Bayesian SAR Model with Stochastic Volatility and Multiple Time-Varying Weights
提出一种新的空间自回归模型，能处理面板数据的时变结构方差和多层网络动态关系，用于分析冲击的时变溢出效应。实证发现合作性地缘关系对G7股市影响大于冲突性，并揭示了异质性网络暴露和不同的直接与间接溢出模式。
Abstract A novel spatial autoregressive model with time-varying structural variance for panels of time series data is introduced. It incorporates multilayer networks and accounts for dynamic relationships, thus enabling the analysis of shock propagation through time-varying spillover effects. The proposed method outperforms alternative spatial model benchmarks in an empirical application investigating the impact of cooperative and conflictual geopolitical relationships on G7 stock markets. The results indicate that cooperative interactions have a greater influence on stock markets than conflictual ones, highlighting the collaborative nature of the G7. They also reveal heterogeneous network exposures and distinct patterns of direct and indirect spillover effects.