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

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Listed:
  • Burgard Jan Pablo
  • Münnich Ralf
  • 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.

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 1-23, December.
    • Handle: RePEc:vrs:offsta:v:30:y:2014:i:4:p:23:n:9
    DOI: 10.2478/jos-2014-0046
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    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.
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