论文标题
快速两阶段的贝叶斯方法，用于估计具有无限空间重量矩阵的面板空间自回归模型
Fast Two-Stage Variational Bayesian Approach to Estimating Panel Spatial Autoregressive Models with Unrestricted Spatial Weights Matrices
论文作者
论文摘要
本文提出了一种快速的两阶段变异贝叶斯（VB）算法，以估计面板空间自回旋模型。使用Dirichlet-Laplace先验，我们能够发现横截面单元之间的空间关系，而不会施加任何先验限制。 Monte Carlo实验表明，我们的方法在长面板和短面板上都可以很好地工作。我们也是文献中第一个开发VB方法的人，以估算具有无限制稀疏模式的大协方差矩阵，这些矩阵对于流行的大型数据模型（例如贝叶斯矢量自动化）有用。在经验应用中，我们研究了欧元区主权债券评级和点差之间的空间相互依赖性。我们发现北部欧元区国家的溢出行为与南方的溢出行为之间存在明显差异。
This paper proposes a fast two-stage variational Bayesian (VB) algorithm to estimate unrestricted panel spatial autoregressive models. Using Dirichlet-Laplace priors, we are able to uncover the spatial relationships between cross-sectional units without imposing any a priori restrictions. Monte Carlo experiments show that our approach works well for both long and short panels. We are also the first in the literature to develop VB methods to estimate large covariance matrices with unrestricted sparsity patterns, which are useful for popular large data models such as Bayesian vector autoregressions. In empirical applications, we examine the spatial interdependence between euro area sovereign bond ratings and spreads. We find marked differences between the spillover behaviours of the northern euro area countries and those of the south.
