2026/04/01 by María Bugallo, Domingo Morales, Nicola Salvati +1 · 1 voice
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Spatial and Panel Data Analysis #Statistical Methods and Bayesian Inference #demographic modeling and climate adaptation
paper · doi:10.1093/jrsssa/qnag055
openalex publication_date 2026/04/01 · openalex created_date 2026/04/17 · openalex updated_date 2026/07/23
Abstract The paper introduces a novel framework for small area estimation based on spatio-temporal M-quantile regression. The proposed approach extends the Geographically Weighted Regression by incorporating both spatial and temporal weighting schemes, and integrates them with the M-quantile modelling to effectively capture local distributional features across space and time. The resulting predictors are specifically designed for out-of-sample prediction in small domains and are accompanied by analytical estimators of their mean squared error. The methodology is evaluated through extensive simulation studies, demonstrating strong robustness to spatio-temporal dependence and the presence of outliers at both unit and area levels. An application to county-level air quality data in the United States (2016–2023) highlights the predictive performance and practical relevance of the proposed methods.