Title: Difference Benchmarking for Multivariate Small Area Estimation
Author: Zaza Yuda Perwira, Azka Ubaidillah
Maintainer: Zaza Yuda Perwira <221710086@stis.ac.id>
Description: Implements Benchmarking Method for Multivariate Small Area Estimation under Fay Herriot Model. Multivariate Small Area Estimation (MSAE) is a development of Univariate Small Area Estimation that considering the correlation among response variables and borrowing the strength from auxiliary variables effectiveness of a domain sample size, the multivariate model in this package is based on multivariate model 1 proposed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>. Benchmarking in Small Area Estimation is a modification of Small Area Estimation model to guarantees that the aggregate weighted mean of the county predictors equals the corresponding weighted mean of survey estimates. Difference Benchmarking is the simplest benchmarking method but widely used by multiplying empirical best linear unbiased prediction (EBLUP) estimator by the common adjustment factors (J.N.K Rao and Isabel Molina, 2013).

Description: Implements Benchmarking Method for Multivariate Small Area Estimation under Fay Herriot Model. Multivariate Small Area Estimation (MSAE) is a development of Univariate Small Area Estimation that considering the correlation among response variables and borrowing the strength from related areas and auxiliary variables to increase the effectiveness of sample size, the multivariate model in this package is based on multivariate model 1 proposed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>. Benchmarking in Small Area Estimation is a modification of Small Area Estimation model to guarantee that the aggregate weighted mean of the county predictors equals the corresponding weighted mean of survey estimates. Difference Benchmarking is the simplest benchmarking method but widely used by multiplying empirical best linear unbiased prediction (EBLUP) estimator by the common adjustment factors (J.N.K Rao and Isabel Molina, 2015).