Penalty and related estimation strategies in the spatial error model

Spatial autoregressive models are powerful tools in the analysis of data sets from diverse scientific areas of research such as econometrics, plant species richness, cancer mortality rates, image processing, analysis of the functional Magnetic Resonance Imaging (fMRI) data, and many more. An important class in the host of spatial autoregressive models is the class of spatial error models in which spatially lagged error terms are assumed. In this paper, we propose efficient shrinkage and penalty estimators for the regression coefficients of the spatial error model. We carry out asymptotic as well as simulation analyses to illustrate the gain in efficiency achieved by these new estimators. Furthermore, we apply the new methodology to housing prices data and provide a bootstrap approach to compute prediction errors of the new estimators.

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