常见时间序列估计量何时具有非参数因果含义？
When do common time series estimands have nonparametric causal meaning?
AI总结：
本文提出直接潜在结果系统框架，为常见时间序列估计量（如脉冲响应函数、局部投影等）在观测数据中提供非参数因果解释的条件，奠定宏观经济冲击因果效应分析基础。
摘要：
本文引入了直接潜在结果系统作为分析观测时间序列中分配对结果的动态因果效应的框架。我们提供了条件，在这些条件下，常见的预测性时间序列估计量，如脉冲响应函数、广义脉冲响应函数、局部投影和局部投影工具变量，在动态因果效应方面具有非参数因果解释。因此，直接潜在结果系统为分析估计宏观经济冲击对结果因果效应的流行简化形式方法提供了基础。
英文摘要：
In this paper, we introduce the direct potential outcome system as a framework for analyzing dynamic causal effects of assignments on outcomes in observational time series settings. We provide conditions under which common predictive time series estimands, such as the impulse response function, generalized impulse response function, local projection, and local projection instrumental variables, have a nonparametric causal interpretation in terms of dynamic causal effects. The direct potential outcome system therefore provides a foundation for analyzing popular reduced-form methods for estimating the causal effect of macroeconomic shocks on outcomes in time series settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
