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A study of causality structure and dynamics in industrial electricity consumption based on Granger network

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  • Yao, Can-Zhong
  • Lin, Ji-Nan
  • Lin, Qing-Wen
  • Zheng, Xu-Zhou
  • Liu, Xiao-Feng

Abstract

Based on industrial electricity consumption, we model industrial networks by Granger causality method and MST (minimum spanning tree), and then further stick onto an industrial coupling mechanism from energy-consumption perspective.

Suggested Citation

  • Yao, Can-Zhong & Lin, Ji-Nan & Lin, Qing-Wen & Zheng, Xu-Zhou & Liu, Xiao-Feng, 2016. "A study of causality structure and dynamics in industrial electricity consumption based on Granger network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 297-320.
    • Handle: RePEc:eee:phsmap:v:462:y:2016:i:c:p:297-320
    DOI: 10.1016/j.physa.2016.06.100
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    1. Didier Sornette & Wei-Xing Zhou, 2005. "Non-parametric determination of real-time lag structure between two time series: the 'optimal thermal causal path' method," Quantitative Finance, Taylor & Francis Journals, vol. 5(6), pages 577-591.
    2. Kun Guo & Wei-Xing Zhou & Si-Wei Cheng & Didier Sornette, 2011. "The US Stock Market Leads the Federal Funds Rate and Treasury Bond Yields," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-9, August.
    3. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    4. Asafu-Adjaye, John, 2000. "The relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries," Energy Economics, Elsevier, vol. 22(6), pages 615-625, December.
    5. Ersin Kantar & Bayram Deviren & Mustafa Keskin, 2014. "Hierarchical structure of the European countries based on debts as a percentage of GDP during the 2000-2011 period," Papers 1406.6559, arXiv.org, revised Jun 2014.
    6. Lee, Chien-Chiang, 2005. "Energy consumption and GDP in developing countries: A cointegrated panel analysis," Energy Economics, Elsevier, vol. 27(3), pages 415-427, May.
    7. Bernstein, Ronald & Madlener, Reinhard, 2015. "Short- and long-run electricity demand elasticities at the subsectoral level: A cointegration analysis for German manufacturing industries," Energy Economics, Elsevier, vol. 48(C), pages 178-187.
    8. Zhang, Jiang & Yu, Tongkui, 2010. "Allometric scaling of countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4887-4896.
    9. Zeyu Zheng & Kazuko Yamasaki & Joel N. Tenenbaum & H. Eugene Stanley, 2012. "Carbon-dioxide emissions trading and hierarchical structure in worldwide finance and commodities markets," Papers 1205.1861, arXiv.org, revised Aug 2013.
    10. Calderon, Cesar & Liu, Lin, 2003. "The direction of causality between financial development and economic growth," Journal of Development Economics, Elsevier, vol. 72(1), pages 321-334, October.
    11. Dias, João, 2012. "Sovereign debt crisis in the European Union: A minimum spanning tree approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 2046-2055.
    12. Zhou, Wei-Xing & Sornette, Didier, 2006. "Non-parametric determination of real-time lag structure between two time series: The "optimal thermal causal path" method with applications to economic data," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 195-224, March.
    13. Zhou, Wei-Xing & Sornette, Didier, 2007. "Lead-lag cross-sectional structure and detection of correlated–anticorrelated regime shifts: Application to the volatilities of inflation and economic growth rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 287-296.
    14. Kantar, Ersin & Deviren, Bayram & Keskin, Mustafa, 2014. "Hierarchical structure of the European countries based on debts as a percentage of GDP during the 2000–2011 period," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 95-107.
    15. Granger, C. W. J., 1980. "Testing for causality : A personal viewpoint," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 329-352, May.
    16. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    17. Ajmi, Ahdi Noomen & Hammoudeh, Shawkat & Nguyen, Duc Khuong & Sato, João Ricardo, 2015. "On the relationships between CO2 emissions, energy consumption and income: The importance of time variation," Energy Economics, Elsevier, vol. 49(C), pages 629-638.
    18. Onnela, J.-P. & Chakraborti, A. & Kaski, K. & Kertész, J., 2003. "Dynamic asset trees and Black Monday," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 247-252.
    19. Yang, Yue & Yang, Huijie, 2008. "Complex network-based time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(5), pages 1381-1386.
    20. Yao, Can-Zhong & Lin, Ji-Nan & Zheng, Xu-Zhou & Liu, Xiao-Feng, 2015. "The study of RMB exchange rate complex networks based on fluctuation mode," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 359-376.
    21. Soytas, Ugur & Sari, Ramazan, 2003. "Energy consumption and GDP: causality relationship in G-7 countries and emerging markets," Energy Economics, Elsevier, vol. 25(1), pages 33-37, January.
    22. Yıldırım, Ertugrul & Sukruoglu, Deniz & Aslan, Alper, 2014. "Energy consumption and economic growth in the next 11 countries: The bootstrapped autoregressive metric causality approach," Energy Economics, Elsevier, vol. 44(C), pages 14-21.
    23. Masih, Abul M. M. & Masih, Rumi, 1996. "Energy consumption, real income and temporal causality: results from a multi-country study based on cointegration and error-correction modelling techniques," Energy Economics, Elsevier, vol. 18(3), pages 165-183, July.
    24. Yao, Can-Zhong & Lin, Ji-Nan & Liu, Xiao-Feng, 2016. "A study of hierarchical structure on South China industrial electricity-consumption correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 129-145.
    25. Mohammadi, Hassan & Parvaresh, Shahrokh, 2014. "Energy consumption and output: Evidence from a panel of 14 oil-exporting countries," Energy Economics, Elsevier, vol. 41(C), pages 41-46.
    26. Wolde-Rufael, Yemane, 2014. "Electricity consumption and economic growth in transition countries: A revisit using bootstrap panel Granger causality analysis," Energy Economics, Elsevier, vol. 44(C), pages 325-330.
    27. Altinay, Galip & Karagol, Erdal, 2004. "Structural break, unit root, and the causality between energy consumption and GDP in Turkey," Energy Economics, Elsevier, vol. 26(6), pages 985-994, November.
    28. Zhang, Xin & Podobnik, Boris & Kenett, Dror Y. & Eugene Stanley, H., 2014. "Systemic risk and causality dynamics of the world international shipping market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 43-53.
    29. Trancoso, Tiago, 2014. "Emerging markets in the global economic network: Real(ly) decoupling?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 499-510.
    30. Castagneto-Gissey, G. & Chavez, M. & De Vico Fallani, F., 2014. "Dynamic Granger-causal networks of electricity spot prices: A novel approach to market integration," Energy Economics, Elsevier, vol. 44(C), pages 422-432.
    31. Giorgio Castagneto-Gissey & Mario Chavez & Fabrizio de Vico Fallani, 2014. "Dynamic Granger-causal networks of electricity spot prices: A novel approach to market integration," Post-Print hal-01023418, HAL.
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