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.

针对空间计量经济学数据的空间变化系数模型中变量选择的问题，提出了一种基于空间正伽马过程（SNGP）的贝叶斯空间动态选择模型，该模型在动态回归模型中追求空间变化的稀疏性。讨论了SNGP的理论性质。后验样本是通过nimble获得的，后者是用于贝叶斯推断的强大R包。新的免调变量选择基于$ķ$提出了基于群聚类的方法来区分信号和噪声。仿真研究表明，该方法具有良好的估计性能和选择性能。最后，将新方法应用于分析路易斯安那州的县级收入数据。