IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v32y2023i5d10.1007_s10260-023-00700-6.html
   My bibliography  Save this article

Three-fold Fay–Herriot model for small area estimation and its diagnostics

Author

Listed:
  • Laura Marcis

    (Department Economics and Law, University of Cassino and Southern Lazio, Campus Folcara)

  • Domingo Morales

    (University “Miguel Hernández de Elche”)

  • Maria Chiara Pagliarella

    (Department Economics and Law, University of Cassino and Southern Lazio, Campus Folcara)

  • Renato Salvatore

    (Department Economics and Law, University of Cassino and Southern Lazio, Campus Folcara)

Abstract

This paper introduces a three-fold Fay–Herriot model with random effects at three hierarchical levels. Small area best linear unbiased predictors of linear indicators are derived from the new model and the corresponding mean squared errors are approximated and estimated analytically and by parametric bootstrap. The problem of influence analysis and model diagnostics is addressed by introducing measures adapted to small area estimation. Simulation experiments empirically investigate the behavior of the predictors and mean squared error estimators. The new statistical methodology is applied to Spanish living conditions survey of 2004–2008. The target is the estimation of proportions of women and men under the poverty line by province and year.

Suggested Citation

  • Laura Marcis & Domingo Morales & Maria Chiara Pagliarella & Renato Salvatore, 2023. "Three-fold Fay–Herriot model for small area estimation and its diagnostics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1563-1609, December.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:5:d:10.1007_s10260-023-00700-6
    DOI: 10.1007/s10260-023-00700-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-023-00700-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-023-00700-6?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.

    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:spr:stmapp:v:32:y:2023:i:5:d:10.1007_s10260-023-00700-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.