IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v179y2016i2p377-402.html
   My bibliography  Save this article

Multivariate state space approach to variance reduction in series with level and variance breaks due to survey redesigns

Author

Listed:
  • Oksana Bollineni-Balabay
  • Jan Brakel
  • Franz Palm

Abstract

type="main" xml:id="rssa12117-abs-0001"> Statistics Netherlands applies a design-based estimation procedure to produce road transportation figures. Frequent survey redesigns caused discontinuities in these series which obstruct the comparability of figures over time. Reductions in the sample size and changes in the sample design resulted in variance breaks and unacceptably large sampling errors in the recent part of the series. Both problems are addressed and solved simultaneously. Discontinuities and small sample sizes are accounted for by using a multivariate structural time series model that borrows strength over time and space. The paper illustrates an increased precision when we move from univariate models to a multivariate model where the domains are jointly modelled. This increase is especially significant in the most recent period when sample sizes become smaller, with standard errors of the design-based estimator of the target variables being reduced by 40 –70 % with the model-based approach.

Suggested Citation

  • Oksana Bollineni-Balabay & Jan Brakel & Franz Palm, 2016. "Multivariate state space approach to variance reduction in series with level and variance breaks due to survey redesigns," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 377-402, February.
    • Handle: RePEc:bla:jorssa:v:179:y:2016:i:2:p:377-402
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssa.2016.179.issue-2
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Smith Paul A. & James Gareth G., 2017. "Changing Industrial Classification to SIC (2007) at the UK Office for National Statistics," Journal of Official Statistics, Sciendo, vol. 33(1), pages 223-247, March.
    2. Jan A. Brakel & Sabine Krieg, 2016. "Small area estimation with state space common factor models for rotating panels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 763-791, June.
    3. Oksana Bollineni‐Balabay & Jan van den Brakel & Franz Palm & Harm Jan Boonstra, 2017. "Multilevel hierarchical Bayesian versus state space approach in time series small area estimation: the Dutch Travel Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1281-1308, October.

    More about this item

    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:bla:jorssa:v:179:y:2016:i:2:p:377-402. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.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.