A Framework for Producing Small Area Estimates Based on Area-Level Models in R

Abstract:

The R package emdi facilitates the estimation of regionally disaggregated indicators using small area estimation methods and provides tools for model building, diagnostics, presenting, and exporting the results. The package version 1.1.7 includes unit-level small area models that rely on access to micro data. The area-level model by and various extensions have been added to the package since the release of version 2.0.0. These extensions include (a) area-level models with back-transformations, (b) spatial and robust extensions, (c) adjusted variance estimation methods, and (d) area-level models that account for measurement errors. Corresponding mean squared error estimators are implemented for assessing the uncertainty. User-friendly tools like a stepwise variable selection, model diagnostics, benchmarking options, high quality maps and results exportation options enable a complete analysis procedure. The functionality of the package is illustrated by examples based on synthetic data for Austrian districts.

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Published

Sept. 24, 2023

Received

Sep 13, 2022

DOI

10.32614/RJ-2023-039

Volume

Pages

15/1

316 - 341


Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-039.zip

Footnotes

    References

    R. E. Fay and R. A. Herriot. Estimates of income for small places: An application of James-Stein procedures to census data. Journal of the American Statistical Association, 74(366): 269–277, 1979. URL https://doi.org/10.1080/01621459.1979.10482505.

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    Citation

    For attribution, please cite this work as

    Harmening, et al., "A Framework for Producing Small Area Estimates Based on Area-Level Models in R", The R Journal, 2023

    BibTeX citation

    @article{RJ-2023-039,
      author = {Harmening, Sylvia and Kreutzmann, Ann-Kristin and Schmidt, Sören and Salvati, Nicola and Schmid, Timo},
      title = {A Framework for Producing Small Area Estimates Based on Area-Level Models in R},
      journal = {The R Journal},
      year = {2023},
      note = {https://doi.org/10.32614/RJ-2023-039},
      doi = {10.32614/RJ-2023-039},
      volume = {15},
      issue = {1},
      issn = {2073-4859},
      pages = {316-341}
    }