Spatially selected and dependent random effects for small area estimation with application to rent burden

2024/04/18 by Sho Kawano, Kawano, Sho, Paul A. Parker +3 · 1 voice
Agricultural and Biological Sciences · Decision Sciences · Economics, Econometrics and Finance · #Agricultural Economics and Policy #Spatial and Panel Data Analysis #demographic modeling and climate adaptation

paper · pdf · doi:10.1093/jrsssa/qnaf063

openalex publication_date 2025/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23

Abstract

Abstract Area-level models for small area estimation typically rely on areal random effects to shrink design-based direct estimates towards a model-based predictor. Incorporating the spatial dependence of the random effects into these models can further improve the estimates when there are not enough covariates to fully account for the spatial dependence of the areal means. A number of recent works have investigated models that include random effects for only a subset of areas, in order to improve the precision of estimates. However, such models do not readily handle spatial dependence. In this paper, we introduce a model that accounts for spatial dependence in both the random effects as well as the latent process that selects the effects. We show how this model can significantly improve predictive accuracy via an empirical simulation study based on data from the American Community Survey, and illustrate its properties via an application to estimate county-level median rent burden.

Citations

• Strictly Proper Scoring Rules, Prediction, and Estimation
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• A Generalization of Sampling Without Replacement from a Finite Universe
• Conjugate Modeling Approaches for Small Area Estimation with Heteroscedastic Structure
• A note on intrinsic Conditional Autoregressive models for disconnected\n graphs
• An intuitive Bayesian spatial model for disease mapping that accounts\n for scaling
• A two-component normal mixture alternative to the Fay-Herriot model
• Small Area Estimation via Multivariate Fay-Herriot Models with Latent\n Spatial Dependence
• Bayesian inference for logistic models using Polya-Gamma latent\n variables
• The Bayesian Lasso

Discussions

• 🆕Spatially selected and dependent random effects for small area estimation with application to rent burden 💡Kawano Parker & Li we introduce a model that accounts for spatial dependence in random ef [bsky, 0 points, 1 comments]