Data‐driven identification in SVARs—When and how can statistical characteristics be used to unravel causal relationships?
比较了在异方差或非高斯独立成分下，利用统计特性识别结构向量自回归模型中经济冲击的不同方法，发现统计识别方案对数据结构有一定稳健性，且检测独立成分最为灵活。
Abstract Structural vector autoregressive analysis aims to trace the contemporaneous linkages among multiple economic time series back to underlying orthogonal structural shocks. Traditionally, researchers rely on economically motivated restrictions to identify these shocks. However, in the presence of heteroskedasticity or non‐Gaussian independent components, only these statistical properties allow a locally unique identification. In this paper, we compare alternative statistical identification procedures under distinct covariance changes and distributional frameworks. We find that statistical identification schemes are robust under distinct data structures to some extent and support researchers in detecting shocks that feature an economic underpinning. The detection of independent components appears most flexible.