A1338
Title: Small area estimation under bivariate Fay-Herriot model with correlated random effects
Authors: Domingo Morales - University Miguel Hernandez of Elche (Spain) [presenting]
Esteban Cabello - Universidad Miguel Hernandez de Elche (Spain)
Maria-Dolores Esteban - University-Miguel Hernandez of Elche (Spain)
Agustin Perez Martin - University Miguel Hernandez of Elche (Spain)
Abstract: An area-level temporal bivariate linear mixed model is presented, which incorporates correlated time effects for estimating poverty indicators in small areas. The model is applied through the residual maximum likelihood method, leading to the derivation of empirical best linear unbiased predictors for these indicators. Additionally, it provides an approximation of the matrix of mean squared errors (MSE), and it proposes four MSE estimators. The first estimator involves a plug-in approach to the MSE approximation, while the remaining estimators are based on parametric bootstrap procedures. Three simulation experiments were carried out to assess the performance of the fitting algorithm, predictors, and MSE estimators. An application to real data from the 2016 to 2022 Spanish Living Conditions Survey is conducted. The focus is on estimating poverty proportions and gaps for the year 2022, categorized by provinces and sex.
