SIMULATION STUDY OF HIERARCHICAL BAYESIAN APPROACH FOR SMALL AREA ESTIMATION WITH MEASUREMENT ERROR

Abstract

In small area estimation (SAE), the auxiliary variables used are commonly derived from registration data such as census and administrative data. It is assumed that the auxiliary variables are available for all areas. The limited availability of auxiliary variables can be an obstacle in SAE. The additional information from the survey can be alternative data, but it is assumed that the auxiliary variables will contain measurement errors. This study conducted a simulation of data that aims to handle when auxiliary variables are measured with errors. Two simulations were studied with some scenarios to the percentage area where the auxiliary variable is measured with error and scenarios to the generated auxiliary variables. Compare four methods: direct estimation, Fay-Herriot Empirical Best Linear Unbiased Prediction (EBLUP-FH), Ybarra-Lohr SAE with measurement error (SaeME), and Hierarchical Bayesian SaeME. The results show that, in both the simulation studies, the Hierarchical Bayesian SaeME method gives a smaller EMSE value than the other two methods when auxiliary information is measured with error.