IDEAS home Printed from https://ideas.repec.org/p/rba/rbardp/rdp2015-13.html
   My bibliography  Save this paper

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

    Download full text from publisher

    File URL: https://www.rba.gov.au/publications/rdp/2015/pdf/rdp2015-13.pdf
    Download Restriction: no
    ---><---

    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. 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.
    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. repec:zbw:bofitp:2012_014 is not listed on IDEAS
    6. 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.
    7. Saijo, Hikaru, 2013. "Estimating DSGE models using seasonally adjusted and unadjusted data," Journal of Econometrics, Elsevier, vol. 173(1), pages 22-35.
    8. repec:zbw:bofitp:2011_027 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. INOUE Tomoo & OKIMOTO Tatsuyoshi, 2017. "Measuring the Effects of Commodity Price Shocks on Asian Economies," Discussion papers 17009, Research Institute of Economy, Trade and Industry (RIETI).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Buono, Dario & Alpay, Kocak, 2010. "Backward recalculation of seasonal series affected by economic crisis: a Model-Based-Link method for the case of Turkish GDP," MPRA Paper 40092, University Library of Munich, Germany.
    2. Barnett, William A. & de Peretti, Philippe, 2009. "Admissible Clustering Of Aggregator Components: A Necessary And Sufficient Stochastic Seminonparametric Test For Weak Separability," Macroeconomic Dynamics, Cambridge University Press, vol. 13(S2), pages 317-334, September.
    3. Wildi, Marc & McElroy, Tucker S., 2019. "The trilemma between accuracy, timeliness and smoothness in real-time signal extraction," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1072-1084.
    4. Tommaso Proietti, 2012. "Seasonality, Forecast Extensions And Business Cycle Uncertainty," Journal of Economic Surveys, Wiley Blackwell, vol. 26(4), pages 555-569, September.
    5. Yorghos Tripodis & Jeremy Penzer, 2009. "Modelling time series with season-dependent autocorrelation structure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 559-574.
    6. Kaiser, Regina & Maravall, Agustin, 2005. "Combining filter design with model-based filtering (with an application to business-cycle estimation)," International Journal of Forecasting, Elsevier, vol. 21(4), pages 691-710.
    7. Diego Bodas & Juan Ramon Garcia & Juan Murillo & Matias Pacce & Tomasa Rodrigo & Juan de Dios Romero & Pep Ruiz & Camilo Ulloa & Heribert Valero, 2018. "Measuring Retail Trade Using Card Transactional Data," Working Papers 18/03, BBVA Bank, Economic Research Department.
    8. 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.
    9. M. Angeles Carnero & Siem Jan Koopman & Marius Ooms, 2003. "Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 03-071/4, Tinbergen Institute.
    10. Edward E. Leamer, 2011. "Workday, Holiday and Calendar Adjustment with 21st Century Data: Monthly Aggregates from Daily Diesel Fuel Purchases," NBER Working Papers 16897, National Bureau of Economic Research, Inc.
    11. Ghysels, E., 1993. "A Time Series Model with Periodic Stochastic Regime Switching," Cahiers de recherche 9314, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    12. Kubota, Keiichi & Tokunaga, Toshifumi & Wada, Kenji, 2008. "Consumption behavior, asset returns, and risk aversion: Evidence from the Japanese household survey," Japan and the World Economy, Elsevier, vol. 20(1), pages 1-18, January.
    13. Schneider, Nicolas & Strielkowski, Wadim, 2023. "Modelling the unit root properties of electricity data—A general note on time-domain applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    14. Thury, Gerhard & Witt, Stephen F., 1998. "Forecasting industrial production using structural time series models," Omega, Elsevier, vol. 26(6), pages 751-767, December.
    15. Abdur Chowdhury, 1995. "The demand for money in a small open economy: The case of Switzerland," Open Economies Review, Springer, vol. 6(2), pages 131-144, April.
    16. Kaiser Remiro, Regina & Maravall, Agustín, 1999. "Short-term and long-term trends, seasonal and the business cycle," DES - Working Papers. Statistics and Econometrics. WS 6291, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Susi Gorbey & Doug James & Jacques Poot, 1999. "Population Forecasting with Endogenous Migration: An Application to Trans-Tasman Migration," International Regional Science Review, , vol. 22(1), pages 69-101, April.
    18. Maravall, Agustin, 2006. "An application of the TRAMO-SEATS automatic procedure; direct versus indirect adjustment," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2167-2190, May.
    19. Rudrani Bhattacharya, 2008. "Early Warnings of Inflation in India," Working Papers id:1682, eSocialSciences.
    20. Silhan, Peter A., 2014. "Income smoothing from a Census X-12 perspective," Advances in accounting, Elsevier, vol. 30(1), pages 106-115.

    More about this item

    Keywords

    seasonal adjustment; moving holidays; calendar effects; China; X-12-ARIMA; SEATS;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rba:rbardp:rdp2015-13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Paula Drew (email available below). General contact details of provider: https://edirc.repec.org/data/rbagvau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.