摘要翻译：
本文提出了差中差(DID)研究设计中平均治疗效果(ATT)的双稳健估计。与替代的DID估计量相比，如果倾向评分或结果回归工作模型中的任何一个（但不一定都是）被正确指定，建议的估计量是一致的。我们还导出了在DID设计中ATT的半参数效率界，当面板或重复截面数据可用时，证明了当工作模型正确指定时，我们提出的估计量达到了半参数效率界。此外，我们量化了访问面板数据而不是重复截面数据的潜在效率增益。最后，通过对用于估计干扰参数的估计方法的关注，我们表明有时可以为ATT构造双鲁棒的DID估计，这些估计对于推理也是双鲁棒的。仿真研究和一个经验应用说明了所提出的估计器的理想的有限样本性能。现有用于执行拟议政策评价工具的开放源码软件。
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英文标题：
《Doubly Robust Difference-in-Differences Estimators》
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作者：
Pedro H. C. Sant'Anna, Jun B. Zhao
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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：
  This article proposes doubly robust estimators for the average treatment effect on the treated (ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID estimators, the proposed estimators are consistent if either (but not necessarily both) a propensity score or outcome regression working models are correctly specified. We also derive the semiparametric efficiency bound for the ATT in DID designs when either panel or repeated cross-section data are available, and show that our proposed estimators attain the semiparametric efficiency bound when the working models are correctly specified. Furthermore, we quantify the potential efficiency gains of having access to panel data instead of repeated cross-section data. Finally, by paying articular attention to the estimation method used to estimate the nuisance parameters, we show that one can sometimes construct doubly robust DID estimators for the ATT that are also doubly robust for inference. Simulation studies and an empirical application illustrate the desirable finite-sample performance of the proposed estimators. Open-source software for implementing the proposed policy evaluation tools is available.
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PDF链接：
https://arxiv.org/pdf/1812.01723