IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v56y2012i10p2840-2855.html
   My bibliography  Save this article

Small area estimation of poverty proportions under area-level time models

Author

Listed:
  • Esteban, M.D.
  • Morales, D.
  • Pérez, A.
  • Santamaría, L.

Abstract

The unit-level small area estimation approach has no standard procedure and each case needs separate modeling when the domain parameters are not linear or the target variable is not normally distributed. Area-level linear mixed models can be generally applied to produce EBLUP estimates of linear and non linear parameters because direct estimates are weighted sums, so that the assumption of normality may be acceptable. The problem of estimating small area non linear parameters is treated, with special emphasis on the estimation of poverty proportions. Borrowing strength from time by using area-level linear time models is proposed. Four time-dependent area-level models are considered and the behavior of the two basic ones is empirically investigated. The developed model-based methodology for estimating poverty proportions is applied in the Spanish Living Conditions Survey.

Suggested Citation

  • Esteban, M.D. & Morales, D. & Pérez, A. & Santamaría, L., 2012. "Small area estimation of poverty proportions under area-level time models," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2840-2855.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:10:p:2840-2855
    DOI: 10.1016/j.csda.2011.10.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947311003720
    Download Restriction: Full text for ScienceDirect subscribers only.

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

    References listed on IDEAS

    as
    1. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    2. González-Manteiga, W. & Lombardi­a, M.J. & Molina, I. & Morales, D. & Santamari­a, L., 2008. "Analytic and bootstrap approximations of prediction errors under a multivariate Fay-Herriot model," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5242-5252, August.
    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. Domingo Morales, 2014. "Comments on: Single and two-stage cross-sectional and time series benchmarking procedures for small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 674-679, December.
    2. repec:exl:29stat:v:16:y:2015:i:4:p:563-584 is not listed on IDEAS
    3. repec:eee:csdana:v:115:y:2017:i:c:p:53-66 is not listed on IDEAS
    4. Tomasz Ża̧dło, 2015. "On longitudinal moving average model for prediction of subpopulation total," Statistical Papers, Springer, vol. 56(3), pages 749-771, August.
    5. Torabi, Mahmoud & Lele, Subhash R. & Prasad, Narasimha G.N., 2015. "Likelihood inference for small area estimation using data cloning," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 158-171.
    6. William R. Bell & Carolina Franco, 2015. "Borrowing information over time in binomial/logit normal models for small area estimation," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 563-584, December.
    7. Boubeta, Miguel & Lombardía, María José & Morales, Domingo, 2017. "Poisson mixed models for studying the poverty in small areas," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 32-47.
    8. repec:spr:testjl:v:27:y:2018:i:2:d:10.1007_s11749-017-0551-5 is not listed on IDEAS
    9. Marhuenda, Yolanda & Molina, Isabel & Morales, Domingo, 2013. "Small area estimation with spatio-temporal Fay–Herriot models," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 308-325.
    10. Benavent, Roberto & Morales, Domingo, 2016. "Multivariate Fay–Herriot models for small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 372-390.
    11. repec:exl:29stat:v:17:y:2016:i:1:p:105-132 is not listed on IDEAS
    12. repec:csb:stintr:v:17:y:2016:i:1:p:105-132 is not listed on IDEAS
    13. Miguel Boubeta & María José Lombardía & Domingo Morales, 2016. "Empirical best prediction under area-level Poisson mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 548-569, September.
    14. Tamura, Karin Ayumi & Giampaoli, Viviana, 2013. "New prediction method for the mixed logistic model applied in a marketing problem," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 202-216.
    15. Marhuenda, Yolanda & Morales, Domingo & del Carmen Pardo, María, 2014. "Information criteria for Fay–Herriot model selection," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 268-280.

    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:eee:csdana:v:56:y:2012:i:10:p:2840-2855. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/csda .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.