摘要翻译:
Bojinov&Shephard(2019)定义了潜在结果时间序列,以非参数测量时间序列实验中的动态因果效应。本文提出了四个创新点:“工具性路径”、“冲击”治疗、“线性潜在结果”和“因果反应函数”。然后利用潜在结果时间序列提供脉冲响应函数、广义脉冲响应函数、局部投影和LP-IV的非参数因果解释。
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英文标题:
《Econometric analysis of potential outcomes time series: instruments,
shocks, linearity and the causal response function》
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作者:
Ashesh Rambachan and Neil Shephard
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最新提交年份:
2020
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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:
Bojinov & Shephard (2019) defined potential outcome time series to nonparametrically measure dynamic causal effects in time series experiments. Four innovations are developed in this paper: "instrumental paths," treatments which are "shocks," "linear potential outcomes" and the "causal response function." Potential outcome time series are then used to provide a nonparametric causal interpretation of impulse response functions, generalized impulse response functions, local projections and LP-IV.
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PDF链接:
https://arxiv.org/pdf/1903.01637