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Kernel-based spatial error model for analyzing spatial panel data

Spatial panel data model captures spatial interactions across spatial units and over time. Lots of effort have been devoted to develop effective estimation methods for parametric and nonparametric spatial panel data models. Varying coefficient model has received a great deal of attention as an important tool for modeling panel data. In this paper we propose a kernel-based spatial error model for the purpose of analyzing spatial panel data. This model is based on the idea of fixed effect time-varying coefficient model and the kernel technique of support vector machine along with the technique of regularization. A generalized cross validation method is also considered for choosing the hyperparameters which affect the performance of the proposed model. The proposed model is evaluated through numerical studies.