Spatial Prediction in Small Area Estimation

Martin Vogt; Partha Lahiri; Ralf Münnich

doi:https://doi.org/10.59170/stattrans-2023-037

Spatial Prediction in Small Area Estimation

Martin Vogt Trier University of Applied Sciences, Germany ORCID:https://orcid.org/0009-0003-2934-1415 , Partha Lahiri University of Maryland, College Park, MD 20742, USA ORCID:https://orcid.org/0000-0002-7103-545X , Ralf Münnich Economics, Economic and Social Statistics Department, Trier University, Germany ORCID:https://orcid.org/0000-0001-8285-5667 Statistics in Transition new series, vol. 24, 2023, 3, pages: 77-94 Published online: 13 June 2023 https://doi.org/10.59170/stattrans-2023-037 Citation: Vogt, M., Lahiri, P., Münnich, r., 2023. Spatial Prediction in Small Area Estimation. Statistics in Transition new series, 24(3), pp. 77-94. https://doi.org/10.59170/stattrans-2023-037

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ABSTRACT

Small area estimation methods have become a widely used tool to provide accurate estimates for regional indicators such as poverty measures. Recent research has provided evidence that spatial modelling still can improve the precision of regional and local estimates. In this paper, we provide an intrinsic spatial autocorrelation model and prove the propriety of the posterior under a flat p rior. F urther, we show using the SAIPE poverty data that the gain in efficiency using a spatial model can be essentially important in the presence of a lack of strong auxiliary variables

KEYWORDS

Fay-Herriot, CAR, poverty estimation, spatial models.

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