University of Bamberg
open search form
You are here

Faculty of Social Sciences, Economics, and Business Administration

Chair of Statistics and Econometrics

  1.  University of bamberg
  2. Faculties
  3. Social Sciences, Economics and Business Administration
  4. Subjects and Institutes
  5. Institute for Statistics
  6. Lehrstühle & Professuren
  7. Chair of Statistics and Econometrics
  8. News
  9. New Article in R Journal

New Article in R Journal

Silvia Harmening, Timo Schmid, and colleagues publish software for the application of aggregated small area methods.

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

Harmening, S.; Kreutzmann, A.-K..; Schmidt, S.; Salvati, N.; Schmid, T.

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 Fay and Herriot (1979) 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.

Silvia Harmening, Ann-Kristin Kreutzmann, Sören Schmidt, Nicola Salvati & Timo Schmid (2023) A Framework for Producing Small Area Estimates Based on Area-Level Models in R, The R Journal, DOI: https://doi.org/10.32614/RJ-2023-039

Related News