摘要翻译：
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断。然而，除非这些外部工具（代理）捕捉到潜在的冲击而没有测量误差，否则现有的方法对这种冲击对宏观经济波动的重要性保持沉默。我们证明，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，具有信息的界。各种附加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，它们给出了货币冲击对通胀动态重要性的严格上限。
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英文标题：
《Instrumental Variable Identification of Dynamic Variance Decompositions》
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作者：
Mikkel Plagborg-M{\\o}ller, Christian K. Wolf
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最新提交年份：
2021
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分类信息：

一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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.
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