Spatial Prediction in Small Area Estimation

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.