Research on spatial economic structure for different economic sectors from a perspective of a complex network
The economy system is a complex system, and the complex network is a powerful tool to study its complexity. Here we calculate the economic distance matrices based on annual GDP of nine economic sectors from 1995–2010 in 31 Chinese provinces and autonomous regions,11In this paper, we just study the economy structure in Chinese mainland, and Taiwan, Hong Kong and Macao are not involved. In the following parts, we use ‘region’ to represent a province or autonomous region. The relevant economic data contains the annual GDP of nine economic sectors, and are downloaded from http://188.8.131.52/welcome.do, and we introduce the 31 regions in Appendix A simply. then build several spatial economic networks through the threshold method and the Minimal Spanning Tree method. After the analysis on the structure of the networks and the influence of geographic distance, some conclusions are drawn. First, connectivity distribution of a spatial economic network does not follow the power law. Second, according to the network structure, nine economic sectors could be divided into two groups, and there is significant discrepancy of network structure between these two groups. Moreover, the influence of the geographic distance plays an important role on the structure of a spatial economic network, network parameters are changed with the influence of the geographic distance. At last, 2000 km is the critical value for geographic distance: for real estate and finance, the spearman’s rho with l<2000 is bigger than that with l>2000, and the case is opposite for other economic sectors.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 392 (2013)
Issue (Month): 17 ()
|Contact details of provider:|| Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Heimo, Tapio & Kaski, Kimmo & Saramäki, Jari, 2009. "Maximal spanning trees, asset graphs and random matrix denoising in the analysis of dynamics of financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(2), pages 145-156.
- 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.
- 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.
- Rosario N. Mantegna, 1998. "Hierarchical Structure in Financial Markets," Papers cond-mat/9802256, arXiv.org.
- Dong-Ming Song & Michele Tumminello & Wei-Xing Zhou & Rosario N. Mantegna, 2011. "Evolution of worldwide stock markets, correlation structure and correlation based graphs," Papers 1103.5555, arXiv.org.
- Lee, Junghoon & Youn, Janghyuk & Chang, Woojin, 2012. "Intraday volatility and network topological properties in the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1354-1360.
- Leonidas Sandoval Junior & Italo De Paula Franca, 2011. "Correlation of financial markets in times of crisis," Papers 1102.1339, arXiv.org, revised Mar 2011.
- Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
- Namaki, A. & Shirazi, A.H. & Raei, R. & Jafari, G.R., 2011. "Network analysis of a financial market based on genuine correlation and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3835-3841.
- Tian Qiu & Bo Zheng & Guang Chen, 2010. "Adaptive financial networks with static and dynamic thresholds," Papers 1002.3432, arXiv.org.
- Eom, Cheoljun & Oh, Gabjin & Jung, Woo-Sung & Jeong, Hawoong & Kim, Seunghwan, 2009. "Topological properties of stock networks based on minimal spanning tree and random matrix theory in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(6), pages 900-906. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:392:y:2013:i:17:p:3682-3697. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.