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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.

Suggested Citation

  • Wang, Jin-ming & Gao, Tie-mei & McNown, Robert, 2009. "Measuring Chinese business cycles with dynamic factor models," Journal of Asian Economics, Elsevier, vol. 20(2), pages 89-97, March.
    • Handle: RePEc:eee:asieco:v:20:y:2009:i:2:p:89-97
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    References listed on IDEAS

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    1. Sichel, Daniel E, 1989. "Are Business Cycles Asymmetric? A Correction," Journal of Political Economy, University of Chicago Press, vol. 97(5), pages 1255-1260, October.
    2. 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 357-384, March.
    3. 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, September.
    4. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
    5. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
    6. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    7. 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.
    8. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, March.
    9. 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 969-996, November.
    10. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    11. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    12. 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 307-328, April.
    13. Ploberger, Werner & Kramer, Walter & Kontrus, Karl, 1989. "A new test for structural stability in the linear regression model," Journal of Econometrics, Elsevier, vol. 40(2), pages 307-318, February.
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    2. Shirly Siew-Ling WONG & Chin-Hong PUAH & Shazali ABU MANSOR & Venus Khim-Sen LIEW, 2016. "Measuring Business Cycle Fluctuations: An Alternative Precursor To Economic Crises," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 235-248.
    3. Zhang, Wei & He, Jie & Ge, Chanyuan & Xue, Rui, 2022. "Real-time macroeconomic monitoring using mixed frequency data: Evidence from China," Economic Modelling, Elsevier, vol. 117(C).
    4. Gatfaoui, Jamel & Girardin, Eric, 2015. "Comovement of Chinese provincial business cycles," Economic Modelling, Elsevier, vol. 44(C), pages 294-306.
    5. Bian, Zhicun & Ma, Jun & Ni, Jinlan & Stewart, Shamar, 2020. "Synchronization of regional growth dynamics in China," China Economic Review, Elsevier, vol. 61(C).
    6. Wang, Xiaoyu & Sun, Yanlin & Peng, Bin, 2023. "Industrial linkage and clustered regional business cycles in China," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 59-72.
    7. Laurenceson, James & Rodgers, Danielle, 2010. "China's macroeconomic volatility -- How important is the business cycle?," China Economic Review, Elsevier, vol. 21(2), pages 324-333, June.

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