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Seasonal Adjustment of Chinese Economic Statistics

Author

Listed:
  • Ivan Roberts

    (Reserve Bank of Australia)

  • Graham White

    (Reserve Bank of Australia)

Abstract

China's growing importance in the global economy and significance as a source of demand for commodities produced by many countries, including Australia, has focused increasing attention on high-frequency Chinese macroeconomic data. Yet the signal from these data is often distorted by traditional holidays whose timing varies from year to year on the Gregorian calendar. This paper shows that seasonal adjustment procedures (such as the US Census Bureau's X-12-ARIMA and the Bank of Spain's SEATS) can assist in the timely interpretation of a range of commonly used Chinese macroeconomic indicators, including industrial production, trade, credit and inflation. In addition, it suggests a strategy to optimise the selection of moving holiday corrections that account for Chinese New Year, the Dragon Boat festival and the Mid-Autumn festival, prior to seasonal adjustment. It is argued that seasonal adjustment performed with this approach is preferable to simpler techniques.

Suggested Citation

  • Ivan Roberts & Graham White, 2015. "Seasonal Adjustment of Chinese Economic Statistics," RBA Research Discussion Papers rdp2015-13, Reserve Bank of Australia.
    • Handle: RePEc:rba:rbardp:rdp2015-13
    as

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    File URL: https://www.rba.gov.au/publications/rdp/2015/pdf/rdp2015-13.pdf
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    References listed on IDEAS

    as
    1. Cheung, Yin-Wong & Chinn, Menzie D. & Qian, XingWang, 2012. "Are Chinese trade flows different?," Journal of International Money and Finance, Elsevier, vol. 31(8), pages 2127-2146.
    2. Anne-Laure Delatte & Julien Fouquau & Carsten Holz, 2014. "Explaining money demand in China during the transition from a centrally planned to a market-based monetary system," Post-Communist Economies, Taylor & Francis Journals, vol. 26(3), pages 376-400, September.
    3. Fernald, John G. & Spiegel, Mark M. & Swanson, Eric T., 2014. "Monetary policy effectiveness in China: Evidence from a FAVAR model," Journal of International Money and Finance, Elsevier, vol. 49(PA), pages 83-103.
    4. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 291-320, October.
    5. Anne-Laure Delatte & Julien Fouquau & Carsten Holz, 2014. "Explaining money demand in China during the transition from a centrally planned to a market-based monetary system," Post-Communist Economies, Taylor & Francis Journals, vol. 26(3), pages 376-400, September.
    6. Cheung, Yin-Wong & Chinn, Menzie D. & Qian, XingWang, 2012. "Are Chinese trade flows different?," Journal of International Money and Finance, Elsevier, vol. 31(8), pages 2127-2146.
    7. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Time Series: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 343-349, October.
    8. Víctor Gómez & Agustín Maravall, 1998. "Guide for Using the Programs TRAMO and SEATS (Beta Version: December 1997)," Working Papers 9805, Banco de España.
    9. Saijo, Hikaru, 2013. "Estimating DSGE models using seasonally adjusted and unadjusted data," Journal of Econometrics, Elsevier, vol. 173(1), pages 22-35.
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    Cited by:

    1. Barend Abeln & Jan P. A. M. Jacobs, 2023. "CAMPLET: Seasonal Adjustment Without Revisions," SpringerBriefs in Economics, in: Seasonal Adjustment Without Revisions, chapter 0, pages 7-29, Springer.
    2. Higgins, Patrick & Zha, Tao & Zhong, Wenna, 2016. "Forecasting China's economic growth and inflation," China Economic Review, Elsevier, vol. 41(C), pages 46-61.
    3. Tomoo INOUE & Tatsuyoshi OKIMOTO, 2017. "Measuring the Effects of Commodity Price Shocks on Asian Economies," Discussion papers 17009, Research Institute of Economy, Trade and Industry (RIETI).
    4. Meier, Matthias & Pinto, Eugenio, 2024. "COVID-19 Supply Chain Disruptions," European Economic Review, Elsevier, vol. 162(C).

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    Keywords

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    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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