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Chinese Geographical Science

, Volume 28, Issue 4, pp 571–583 | Cite as

Regional Clustering and Synchronization of Provincial Business Fluctuations in China

  • Tao Song
  • Tingguo Zheng
  • Kai Xia
Article
  • 16 Downloads

Abstract

In this article, we propose a novel, multilevel, dynamic factor model, to determine endogenously clustered regions for the investigation of regional clustering and synchronization of provincial business fluctuations in China. The parameter identification and model estimation was conducted using the Markov Chain Monte Carlo method. We then conducted an empirical study of the provincial business fluctuations in China (31 Chinese provinces are considered except Hong Kong, Macau, and Taiwan due to the data unavailability), which were sampled from January 2000 to December 2015. Our results indicated that these provinces could be clustered into four regions: leading, coincident, lagging, and overshooting. In comparison with traditional geographical divisions, this novel clustering into four regions enabled the regional business cycle synchronization to be more accurately captured. Within the four regional clusters it was possible to identify substantial heterogeneities among regional business cycle fluctuations, especially during the periods of the 2008 financial crisis and the ‘four-trillion economic stimulus plan’.

Keywords

regional division business cycle synchronization multilevel dynamic factor model variance decomposition 

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Copyright information

© Science Press, Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of EconomicsXiamen UniversityXiamenChina
  2. 2.Department of Statistics, School of EconomicsXiamen UniversityXiamenChina
  3. 3.Wang Yanan Institute for Studies in EconomicsXiamen UniversityXiamenChina

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