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Decomposing the co-movement of the business cycle: a time-frequency analysis of growth cycles in the euro area

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  • Crowley , Patrick

    ()
    (Bank of Finland Research and College of Business, Texas A&M University)

  • Lee , Jim

    (College of Business, Texas A&M University)

Abstract

This article analyses the frequency components of European business cycles using real GDP by employ-ing multiresolution decomposition (MRD) with the use of maximal overlap discrete wavelet transforms (MODWT). Static wavelet variance and correlation analysis is performed, and phasing is studied using co-correlation with the euro area by scale. Lastly dynamic conditional correlation GARCH models are used to obtain dynamic correlation estimates by scale against the EU to evaluate synchronicity of cycles through time. The general findings are that euro area members fall into one of three categories: i) high and dynamic correlations at all frequency cycles (eg France, Belgium, Germany), ii) low static and dy-namic correlations, with little sign of convergence occurring (eg Greece), and iii) low static correlation but convergent dynamic correlations (eg Finland and Ireland).

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Bibliographic Info

Paper provided by Bank of Finland in its series Research Discussion Papers with number 12/2005.

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Length: 72 pages
Date of creation: 11 May 2005
Date of revision:
Handle: RePEc:hhs:bofrdp:2005_012

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Postal: Bank of Finland, P.O. Box 160, FI-00101 Helsinki, Finland
Web page: http://www.suomenpankki.fi/en/
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Keywords: business cycles; growth cycles; European Union; multiresolution analysis; wavelets; co-correlation; dynamic correlation;

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References

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Citations

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Cited by:
  1. Zagaglia, Paolo, 2006. "The Predictive Power of the Yield Spread under the Veil of Time," Research Papers in Economics 2006:4, Stockholm University, Department of Economics.
  2. Joanna Bruzda, 2011. "Business cycle synchronization according to wavelets – the case of Poland and the euro zone member countries," Bank i Kredyt, National Bank of Poland, Economic Institute, vol. 42(3), pages 5-32.
  3. Benhmad, François, 2013. "Dynamic cyclical comovements between oil prices and US GDP: A wavelet perspective," Energy Policy, Elsevier, vol. 57(C), pages 141-151.
  4. Michal Bencik, 2011. "Business cycle synchronisation between the V4 countries and the euro area," Working and Discussion Papers WP 1/2011, Research Department, National Bank of Slovakia.
  5. Crowley , Patrick & Maraun , Douglas & Mayes , David, 2006. "How hard is the euro are core? An evaluation of growth cycles using wavelet analysis," Research Discussion Papers 18/2006, Bank of Finland.
  6. Crowley, Patrick, 2008. "One Money, Several Cycles? Evaluation of European business cycles using model-based cluster analysis," Research Discussion Papers 3/2008, Bank of Finland.
  7. Crowley, Patrick M & Schultz, Aaron, 2010. "A new approach to analyzing convergence and synchronicity in growth and business cycles: cross recurrence plots and quantification analysis," Research Discussion Papers 16/2010, Bank of Finland.
  8. Papageorgiou, Theofanis & Michaelides, Panayotis G. & Milios, John G., 2010. "Business cycles synchronization and clustering in Europe (1960-2009)," Journal of Economics and Business, Elsevier, vol. 62(5), pages 419-470, September.

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