Abstract
Small Area Estimation (SAE) is a statistical technique for estimating the parameters of a sub-population with a small sample size. SAE aims to improve the accuracy of parameter estimation, with indirect estimation. This study aims to determine the best method between empirical best linear unbiased prediction (EBLUP) and spatial EBLUP methods (with a queen contiguity weighted matrix) in estimating per capita expenditure per sub-district in Bali. The results of this study indicate that the best SAE method in estimating per capita expenditure per sub-district in Bali is the EBLUP method with the smaller mean squared error. The EBLUP estimation results are significantly influenced by three variables, namely the population, public primary schools, and families using PLN. The sub-district with the highest per capita expenditure in Bali is Denpasar Selatan sub-district. Meanwhile, the sub-district with the lowest per capita expenditure was Abang sub-district. Since the EBLUP model is better than SEBLUP model, this indicates that per capita expenditure per sub-district in Bali is not influenced by its neighbors.
Highlights
Survei sosial ekonomi nasional (SUSENAS) conducted by Badan Pusat Statistik (BPS) is a survey activity to collect information/data on population, health, education, family planning, housing, consumption, and expenditure
The response variable used in this study is per capita expenditure per sub-district which comes from Bali dalam Angka 2018
Based on the results of this study, per capita expenditure per sub-district in Bali, both the empirical best linear unbiased prediction (EBLUP) and spatial EBLUP method (SEBLUP) methods, is significant according to population, number of public elementary schools, and number of families using PLN
Summary
Survei sosial ekonomi nasional (SUSENAS) conducted by Badan Pusat Statistik (BPS) is a survey activity to collect information/data on population, health, education, family planning, housing, consumption, and expenditure. This survey is designed to have three datasets (modules) which held every three years. According to BPS (2019), measurement of poverty is carried out using the concept of the ability to meet basic needs. The sample system in a population survey in a small area causes a limited number of survey objects. In order to produce better predictions, the indirect estimation method can be used in a small area (Rao, 2003)
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