Regional Clustering and Synchronization of Provincial Business Fluctuations in China
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’.
Keywordsregional division business cycle synchronization multilevel dynamic factor model variance decomposition
Unable to display preview. Download preview PDF.
- Francis N, Owyang M T, Savascin Ö, 2012. An endogenously clustered factor approach to international business cycles. Federal Reserve Bank of St. Louis Working Paper. St. Louis: Federal Reserve Bank.Google Scholar
- Guo Q W, Jia J X, 2005. Dynamic factor analysis of provincial business cycles in China. Management World, (11): 50–58. (in Chinese)Google Scholar
- Guo Q W, Zhao X J, 2012. Local governmental investment competition and business cycle fluctuation. The Journal of World Economy, (5): 3–21. (in Chinese)Google Scholar
- Huang J L, Li K W, Li D F, 2011. The synchronization of regional real business cycle in China. The Journal of World Economy, (9): 19–41. (in Chinese)Google Scholar
- Kim C J, Nelson C R, 1999. State-Space Models With Regime Switching: Classical and Gibbs-Sampling Approaches with Applications. Cambridge, MA: MIT Press.Google Scholar
- Li Y, Zeng T, Yu J, 2013. Robust deviance information criterion for latent variable models. CAFE Research Paper No. 13.19. doi: 10.2139/ssrn.2316341Google Scholar
- Poncet S, 2004. Are Chinese provinces forming an optimal currency area? Magnitude and Determinants of Business Cycles within China. Working Paper, 2004.Google Scholar
- Stock J H, Watson M W, 2010. The evolution of national and regional factors in US housing construction. In: Bollerslev T, Russell J, and Watson M (eds). Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle. Oxford: Oxford University Press, 35–61. doi: 10.1093/acprof:oso/9780 199549498.001.0001CrossRefGoogle Scholar
- Zhang W B, Tong D, 2011. Provincial industrial business cycles in China. The Journal of Quantitative amp; Technical Economics, 28(1): 104–116. (in Chinese)Google Scholar
- Zheng T G, Wang X, 2013. Measuring China’s business cycle with mixed-frequency data and its real time analysis. Economic Research Journal, 48(6): 58–70. (in Chinese)Google Scholar
- Zheng T G, Xia K, 2016. Macroeconomic data releasing and methodology research on measuring China’s business cycle in the real-time. Systems Engineering-Theory amp; Practice, 37(4): 817–830. (in Chinese)Google Scholar