Identifying structural VARs from sparse narrative instruments: Dynamic effects of US macroprudential policies
研究了当工具变量是稀疏的定性观测（仅指示特定时期冲击的符号）时，如何在贝叶斯代理向量自回归模型中实现正确推断，并应用于美国宏观审慎政策，发现资本要求或抵押贷款承销标准收紧后信贷量和房价持续下降，GDP和通胀温和下降，公司债利差扩大。
Summary We study identification in Bayesian proxy VARs for instruments that consist of sparse qualitative observations indicating the signs of shocks in specific periods. We propose the Fisher discriminant regression and a non‐parametric sign concordance criterion as two alternative methods for achieving correct inference in this case. The former represents a minor deviation from a standard proxy VAR, whereas the non‐parametric approach builds on set identification. Our application to US macroprudential policies finds persistent declines in credit volumes and house prices together with moderate declines in GDP and inflation and a widening of corporate bond spreads after a tightening of capital requirements or mortgage underwriting standards.