An empirical comparison of various MSPE estimators and associated prediction intervals for small area means

Abstract

Small area estimation is a method for prediction involving subpopulation of interest. A standard measure of uncertainty for the prediction is the mean squared prediction error (MSPE). Various predictors and MSPE estimators have been proposed based on different principles over past decades. Despite various existing studies on performance of individual MSPE estimators, a comprehensive comparison of different MSPE estimators in terms of finite sample performance, especially for several recently proposed MSPE estimators, has not been documented. To this end, we carry out a study that systematically compare the existing MSPE estimators regarding their finite sample performance and computational speed. Performance of prediction intervals is also considered, which involves both the predictor and the associated MSPE estimator. The study covers two main types of small area models, namely, the area-level (Fay-Herriot) model and the unit-level (nested-error regression) model. Illustrative examples are provided.