Multivariate spatial hierarchical Bayesian empirical likelihood methods for small area estimation

Abstract

Recent advances in small area estimation incorporating both explicit spatial autocorrelation and empirical likelihood techniques have produced estimates with greater precision. Furthermore, the multivariate Fay–Herriot models take advantage of within‐location correlation between multiple outcomes for a set of small areas. We extend the Fay–Herriot model by utilizing empirical likelihood techniques to the spatially explicit multivariate setting. We then model the five‐year period estimates from the American Community Survey (2006–10) of percent of unemployed individuals and percent of families in poverty for the counties of Missouri. We demonstrate bivariate reduction in leave‐one‐out median absolute deviation over an approximately equivalently specified parametric model. Copyright © 2015 John Wiley & Sons, Ltd.