摘要翻译：
本研究在不同的假设下，考虑了不同的半参数差中差模型，对横截面数据和面板数据的处理组标识符、时间和协变量之间的关系进行了研究。方差下界对模型假设很敏感，这意味着鲁棒性和效率之间的权衡。在弱第一阶段收敛条件下，所得到的有效影响函数使估计量具有速率双鲁棒性和理想的渐近性质。这使得能够使用复杂的机器学习算法来处理常见的趋势混杂是高维的设置。在一个经验例子中评估了所提出的估计量的有用性。结果表明，效率-鲁棒性的权衡和第一阶段预测器的选择在实践中会导致不同的实证结果。
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英文标题：
《Efficient Difference-in-Differences Estimation with High-Dimensional
  Common Trend Confounding》
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作者：
Michael Zimmert
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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 study considers various semiparametric difference-in-differences models under different assumptions on the relation between the treatment group identifier, time and covariates for cross-sectional and panel data. The variance lower bound is shown to be sensitive to the model assumptions imposed implying a robustness-efficiency trade-off. The obtained efficient influence functions lead to estimators that are rate double robust and have desirable asymptotic properties under weak first stage convergence conditions. This enables to use sophisticated machine-learning algorithms that can cope with settings where common trend confounding is high-dimensional. The usefulness of the proposed estimators is assessed in an empirical example. It is shown that the efficiency-robustness trade-offs and the choice of first stage predictors can lead to divergent empirical results in practice.
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PDF链接：
https://arxiv.org/pdf/1809.01643