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Convergence and cluster structures in EU area according to fluctuations in macroeconomic indices

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  • Mircea Gligor
  • Marcel Ausloos

Abstract

The cluster analysis methods are used in order to perform a comparative study of 15 EU countries in relation with the fluctuations of some basic macroeconomic indicators. The statistical distances between countries are calculated for various moving time windows, and the time variation of the mean statistical distance is investigated. The decreasing of the mean statistical distance between EU countries is reflected in the correlated fluctuations of the basic ME indicators: GDP, GDP/capita, Consumption and Investments. This empirical evidence can be seen as an economic aspect of globalization. The Moving Average Minimal Length Path (MAMLP) algorithm allows to search for a cluster-like structures derived both from the hierarchical organization of countries and from their relative movement inside the hierarchy. It is found that the strongly correlated countries with respect to GDP fluctuations can be partitioned into stable clusters. Some of the highly correlated countries, with respect to GDP fluctuations, display strong correlations also in the Final Consumption Expenditure, while others are strongly correlated in Gross Capital Formation. On the other hand, one notices the similitude of the classifications regarding GDP and Net Exports fluctuations as concerns the squared sum of the correlation coefficients (so called country sensitivity). The final structure proves to be robust against the constant size time window moving over the scanned time interval. The policy implications of the above empirical results 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 in elaborating a cluster-promotion policy.

Suggested Citation

  • Mircea Gligor & Marcel Ausloos, 2008. "Convergence and cluster structures in EU area according to fluctuations in macroeconomic indices," Papers 0805.3071, arXiv.org.
  • Handle: RePEc:arx:papers:0805.3071
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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 and Business, University of Belgrade, vol. 60(206), pages 45-86, July - Se.
    2. 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.
    3. Ana-Maria HOLOBIUC, 2020. "Assesing The Effects Of The Economic And Financial Crisis On Income Convergence In The Eurozone," Contemporary Economy Journal, Constantin Brancoveanu University, vol. 5(3), pages 134-140.
    4. Marcel Ausloos & Francesca Bartolacci & Nicola G. Castellano & Roy Cerqueti, 2018. "Exploring how innovation strategies at time of crisis influence performance: a cluster analysis perspective," Papers 1808.05893, arXiv.org.
    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. 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 Nov 2020.
    7. Marcel Ausloos, 2013. "Econophysics: Comments on a Few Applications, Successes, Methods and Models," IIM Kozhikode Society & Management Review, , vol. 2(2), pages 101-115, July.
    8. Anna Maria D’Arcangelis & Giulia Rotundo, 2016. "Complex Networks in Finance," Lecture Notes in Economics and Mathematical Systems, in: Pasquale Commendatore & Mariano Matilla-García & Luis M. Varela & Jose S. Cánovas (ed.), Complex Networks and Dynamics, pages 209-235, Springer.
    9. Li-Wei Dai & Chin-Yi Fang, 2023. "The Role of Corporate Governance in Sustaining the Economy: Examining Its Moderating Effect on Brand Equity and Profitability in Tourism Companies," Sustainability, MDPI, vol. 15(17), pages 1-17, August.
    10. Roy Cerqueti & Catherine Deffains‐Crapsky & Saverio Storani, 2023. "Green finance instruments: Exploring minibonds issuance in Italy," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 30(4), pages 1965-1986, July.
    11. Bongiorno, Christian & Miccichè, Salvatore & Mantegna, Rosario N., 2022. "Statistically validated hierarchical clustering: Nested partitions in hierarchical trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    12. 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.
    13. Ausloos, Marcel & Saeedian, Meghdad & Jamali, Tayeb & Farahani, S. Vasheghani & Jafari, G. Reza, 2017. "How visas shape and make visible the geopolitical architecture of the planet," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 267-275.
    14. 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.
    15. 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 Business and Economics.
    16. 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.
    17. Marcel Ausloos & Francesca Bartolacci & Nicola G. Castellano & Roy Cerqueti, 2020. "Simple approaches on how to discover promising strategies for efficient enterprise performance, at time of crisis in the case of SMEs : Voronoi clustering and outlier effects perspective," Papers 2012.14297, arXiv.org.
    18. Manavi, Seyed Alireza & Jafari, Gholamreza & Rouhani, Shahin & Ausloos, Marcel, 2020. "Demythifying the belief in cryptocurrencies decentralized aspects. A study of cryptocurrencies time cross-correlations with common currencies, commodities and financial indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).

    More about this item

    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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