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GLOBAL VS. GROUP-SPECIFIC BUSINESS CYCLES: THE IMPORTANCE OF DEFINING THE GROUPS

Published online by Cambridge University Press:  13 March 2020

Tino Berger  and
Marcus Wortmann
Tino Berger*
Affiliation:
Georg-August-UniversityGöttingen
Marcus Wortmann
Affiliation:
Georg-August-UniversityGöttingen
*
Address correspondence to: Tino Berger, Georg-August-University Göttingen, Platz der Göttinger Sieben 3, Göttingen37073, Germany. e-mail: [email protected].
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Abstract

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The literature on international business cycles has employed dynamic factor models (DFMs) to disentangle global from group-specific and national factors in countries’ macroeconomic aggregates. Therefore, the countries have simply been classified ex ante as belonging to the same region or the same level of development. This paper estimates a DFM for a sample of 106 countries and three variables (output, consumption, investment) over the period 1960–2014, in which the countries are classified according to the outcome of a cluster analysis. By comparing the results with those obtained by the previous grouping approaches, we show substantial deviations in the importance of global and group-specific factors. Remarkably, when the groups are defined properly, the “global business cycle” accounts for only a very small fraction of macroeconomic fluctuations, most evidently in the industrialized world. The group-specific factors, on the other hand, play a much greater role for national business cycles than previously thought—also in the pre-globalization period.

Keywords

International Business CyclesGlobalizationRegionalizationDynamic Factor ModelsCluster Analysis
Type
Articles
Information
Macroeconomic Dynamics , Volume 26 , Issue 1 , January 2022 , pp. 49 - 71
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© Cambridge University Press 2020

References

REFERENCES

Calinski, T. and Harabasz, J. (1974) A dendrite method for cluster analysis. Communications in Statistics 3, 1–27.Google Scholar
Ductor, L. and Leiva-Leon, D. (2016) Dynamics of global business cycle interdependence. Journal of International Economics 102, 110–27.CrossRefGoogle Scholar
Duda, R. O. and Hart, P. E. (1973) Pattern Classification and Scene Analysis. New York, NY: Wiley.Google Scholar
Francis, N., Owyang, M. T. and Savascin, O. (2017) An endogenously clustered factor approach to international business cycles. Journal of Applied Econometrics 2017(0), 1–16.Google Scholar
Helbling, T., Berezin, P., Kose, M. A., Kumhof, M., Laxton, D. and Spatafora, N. (2007) Decoupling the train? Spillovers and cycles in the global economy. In: IMF World Economic Outlook, Chapter 4, pp. 121–160 Washington, DC: International Monetary Fund. doi: https://doi.org/10.5089/9781589066267.081.Google Scholar
Hirata, H., Kose, M. A. and Otrok, C. (2013) Regionalization vs. Globalization. IMF Working Paper 13/19.Google Scholar
Kose, M. A., Otrok, C. and Prasad, E. (2012) Global business cycles: convergence or decoupling? International Economic Review 53(2), 511–538.CrossRefGoogle Scholar
Kose, M. A., Otrok, C. and Whiteman, C. H. (2003) International business cycles: world, region, and country-specific factors. American Economic Review 93(4), 1216–1239.CrossRefGoogle Scholar
Milligan, G. W. and Cooper, M. C. (1985) An examination of procedures for determining the number of clusters in a dataset. Psychometrika 50, 159–179.CrossRefGoogle Scholar
Mumtaz, H., Simonelli, S. and Surico, P. (2011) International comovements, business cycle and inflation: a historical perspective. Review of Economic Dynamics 14(1), 176–198.CrossRefGoogle Scholar
Otrok, C. and Whiteman, C. H. (1998) Bayesian leading indicators: Measuring and predicting economic conditions in Iowa. International Economic Review 39(4), 997–1014.CrossRefGoogle Scholar