An empirical study of hierarchical Bayes small area estimators using different priors for model variances

Abstract

In this paper, we study hierarchical Bayes (HB) estimators based on different priors for small area estimation. In particular, we use inverse gamma and flat priors for variance components in the HB small area models of You and Chapman (2006) and You (2021). We evaluate the HB estimators through a simulation study and real data analysis. Our results indicate that using the inverse gamma prior for the variance components in the HB models can be very effective.