2018/10/06 by Daisuke Murakami, Hajime Seya, Murakami, Daisuke +3
Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #Housing Market and Economics #Methodology (stat.ME) #Regional Economics and Spatial Analysis #Spatial and Panel Data Analysis #stat.ME
paper · pdf · doi:10.48550/arxiv.1810.02956
arxiv created 2018/10/06 · openalex publication_date 2018/10/06 · arxiv updated 2018/10/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This article presents a re-structuring of spatial econometric models in a linear mixed model framework. To that end, it proposes low rank spatial econometric models that are robust to the existence of noise (i.e., measurement error), and can enjoy fast parameter estimation and inference by Type II restricted likelihood maximization (empirical Bayes) techniques. The small sample properties of the proposed low rank spatial econometric models are examined using Monte Carlo simulation experiments, the results of these experiments confirm that direct effects and indirect effects a la LeSage and Pace (2009) can be estimated with a high degree of accuracy. Also, when data are noisy, estimators for coefficients in the proposed models have lower root mean squared errors compared to conventional specifications, despite them being low rank approximations. The proposed approach is implemented in an R package "spmoran".