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An assessment on the quality of China’s preliminary data of quarterly GDP announcements

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  • Lixiong Yang

Abstract

This article examines the quality of China’s preliminary announcements of the quarterly GDP. We modify the tests for unbiasedness and efficiency by incorporating the Fourier approximation to capture the effect of the state of the economy, and employing the Kiefer, Vogelsang and Bunzel (KVB) approach, developed by KVB in 2000 to reconstruct the tests to improve the finite sample properties. The results show that: (1) there is no enough evidence to support that the preliminary and first revised data are unbiased and efficient; (2) there exist systematic errors related to the state of the economy, and hence information about the state of the economy was not incorporated into the GDP data. Furthermore, we find that there is a possibility that these systematic errors associated with the stages of the business cycle may offset each other, and there is also a possibility that there exist offsetting errors in the underlying components of GDP.

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  • Lixiong Yang, 2017. "An assessment on the quality of China’s preliminary data of quarterly GDP announcements," Applied Economics, Taylor & Francis Journals, vol. 49(54), pages 5558-5569, November.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:54:p:5558-5569
    DOI: 10.1080/00036846.2017.1313950
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    Cited by:

    1. Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
    2. Sinclair, Tara M., 2019. "Characteristics and implications of Chinese macroeconomic data revisions," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1108-1117.

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