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 structural vector autoregression analysis, our methods do not require invertibility. Applied to US data, they give a tight upper bound on the importance of monetary shocks for inflation dynamics.

中文翻译：

---

动态方差分解的工具变量识别

宏观经济学家越来越多地使用外生变异的外部来源进行因果推理。然而，除非这些外部工具（代理）在没有测量误差的情况下捕捉到潜在的冲击，否则现有方法对这种冲击对宏观经济波动的重要性保持沉默。我们表明，在具有外部工具的一般移动平均模型中，工具冲击的方差分解是区间识别的，具有信息界限。各种附加限制保证了方差和历史分解的点识别。与结构向量自回归分析不同，我们的方法不需要可逆性。应用于美国数据，它们对货币冲击对通胀动态的重要性给出了严格的上限。