论文标题
仪器变量识别动态差异分解
Instrumental Variable Identification of Dynamic Variance Decompositions
论文作者
论文摘要
宏观经济学家越来越多地将外源性变异的外部来源用于因果推断。但是，除非此类外部工具（代理）捕获基本的冲击而没有测量误差，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，在具有外部仪器的一般移动平均模型中，仪器冲击的方差分解是间隔识别的，具有信息界限。各种额外的限制保证了方差和历史分解的点识别。与SVAR分析不同，我们的方法不需要可逆性。应用于美国数据，他们对货币冲击对通货膨胀动态的重要性产生了严格的上限。
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.
