IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v30y2014i4p23n9.html
   My bibliography  Save this article

The Impact of Sampling Designs on Small Area Estimates for Business Data

Author

Listed:
  • Burgard Jan Pablo

    (University of Trier – Fachbereich IV, Lehrstuhl für Wirtschafts- und Sozialstatistik, Universitätsring 15 Trier D-54286, Germany.)

  • Münnich Ralf

    (University of Trier – Fachbereich IV, Lehrstuhl für Wirtschafts- und Sozialstatistik, Universitätsring 15 Trier D-54286, Germany.)

  • Zimmermann Thomas

    (University of Trier – Fachbereich IV, Lehrstuhl für Wirtschafts- und Sozialstatistik, Universitätsring 15 Trier D-54286, Germany.)

Abstract

Evidence-based policy making and economic decision making rely on accurate business information on a national level and increasingly also on smaller regions and business classes. In general, traditional design-based methods suffer from low accuracy in the case of very small sample sizes in certain subgroups, whereas model-based methods, such as small area techniques, heavily rely on strong statistical models.In small area applications in business statistics, two major issues may occur. First, in many countries business registers do not deliver strong auxiliary information for adequate model building. Second, sampling designs in business surveys are generally nonignorable and contain a large variation of survey weights.The present study focuses on the performance of small area point and accuracy estimates of business statistics under different sampling designs. Different strategies of including sampling design information in the models are discussed. A design-based Monte Carlo simulation study unveils the impact of the variability of design weights and different levels of aggregation on model- versus design-based estimation methods. This study is based on a close to reality data set generated from Italian business data.

Suggested Citation

  • Burgard Jan Pablo & Münnich Ralf & Zimmermann Thomas, 2014. "The Impact of Sampling Designs on Small Area Estimates for Business Data," Journal of Official Statistics, Sciendo, vol. 30(4), pages 749-771, December.
  • Handle: RePEc:vrs:offsta:v:30:y:2014:i:4:p:23:n:9
    DOI: 10.2478/jos-2014-0046
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/jos-2014-0046
    Download Restriction: no

    File URL: https://libkey.io/10.2478/jos-2014-0046?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
    ---><---

    References listed on IDEAS

    as
    1. Pfeffermann, Danny & Sverchkov, Michail, 2007. "Small-Area Estimation Under Informative Probability Sampling of Areas and Within the Selected Areas," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1427-1439, December.
    Full references (including those not matched with items on IDEAS)

    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. J. N. K. Rao, 2015. "Inferential issues in model-based small area estimation: some new developments," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 491-510, December.
    2. repec:csb:stintr:v:17:y:2016:i:1:p:133-154 is not listed on IDEAS
    3. Michael A. Hidiroglou & Victor M. Estevao, 2016. "A Comparison Of Small Area And Calibration Estimators Via Simulation," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 133-154, March.
    4. Agne Bikauskaite & Isabel Molina & Domingo Morales, 2022. "Multivariate mixture model for small area estimation of poverty indicators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 724-755, December.
    5. Guadarrama, María & Molina, Isabel & Rao, J.N.K., 2018. "Small area estimation of general parameters under complex sampling designs," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 20-40.
    6. J. N. K. Rao, 2021. "On Making Valid Inferences by Integrating Data from Surveys and Other Sources," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 242-272, May.
    7. Hidiroglou M. A. & Estevao V. M., 2016. "A Comparison of Small Area and Calibration Estimators Via Simulation," Statistics in Transition New Series, Statistics Poland, vol. 17(1), pages 133-154, March.
    8. Zimmermann Thomas & Münnich Ralf Thomas, 2018. "Small Area Estimation with a Lognormal Mixed Model under Informative Sampling," Journal of Official Statistics, Sciendo, vol. 34(2), pages 523-542, June.
    9. Torabi, Mahmoud & Rao, J.N.K., 2014. "On small area estimation under a sub-area level model," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 36-55.
    10. Feng Wang & HaiYing Wang & Jun Yan, 2023. "Diagnostic Tests for the Necessity of Weight in Regression With Survey Data," International Statistical Review, International Statistical Institute, vol. 91(1), pages 55-71, April.
    11. J. N. K. Rao, 2015. "Inferential Issues In Model-Based Small Area Estimation: Some New Developments," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 491-510, December.
    12. Isabel Molina & Malay Ghosh, 2021. "Accounting for dependent informative sampling in model-based finite population inference," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 179-197, March.
    13. Rao J. N. K., 2015. "Inferential Issues in Model-Based Small Area Estimation: Some New Developments," Statistics in Transition New Series, Statistics Poland, vol. 16(4), pages 491-510, December.
    14. Ralf Münnich & Jan Burgard & Martin Vogt, 2013. "Small Area-Statistik: Methoden und Anwendungen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 149-191, March.
    15. Berg Emily, 2022. "Construction of Databases for Small Area Estimation," Journal of Official Statistics, Sciendo, vol. 38(3), pages 673-708, September.
    16. Maciej Beręsewicz & Dagmara Nikulin, 2018. "Informal employment in Poland: an empirical spatial analysis," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(3), pages 338-355, July.
    17. Isabel Molina & Paul Corral & Minh Nguyen, 2022. "Estimation of poverty and inequality in small areas: review and discussion," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1143-1166, December.
    18. Abhishek Singh & Ashish Kumar Upadhyay & Kaushalendra Kumar & Ashish Singh & Fiifi Amoako Johnson & Sabu S. Padmadas, 2022. "Spatial heterogeneity in son preference across India’s 640 districts: An application of small-area estimation," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(26), pages 793-842.
    19. Alexander Sun & Paul A. Parker & Scott H. Holan, 2022. "Analysis of Household Pulse Survey Public-Use Microdata via Unit-Level Models for Informative Sampling," Stats, MDPI, vol. 5(1), pages 1-15, February.
    20. Monique Graf & J. Miguel Marín & Isabel Molina, 2019. "A generalized mixed model for skewed distributions applied to small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 565-597, June.
    21. Bor, Jacob & Venkataramani, Atheendar & Williams, David & Tsai, Alexander, 2024. "Reply to McCrain, Adams, Nix, and Del Pozo (2024), "Reconsidering a prominent finding on the spillover effects of police killings of unarmed Black Americans"," SocArXiv 6hx5p, Center for Open Science.

    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:vrs:offsta:v:30:y:2014:i:4:p:23:n:9. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.