Comparison of mean squared error estimators under the Fay-Herriot model: application to poverty and percentage of food expenditure data

Abstract

Small area estimates have received much attention from both private and public sectors due to the growing demand for effective planning of health services, apportioning of government funds and policy and decision making. The uncertainty of empirical best linear unbiased predictor (EBLUP) estimates is widely assessed by mean squared error (MSE). MSEs are criticized as they are not area specific since they do not depend on the direct estimators from the survey. In this paper, we compare the performances of different MSE estimators with respect to the relative bias and relative risk using a Monte Carlo simulation study. Simulation results suggest the superiority of the proposed MSEs over the existing methods in some situations. As a case study, the 2010/11 household consumption expenditure survey (HCES) and the 2007 housing and population census of Ethiopia have been used to study the performances of the MSE estimators.