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Convergence and Cluster Structures in EU Area according to Fluctuations in Macroeconomic Indices

Author

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  • Gligor, Mircea

    () (University of Liege)

  • Ausloos, Marcel

    () (University of Liege)

Abstract

Cluster analysis methods allow for a comparative study of countries through basic macroeconomic indicator fluctuations. Statistical distances between 15 EU countries are first calculated for various moving time windows. The decrease in time of the mean statistical distance is observed through the correlated fluctuations of typical macroeconomic indicators: GDP, GDP/capita, Consumption and Investments. This empirical evidence can be seen as a mark of globalization. The Moving Average Minimal Length Path algorithm indicates the existence of cluster-like structures both in the hierarchical organization of countries and their relative movements inside the hierarchy. The most strongly correlated countries with respect to GDP fluctuations can be partitioned into stable clusters. Several so correlated countries display strong correlations also in the Final Consumption Expenditure; others are strongly correlated in the Gross Capital Formation. The similarity between the classifications due to GDP and Net Exports fluctuations is pointed out through the squared sum of the correlation coefficients, a so called “country sensitivity”. The structures are robust against changes in time window size. Policy implications concern the economic clusters arising in the presence of Marshallian externalities and the relationships between trade barriers, R&D incentives and growth that must be accounted for in elaborating cluster-promotion policies.

Suggested Citation

  • Gligor, Mircea & Ausloos, Marcel, 2008. "Convergence and Cluster Structures in EU Area according to Fluctuations in Macroeconomic Indices," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 23, pages 297-330.
  • Handle: RePEc:ris:integr:0435
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    Cited by:

    1. Jasna Soldić-Aleksić & Rade Stankić, 2015. "A Comparative Analysis Of Serbia And The Eu Member States In The Context Of The Networked Readiness Index Values," Economic Annals, Faculty of Economics, University of Belgrade, vol. 60(206), pages 45-86, July - Se.
    2. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised May 2018.
    3. repec:eee:phsmap:v:484:y:2017:i:c:p:267-275 is not listed on IDEAS
    4. Miguel Carvalho & António Rua, 2014. "Extremal Dependence in International Output Growth: Tales from the Tails," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(4), pages 605-620, August.
    5. Maharaj, Elizabeth Ann & D’Urso, Pierpaolo, 2010. "A coherence-based approach for the pattern recognition of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3516-3537.
    6. Miśkiewicz, Janusz, 2013. "Power law classification scheme of time series correlations. On the example of G20 group," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2150-2162.
    7. de Mattos Neto, Paulo S.G. & Cavalcanti, George D.C. & Madeiro, Francisco & Ferreira, Tiago A.E., 2013. "An ideal gas approach to classify countries using financial indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 177-183.
    8. Redelico, Francisco O. & Proto, Araceli N. & Ausloos, Marcel, 2009. "Hierarchical structures in the Gross Domestic Product per capita fluctuation in Latin American countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3527-3535.
    9. VAN POECK, André, 2009. "One money and fifteen needs inflation and output convergence in the European Monetary Union," Working Papers 2009001, University of Antwerp, Faculty of Applied Economics.

    More about this item

    Keywords

    Statistical distances; Minimal length path; Convergence; Clusteri;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries

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