IDEAS home Printed from https://ideas.repec.org/a/oec/stdkaa/5km7v1835wr5.html
   My bibliography  Save this article

Benchmarking Systems of Seasonally Adjusted Time Series

Author

Listed:
  • Tommaso di Fonzo
  • Marco Marini

Abstract

When a system of time series is seasonally adjusted, generally the accounting constraints originally linking the series are not fulfilled. To overcome this problem, we discuss an extension to a system of series linked by an accounting constraint of the classical univariate benchmarking procedure due to Denton (1971), which is founded on a movement preservation principle that is very relevant in this case. The presence of linear dependence between the variables makes it necessary to deal with the whole set of contemporaneous and temporal aggregation relationships. The cases of one-way classified (e.g., by regions or by industries) and of two-way classified (e.g., by regions and by industries) systems of series are studied. An empirical application to the Canadian retail trade series by province (12 series) and trade groups (18 series) is considered to show the capability of the proposed procedures.

Suggested Citation

  • Tommaso di Fonzo & Marco Marini, 2005. "Benchmarking Systems of Seasonally Adjusted Time Series," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 89-123.
    • Handle: RePEc:oec:stdkaa:5km7v1835wr5
    DOI: 10.1787/jbcma-2005-5km7v1835wr5
    as

    Download full text from publisher

    File URL: https://doi.org/10.1787/jbcma-2005-5km7v1835wr5
    Download Restriction: Full text available to READ online. PDF download available to OECD iLibrary subscribers.
    File URL: https://libkey.io/10.1787/jbcma-2005-5km7v1835wr5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Nino Mushkudiani & Jacco Daalmans & Reinier Bikker, 2018. "Solving large‐data consistency problems at Statistics Netherlands using macro‐integration techniques," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 553-573, November.
    2. Baoline Chen & Tommaso Di Fonzo & Thomas Howells & Marco Marini, 2018. "The statistical reconciliation of time series of accounts between two benchmark revisions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 533-552, November.
    3. José Casals & Miguel Jerez & Sonia Sotoca, 2009. "Modelling and forecasting time series sampled at different frequencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 316-342.

    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:oec:stdkaa:5km7v1835wr5. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/oecddfr.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.