🤖 AI Summary
This study addresses the challenge that traditional spatial prediction models struggle to simultaneously maintain interpretability of covariates and flexibility in modeling complex spatial dependence structures. To overcome this limitation, the authors propose a semiparametric spatial autoregressive model that integrates linear covariate effects with nonparametrically estimated spatial components. This approach preserves model interpretability while flexibly capturing intricate spatial dependencies, thereby relaxing the strong assumptions on covariance structures commonly imposed by conventional models. The proposed method achieves both high predictive accuracy and strong interpretability, supported by a rigorous asymptotic theory. Empirical evaluations on both simulated and real-world datasets demonstrate that its predictive performance is comparable to that of geostatistical methods, while substantially outperforming classical spatial econometric models in terms of explanatory power.
📝 Abstract
In this paper we propose a semiparametric spatial autoregressive model that combines a linear covariate component with a nonparametrically estimated spatial term, allowing flexible dependence modeling without restrictive covariance structure while preserving interpretability. We establish asymptotic properties, including consistency and asymptotic normality, and evaluate performance through simulations and real data. Results show competitive predictive accuracy relative to geostatistical methods and improved interpretability compared to spatial econometric models.