Hierarchical bayes estimation using time series and cross-sectional data: A case of per-capita expenditure in Indonesia

Abstract

In Indonesia, there is a growing demand for reliable small area statistics in order to assess or to put into policies and programs. Sample survey data provide effective reliable estimators of totals and means for large area and domains. But it is recognized that the usual direct survey estimator performing statistics for a small area, have unacceptably large standard errors, due to the circumstance of small sample size in the area. The primary source of data for this paper is the National Socio-economic Survey (Susenas), a survey which is conducted every year in Indonesia. However, the estimation of Susenas village per-capita expenditure is unreliable, due to the limited number of observations per village. Hence, it is important to improve the estimates. We proposed a hierarchical Bayes (HB) method using a time series generalization of a widely used cross-sectional model in small area estimation. Generalized variance function (GVF) is used to obtain the estimates of sampling variance.