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Cluster structure of EU-15 countries derived from the correlation matrix analysis of macroeconomic index fluctuations

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  • M. Gligor
  • M. Ausloos

Abstract

The statistical distances between countries, calculated for various moving average time windows, are mapped into the ultrametric subdominant space as in classical Minimal Spanning Tree methods. The Moving Average Minimal Length Path (MAMLP) algorithm allows a decoupling of fluctuations with respect to the mass center of the system from the movement of the mass center itself. A Hamiltonian representation given by a factor graph is used and plays the role of cost function. The present analysis pertains to 11 macroeconomic (ME) indicators, namely the GDP (x 1 ), Final Consumption Expenditure (x 2 ), Gross Capital Formation (x 3 ), Net Exports (x 4 ), Consumer Price Index (y 1 ), Rates of Interest of the Central Banks (y 2 ), Labour Force (z 1 ), Unemployment (z 2 ), GDP/hour worked (z 3 ), GDP/capita (w 1 ) and Gini coefficient (w 2 ). The target group of countries is composed of 15 EU countries, data taken between 1995 and 2004. By two different methods (the Bipartite Factor Graph Analysis and the Correlation Matrix Eigensystem Analysis) it is found that the strongly correlated countries with respect to the macroeconomic indicators fluctuations can be partitioned into stable clusters. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2007

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  • M. Gligor & M. Ausloos, 2007. "Cluster structure of EU-15 countries derived from the correlation matrix analysis of macroeconomic index fluctuations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 57(2), pages 139-146, May.
    • Handle: RePEc:spr:eurphb:v:57:y:2007:i:2:p:139-146
    DOI: 10.1140/epjb/e2007-00132-5
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    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. Cheong, Siew Ann & Fornia, Robert Paulo & Lee, Gladys Hui Ting & Kok, Jun Liang & Yim, Woei Shyr & Xu, Danny Yuan & Zhang, Yiting, 2011. "The Japanese economy in crises: A time series segmentation study," Economics Discussion Papers 2011-24, Kiel Institute for the World Economy (IfW Kiel).
    3. 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.
    4. 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.
    5. Ausloos, Marcel & Jovanovic, Franck & Schinckus, Christophe, 2016. "On the “usual” misunderstandings between econophysics and finance: Some clarifications on modelling approaches and efficient market hypothesis," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 7-14.
    6. 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.
    7. Cheong, Siew Ann & Fornia, Robert Paulo & Lee, Gladys Hui Ting & Kok, Jun Liang & Yim, Woei Shyr & Xu, Danny Yuan & Zhang, Yiting, 2012. "The Japanese economy in crises: A time series segmentation study," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 6, pages 1-81.
    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. 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).
    10. 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.
    11. Yves Surry & Konstantinos Galanopoulos, 2014. "A random matrix theory approach to test for agricultural productivity convergence," Applied Economics Letters, Taylor & Francis Journals, vol. 21(18), pages 1319-1323, December.
    12. 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.
    13. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.
    14. Zhang, Yiting & Lee, Gladys Hui Ting & Wong, Jian Cheng & Kok, Jun Liang & Prusty, Manamohan & Cheong, Siew Ann, 2011. "Will the US economy recover in 2010? A minimal spanning tree study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2020-2050.
    15. James Graham, 2014. "'N Sync: how do countries' economies move together?," Reserve Bank of New Zealand Analytical Notes series AN2014/04, Reserve Bank of New Zealand.

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