 1996).

  7. Note that the chance of not being significant increases with the period length.

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Acknowledgments

We would like to thank Pietro Terna and Vittorio Valli for their valuable guidance to the two lead authors and for their many suggestions about the economic interpretation of this work’s results. AG acknowledges support from the Groupement d’Intérêt Scientifique (GIS) Réseau de Recherche sur le Développement Soutenable (R2DS) of the Région Ile-de-France while affiliated with the Environmental Research and Teaching Institute at the Ecole Normale Supérieure in Paris. AG and MG both received support from NSF grant OCE-1243175, as well as from ONR MURI Grant N00014-12-1-0911.

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Authors and Affiliations

  1. Department of Economics and Statistics “S. Cognetti de Martiis”, University of Turin, Turin, Italy

    Lisa Sella

  2. CNR-IRCrES, Moncalieri, Turin, Italy

    Lisa Sella

  3. IMT School for Advanced Studies, Lucca, Italy

    Gianna Vivaldo

  4. Environmental Research and Teaching Institute & Geosciences Department, Ecole Normale Supérieure, Paris, France

    Andreas Groth & Michael Ghil

  5. Department of Atmospheric and Oceanic Sciences & Institute of Geophysics and Planetary Physics, University of California, Los Angeles, CA, USA

    Andreas Groth & Michael Ghil

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  1. Lisa Sella
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Correspondence to Lisa Sella.

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Sella, L., Vivaldo, G., Groth, A. et al. Economic Cycles and Their Synchronization: A Comparison of Cyclic Modes in Three European Countries. J Bus Cycle Res 12, 25–48 (2016). https://doi.org/10.1007/s41549-016-0003-4

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