Statistical identification in panel structural vector autoregressive models based on independence criteria
提出一种新的面板结构向量自回归方法，通过施加结构创新在合并层面的独立性来实现识别，模拟实验证明该方法在截面相关和异质性下稳健，并应用于欧元区货币政策传导分析。
Summary This paper introduces a novel panel approach to structural vector autoregressive analysis. For identification, we impose independence of structural innovations at the pooled level. We demonstrate robustness of the method under cross‐sectional correlation and heterogeneity through simulation experiments. In an empirical application on monetary policy transmission in the Euro area, we find that bond spreads rise significantly after an unexpected monetary tightening. Furthermore, the central bank responds to offset effects of adverse financial shocks. Additionally, we document sizable heterogeneity in country‐specific output responses.