2013/10/27 by Aaron T. Porter, Christopher K. Wikle, Porter, Aaron T. +3 · 2 citations
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Economic and Environmental Valuation #FOS: Computer and information sciences #Methodology (stat.ME) #Spatial and Panel Data Analysis #Statistical Methods and Bayesian Inference #demographic modeling and climate adaptation #stat.ME
paper · pdf · doi:10.48550/arxiv.1310.7211
22 pages, 2 figures
arxiv created 2013/10/27 · openalex publication_date 2013/10/27 · arxiv updated 2013/10/29 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
The Fay-Herriot model is a standard model for direct survey estimators in which the true quantity of interest, the superpopulation mean, is latent and its estimation is improved through the use of auxiliary covariates. In the context of small area estimation, these estimates can be further improved by borrowing strength across spatial region or by considering multiple outcomes simultaneously. We provide here two formulations to perform small area estimation with Fay-Herriot models that include both multivariate outcomes and latent spatial dependence. We consider two model formulations, one in which the outcome-by-space dependence structure is separable and one that accounts for the cross dependence through the use of a generalized multivariate conditional autoregressive (GMCAR) structure. The GMCAR model is shown in a state-level example to produce smaller mean square prediction errors, relative to equivalent census variables, than the separable model and the state-of-the-art multivariate model with unstructured dependence between outcomes and no spatial dependence. In addition, both the GMCAR and the separable models give smaller mean squared prediction error than the state-of-the-art model when conducting small area estimation on county level data from the American Community Survey.