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Disentangling different patterns of business cycle synchronicity in the EU regions

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Abstract

The present paper provides a comprehensive and consolidated analysis of the business cycle synchronicity between European regions and EU-14. Our study is conducted in three levels. First, we analyse regional business cycle synchronization with the EU-14 benchmark cycle, using real GDP in 200 NUTS II regions for a period of 30 years (1980–2009), detrended by Hodrick–Prescott filter. Secondly, we employ a VAR type methodology as a measurement devise to examine the dynamic relationship of the regional business cycles. Our main interest is to study the dynamics of business cycles as well as the pattern of the transmission mechanism to regions with different level of development. Finally, we empirically extend the research on identifying factors which might drive regional business cycle synchronization. In particular, we analyse the role of trade integration-cum- the sectoral patterns of specialisation as determinants of regional growth cycle correlations with the EU-14. Moreover, we draw attention to regional productivity as another possible determinant of business cycle synchronisation associated with the pattern of the spatial distribution of economic activities across regions. Panel three-stage least-squares estimation is implemented for the simultaneous equations between determinants and regional business cycles synchronisation.

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Notes

  1. Luxembourg was discarded from the sample because of lack of data.

  2. See Canova and F Ciccarelli (2013) the main features of the mentioned models and how they compare to the PVAR models.

  3. To perform the analysis we used the STATA pvar routine written by Inessa Love (see Love and Zicchino 2006).

  4. Our identification scheme is based on a lower triangular Cholesky decomposition with the above indicated ordering. Hence, a variable coming earlier in the ordering affects the next ones both contemporaneously and with a lag, while a variable coming later has merely lagged effects on the preceding ones. This implies that structural shocks of national and EU14 affect regional cycles but not vise-versa. Reversing the order was also tested but results were approximately the same (not shown in our paper). To complete the interpretation of our findings, we also expose the matrix with variance decompositions, which describe the percent of one variable explained by innovations accumulated over time in another variable.

  5. The panel is balanced.

  6. Pedroni Residual Cointegration Test, Johansen Fisher Panel Cointegration Test, and Kao Residual Cointegration Test were employed to test for cointegration in our panel sample, the existence of the cointegration relationship was no supported, the results are not presented here for economy of space.

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Authors and Affiliations

  1. Department of Economics, University of Thessaly, Volos, Greece

    Ageliki Anagnostou

  2. Department of Planning and Regional Development, University of Thessaly, Volos, Greece

    Ioannis Panteladis & Maria Tsiapa

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  1. Ageliki Anagnostou
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  2. Ioannis Panteladis
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Correspondence to Ageliki Anagnostou.

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Anagnostou, A., Panteladis, I. & Tsiapa, M. Disentangling different patterns of business cycle synchronicity in the EU regions. Empirica 42, 615–641 (2015). https://doi.org/10.1007/s10663-014-9268-9

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