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Correlation measure to detect time series distances, whence economy globalization

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  • Miśkiewicz, Janusz
  • Ausloos, Marcel

Abstract

An instantaneous time series distance is defined through the equal time correlation coefficient. The idea is applied to the Gross Domestic Product (GDP) yearly increments of 21 rich countries between 1950 and 2005 in order to test the process of economic globalisation. Some data discussion is first presented to decide what (EKS, GK, or derived) GDP series should be studied. Distances are then calculated from the correlation coefficient values between pairs of series. The role of time averaging of the distances over finite size windows is discussed. Three network structures are next constructed based on the hierarchy of distances. It is shown that the mean distance between the most developed countries on several networks actually decreases in time, —which we consider as a proof of globalization. An empirical law is found for the evolution after 1990, similar to that found in flux creep. The optimal observation time window size is found ≃15 years.

Suggested Citation

  • Miśkiewicz, Janusz & Ausloos, Marcel, 2008. "Correlation measure to detect time series distances, whence economy globalization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6584-6594.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:26:p:6584-6594
    DOI: 10.1016/j.physa.2008.08.004
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    2. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
    3. Clippe, Paulette & Ausloos, Marcel, 2012. "Benford’s law and Theil transform of financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6556-6567.
    4. 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.
    5. 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.
    6. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2010. "Complex stock trading network among investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4929-4941.
    7. Antonis A. Michis, 2021. "Wavelet Multidimensional Scaling Analysis of European Economic Sentiment Indicators," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 443-480, October.
    8. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
    9. Miśkiewicz, Janusz, 2010. "Entropy correlation distance method. The Euro introduction effect on the Consumer Price Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1677-1687.
    10. 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.
    11. Janusz Mi'skiewicz, 2012. "Network analysis of correlation strength between the most developed countries," Papers 1211.3599, arXiv.org.
    12. Miśkiewicz, Janusz, 2008. "Globalization — Entropy unification through the Theil index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6595-6604.

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