Abstract
Motivated by the problem of variable selection in spatially varying coefficients models for spatial econometrics data, a Bayesian spatially dynamic selection model based on spatial normal-gamma process (SNGP) is proposed, which pursues spatial varying sparsity in dynamic regression models. Theoretical properties of SNGP are discussed. Posterior samples are obtained by nimble, a powerful R package for Bayesian inference. A new tuning-free variable selection based on K-groups clustering is proposed for discriminating the signal and the noise. Simulation studies show that the proposed method has both good estimation performance and selection performance. Finally, the new method is applied to analyzing a county level income data of Louisiana.
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