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Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory

Author

Listed:
  • Wang, Na
  • Li, Dong
  • Wang, Qiwen

Abstract

The visibility graph approach and complex network theory provide a new insight into time series analysis. The inheritance of the visibility graph from the original time series was further explored in the paper. We found that degree distributions of visibility graphs extracted from Pseudo Brownian Motion series obtained by the Frequency Domain algorithm exhibit exponential behaviors, in which the exponential exponent is a binomial function of the Hurst index inherited in the time series. Our simulations presented that the quantitative relations between the Hurst indexes and the exponents of degree distribution function are different for different series and the visibility graph inherits some important features of the original time series. Further, we convert some quarterly macroeconomic series including the growth rates of value-added of three industry series and the growth rates of Gross Domestic Product series of China to graphs by the visibility algorithm and explore the topological properties of graphs associated from the four macroeconomic series, namely, the degree distribution and correlations, the clustering coefficient, the average path length, and community structure. Based on complex network analysis we find degree distributions of associated networks from the growth rates of value-added of three industry series are almost exponential and the degree distributions of associated networks from the growth rates of GDP series are scale free. We also discussed the assortativity and disassortativity of the four associated networks as they are related to the evolutionary process of the original macroeconomic series. All the constructed networks have “small-world” features. The community structures of associated networks suggest dynamic changes of the original macroeconomic series. We also detected the relationship among government policy changes, community structures of associated networks and macroeconomic dynamics. We find great influences of government policies in China on the changes of dynamics of GDP and the three industries adjustment. The work in our paper provides a new way to understand the dynamics of economic development.

Suggested Citation

  • Wang, Na & Li, Dong & Wang, Qiwen, 2012. "Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6543-6555.
  • Handle: RePEc:eee:phsmap:v:391:y:2012:i:24:p:6543-6555
    DOI: 10.1016/j.physa.2012.07.054
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    References listed on IDEAS

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    1. Vasiliki Plerou & Parameswaran Gopikrishnan & Bernd Rosenow & Luis A. Nunes Amaral & H. Eugene Stanley, 1999. "Universal and non-universal properties of cross-correlations in financial time series," Papers cond-mat/9902283, arXiv.org.
    2. Liu, Chuang & Zhou, Wei-Xing & Yuan, Wei-Kang, 2010. "Statistical properties of visibility graph of energy dissipation rates in three-dimensional fully developed turbulence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(13), pages 2675-2681.
    3. Yang, Yue & Wang, Jianbo & Yang, Huijie & Mang, Jingshi, 2009. "Visibility graph approach to exchange rate series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(20), pages 4431-4437.
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    Citations

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    Cited by:

    1. An, Haizhong & Gao, Xiangyun & Fang, Wei & Huang, Xuan & Ding, Yinghui, 2014. "The role of fluctuating modes of autocorrelation in crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 382-390.
    2. repec:eee:touman:v:54:y:2016:i:c:p:23-33 is not listed on IDEAS
    3. Tang, Jinjun & Wang, Yinhai & Liu, Fang, 2013. "Characterizing traffic time series based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4192-4201.
    4. Chen, Shiyu & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "A visibility graph averaging aggregation operator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 1-12.
    5. repec:eee:touman:v:63:y:2017:i:c:p:368-382 is not listed on IDEAS
    6. Tang, Jinjun & Wang, Yinhai & Wang, Hua & Zhang, Shen & Liu, Fang, 2014. "Dynamic analysis of traffic time series at different temporal scales: A complex networks approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 303-315.
    7. repec:eee:phsmap:v:482:y:2017:i:c:p:42-55 is not listed on IDEAS
    8. Sun, Mei & Wang, Yaqi & Gao, Cuixia, 2016. "Visibility graph network analysis of natural gas price: The case of North American market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1-11.
    9. Yan, Ying & Zhang, Shen & Tang, Jinjun & Wang, Xiaofei, 2017. "Understanding characteristics in multivariate traffic flow time series from complex network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 477(C), pages 149-160.

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