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Measuring Chinese business cycles with dynamic factor models

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  • Wang, Jin-ming
  • Gao, Tie-mei
  • McNown, Robert

Abstract

The Stock-Watson method and the dynamic Markov switching factor (DMSF) model are employed to construct macroeconomic composite coincident indexes for the Chinese economy, January 1990-March 2008. Four coincident indicators, namely, industrial production, investment in fixed assets, sales revenues, and the money supply, M1, are selected to compute the coincident index. Strong asymmetries are found with recent business cycles in China characterized by expansions of longer duration and smaller amplitude relative to the contraction stage. The two models produce similar composite index series, but the DMSF model shows frequent transitions that are difficult to interpret. A comparison of the composite coincident index and other measures of macroeconomic activity provides economic interpretations of the patterns in the index. There are notable differences between the index and GDP growth rates over this period, reflecting its more comprehensive measurement of economic activity. This more comprehensive view of macroeconomic activity increases understanding of changes in China's policies and economic fluctuations that are not shown by GDP growth rates alone.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Asian Economics.

Volume (Year): 20 (2009)
Issue (Month): 2 (March)
Pages: 89-97

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Handle: RePEc:eee:asieco:v:20:y:2009:i:2:p:89-97

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Web page: http://www.elsevier.com/locate/asieco

Related research

Keywords: Business cycles Composite coincident index State space model Markov switching model Dynamic factor model;

References

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  1. Sylvia Kaufmann, 2000. "Measuring business cycles with a dynamic Markov switching factor model: an assessment using Bayesian simulation methods," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 39-65.
  2. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-96, November.
  3. Kim, C-J., 1991. "Dynamic Linear Models with Markov-Switching," Papers 91-8, York (Canada) - Department of Economics.
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  7. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  8. James H. Stock & Mark W. Watson, 2005. "Understanding Changes In International Business Cycle Dynamics," Journal of the European Economic Association, MIT Press, vol. 3(5), pages 968-1006, 09.
  9. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-28, April.
  10. Sichel, Daniel E, 1989. "Are Business Cycles Asymmetric? A Correction," Journal of Political Economy, University of Chicago Press, vol. 97(5), pages 1255-60, October.
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