Area specific effects selection of small area estimation for construction of regional consumer price indices in Indonesia

Abstract

Small area estimation (SAE) techniques are now widely employed to produce parameter estimates for smaller domains where sample sizes cannot be used to deliver direct estimation, such as regional consumer price indices (CPIs). Area-specific effects have important roles in attaining reliable parameter estimates in SAE. Thus their exclusion and inclusion in the model should be carefully examined to get accurate prediction. In practice, it is common to analyze large scale data in which the normality assumption of SAE models maybe violated due to sparsity of the area-specific effects. In this research, we consider the number of regional baskets of goods and services varies across districts. Hence, the calculation of regional CPIs needs different regional baskets of goods and services. To calculate such indices the normL1 penalty for area-specific effects selection is proposed. The response variables are the CPIs of January 2018 whereas the auxiliary variables are infrastructures and resources which are available in the village potential census in 2018 (PODES 2018). The proposed model fits to predict Regional CPIs by groups of expenditures in the sampled districts based on the empirical relative RMSE and area-specific effect selection performance. Finally, based on the regional CPIs prediction values of all over districts/cities in Indonesia the general CPI of Indonesia during January 2018 can be calculated.