Multivariate small area estimation of multidimensional latent economic wellbeing indicators

Abstract

"Factor analysis models are used in data dimensionality reduction problems where the variability among observed variables can be described through a smaller number of unobserved latent variables. This approach is often used to estimate the multidimensionality of wellbeing. We employ factor analysis models and use multivariate EBLUP (MEBLUP) under a unit-level small area estimation approach to predict a vector of means of factor scores representing wellbeing for small areas. We compare this approach to the standard approach whereby we use SAE (univariate and multivariate) to estimate a dashboard of EBLUPs of the means of the original variables and then averaged. Our simulation study shows that the use of factor scores provides estimates with lower variability than weighted and simple averages of standardised MEBLUPs and univariate EBLUPs. Moreover, we find that when the correlation in the observed data is taken into account before small area estimates are computed, multivariate modelling does not provide large improvements in the precision of the estimates over the univariate modelling. We close with an application using the European Union Statistics on Income and Living Conditions data." (Author's abstract, Published by arrangement with John Wiley & Sons) ((en))

Cite article

Moretti, A., Shlomo, N. & Sakshaug, J. (2020): Multivariate small area estimation of multidimensional latent economic wellbeing indicators. In: International statistical review, Vol. 88, No. 1, p. 1-28. DOI:10.1111/insr.12333