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Using Small Area Estimation to Produce Official Statistics

Author

Listed:
  • Linda J. Young

    (United States Department of Agriculture, National Agricultural Statistics Service, 1400 Independence Avenue SW, Washington, DC 20250, USA)

  • Lu Chen

    (United States Department of Agriculture, National Agricultural Statistics Service, 1400 Independence Avenue SW, Washington, DC 20250, USA
    National Institute of Statistical Sciences, 1750 K Street NW Suite 1100, Washington, DC 20006, USA)

Abstract

The USDA National Agricultural Statistics Service (NASS) and other federal statistical agencies have used probability-based surveys as the foundation for official statistics for over half a century. Non-survey data that can be used to improve the accuracy and precision of estimates such as administrative, remotely sensed, and retail data have become increasingly available. Both frequentist and Bayesian models are used to combine survey and non-survey data in a principled manner. NASS has recently adopted Bayesian subarea models for three of its national programs: farm labor, crop county estimates, and cash rent county estimates. Each program provides valuable estimates at multiple scales of geography. For each program, technical challenges had to be met and a strenuous review completed before models could be adopted as the foundation for official statistics. Moving models out of the research phase into production required major changes in the production process and a cultural shift. With the implemented models, NASS now has measures of uncertainty, transparency, and reproducibility of its official statistics.

Suggested Citation

  • Linda J. Young & Lu Chen, 2022. "Using Small Area Estimation to Produce Official Statistics," Stats, MDPI, vol. 5(3), pages 1-17, September.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:3:p:51-897:d:910469
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    References listed on IDEAS

    as
    1. Shuchi Goyal & Gauri Sankar Datta & Abhyuday Mandal, 2021. "A Hierarchical Bayes Unit-Level Small Area Estimation Model for Normal Mixture Populations," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 215-241, May.
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    3. Andreea L. Erciulescu & Nathan B. Cruze & Balgobin Nandram, 2019. "Model‐based county level crop estimates incorporating auxiliary sources of information," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(1), pages 283-303, January.
    4. Nandram, Balgobin & Cruze, Nathan B & Erciulescu, Andreea L & Chen, Lu, 2022. "Bayesian Small Area Models under Inequality Constraints with Benchmarking and Double Shrinkage," NASS Research Reports 327250, United States Department of Agriculture, National Agricultural Statistics Service.
    5. Malay Ghosh & Rebecca Steorts, 2013. "Two-stage benchmarking as 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. 22(4), pages 670-687, November.
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    7. Adrijo Chakraborty & Gauri Sankar Datta & Abhyuday Mandal, 2019. "Robust Hierarchical Bayes Small Area Estimation for the Nested Error Linear Regression Model," International Statistical Review, International Statistical Institute, vol. 87(S1), pages 158-176, May.
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    Cited by:

    1. Lu Chen & Luca Sartore & Habtamu Benecha & Valbona Bejleri & Balgobin Nandram, 2022. "Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs," Stats, MDPI, vol. 5(3), pages 1-18, September.

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