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Title: Empirical best prediction of bivariate nonlinear small area indicators Authors:  Domingo Morales - University Miguel Hernandez of Elche (Spain) [presenting]
Maria-Dolores Esteban - University-Miguel Hernandez of Elche (Spain)
Maria Jose Lombardia - Universidade da Coruna (Spain)
Esther Lopez Vizcaino - Universidade da Coruña (Spain)
Agustin Perez - University Miguel Hernandez of Elche (Spain)
Abstract: The small area estimation of population averages of unit-level compositional data is investigated. The new methodology transforms the compositions into vectors of a Euclidean space and assumes that the vectors follow a multivariate nested error regression model. Empirical best predictors of domain indicators are derived from the fitted model and their mean squared errors are estimated by parametric bootstrap. The empirical analysis of the behaviour of the introduced predictors is investigated by means of simulation experiments. An application to real data from the Spanish household budget survey, in 2016, is given. The target is to estimate the average proportions of annual household expenditures on food, housing and others, by Spanish provinces.