ADVANCES IN USING VECTOR AUTOREGRESSIONS TO ESTIMATE STRUCTURAL MAGNITUDES
综述了从向量自回归中得出结构结论的最新进展，提出贝叶斯推断可承认先验信息不完美，并纠正了估计结构弹性时的一个常见错误。
This paper surveys recent advances in drawing structural conclusions from vector autoregressions (VARs), providing a unified perspective on the role of prior knowledge. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns from both a frequentist and a Bayesian perspective about the way that results are typically reported for VARs that are set-identified using sign and other restrictions. We call attention to a common but previously unrecognized error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one only knows the effects of a single structural shock.