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Complex scenarios in socio-economic data: a comprehensive analytical study

In: Handbook of Research Methods in Complexity Science

Author

Listed:
  • Assistant Professor Sanjay Kumar Palit
  • Associate Professor Santo Banerjee
  • Assisyant Professor Sayan Mukherjee

Abstract

The socio-economic scenario of a country reflects its social, economic, political, ideological, ethical, cultural, or communicative habits, making its proper analysis for different countries quite challenging. Complexity science has provided some new methods and tools for dealing with this challenge. Country-level Gross Domestic Product (GDP) and population are the two most important issues in the socio-economic context. In order to show the effectiveness of different nonlinear tools in analysing socio-economic data, the authors implemented three popular nonlinear tools: recurrence rate, mean conditional recurrence (MCR) and complex networks (CN) to analyse country level GDP and population data to validate the derived results with the standard conclusions based on general theories of economics. recurrence rate is used to show how two non-identical systems get synchronized through their phase spaces. MCR detects the driver and response system in synchronized states and CN reflects the overall scenarios of the complex systems by its various statistical measures.

Suggested Citation

  • Assistant Professor Sanjay Kumar Palit & Associate Professor Santo Banerjee & Assisyant Professor Sayan Mukherjee, 2018. "Complex scenarios in socio-economic data: a comprehensive analytical study," Chapters, in: Eve Mitleton-Kelly & Alexandros Paraskevas & Christopher Day (ed.), Handbook of Research Methods in Complexity Science, chapter 19, pages 388-425, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:16937_19
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