msae: Multivariate Fay Herriot Models for Small Area Estimation

Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.

Getting started

Package details

AuthorNovia Permatasari, Azka Ubaidillah
MaintainerNovia Permatasari <novia.permatasari@bps.go.id>
LicenseGPL-2
Version0.1.5
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("msae")

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msae documentation built on April 25, 2022, 1:05 a.m.