Application of an Empirical Best Linear Unbiased Prediction Fay–Herriot (EBLUP-FH) Multivariate Method with Cluster Information to Estimate Average Household Expenditure

Abstract

Data at a smaller regional level has now become a necessity for local governments. The average data on household expenditure on food and non-food is designed for provincial and district/city estimation levels. Subdistrict-level statistics are not currently available. Small area estimation (SAE) is one method to address the problem. The Empirical Best Linear Unbiased Prediction (EBLUP)—Fay Herriot Multivariate method estimates the average household expenditure on food and non-food at the sub-district level in Central Java Province in 2020. Meanwhile, for the sub-districts that are not sampled, the estimation of average household expenditure is done by adding cluster information to the EBLUP Multivariate modeling. The K-Medoids Cluster method is used to classify sub-districts based on their characteristics. Small area estimation using the EBLUP-FH Multivariate method can enhance the parameter estimations obtained using the direct estimation method because it results in a lower level of variation (RSE). For sub-districts that are not sampled, the Residual Standard Error (RSE) value from the estimated results using the EBLUP-FH Multivariate method with cluster information is lower than 25%, indicating that the estimate is accurate.