  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".
