Instrumental Variable Identification of Dynamic Variance Decompositions
提出在外部工具变量存在测量误差时，仍能通过区间识别方法估计冲击对宏观经济波动的重要性，并应用于美国数据，发现货币冲击对通胀动态的影响有紧的上界。
Macroeconomists increasingly use external sources of exogenous variation for causal inference. However, unless such external instruments (proxies) capture the underlying shock without measurement error, existing methods are silent on the importance of that shock for macroeconomic fluctuations. We show that, in a general moving average model with external instruments, variance decompositions for the instrumented shock are interval-identified, with informative bounds. Various additional restrictions guarantee point identification of both variance and historical decompositions. Unlike SVAR analysis, our methods do not require invertibility. Applied to U.S. data, they give a tight upper bound on the importance of monetary shocks for inflation dynamics.