Multivariate Fay Herriot Models for Small Area Estimation
Transform Dataframe to Matrix R
EBLUPs based on a Multivariate Fay Herriot (Model 1)
EBLUPs based on a Autoregressive Multivariate Fay Herriot (Model 2)
EBLUPs based on a Heteroscedastic Autoregressive Multivariate Fay Herr...
EBLUPs based on a Univariate Fay Herriot (Model 0)
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>.