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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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    Cited by:

    1. Bian, Zhicun & Ma, Jun & Ni, Jinlan & Stewart, Shamar, 2020. "Synchronization of regional growth dynamics in China," China Economic Review, Elsevier, vol. 61(C).
    2. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
    3. 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.
    4. 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).
    5. 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.
    6. Gatfaoui, Jamel & Girardin, Eric, 2015. "Comovement of Chinese provincial business cycles," Economic Modelling, Elsevier, vol. 44(C), pages 294-306.
    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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