估计具有无约束空间权重矩阵的面板空间自回归模型的快速两阶段变分贝叶斯方法
Fast Two-Stage Variational Bayesian Approach to Estimating Panel Spatial Autoregressive Models with Unrestricted Spatial Weights Matrices
AI总结：
本文提出快速两阶段变分贝叶斯算法估计无约束面板空间自回归模型，采用Dirichlet-Laplace先验挖掘空间关系，经实验验证长短面板均适用，还首次实现无约束稀疏大协方差矩阵的VB估计，并实证发现欧元区南北国家主权债券溢出行为差异显著。
摘要：
本文提出一种快速两阶段变分贝叶斯（VB，variational Bayesian）算法，用于估计无约束面板空间自回归模型。通过采用Dirichlet-Laplace先验，我们无需施加任何先验限制即可揭示截面单元之间的空间关系。蒙特卡洛实验表明，该方法对长面板和短面板均表现良好。我们也是文献中首个开发出VB方法来估计具有无约束稀疏模式的大型协方差矩阵的研究，这类矩阵可用于贝叶斯向量自回归等常用的大型数据模型。在实证应用中，我们考察了欧元区主权债券评级与利差之间的空间相互依存关系，发现欧元区北部国家与南部国家的溢出行为存在显著差异。
英文摘要：
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.
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