摘要翻译：
研究了差中差(DID)模型中处理单元较少且误差空间相关时的推理问题。我们首先证明，当存在单个处理单元时，针对处理单元较少且控制单元较多的设置而设计的已有推理方法在误差弱相关时仍是渐近有效的。然而，这些方法可能是无效的超过一个处理单位。对于含有多个处理单元的设置，我们提出了渐近有效但通常保守的推理方法。即使没有跨单位的相关距离度量，这些替代方案也是有效的。我们还提供了一个实证应用，突出了在DID应用中使用随机化推理的一些常见误解，并说明了如何使用我们的结果来提供正确的推理。
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英文标题：
《Inference in Difference-in-Differences with Few Treated Units and
  Spatial Correlation》
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作者：
Bruno Ferman
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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：
  We consider the problem of inference in Difference-in-Differences (DID) models when there are few treated units and errors are spatially correlated. We first show that, when there is a single treated unit, existing inference methods designed for settings with few treated and many control units remain asymptotically valid when errors are weakly dependent. However, these methods may be invalid with more than one treated unit. We propose asymptotically valid, though generally conservative, inference methods for settings with more than one treated unit. These alternatives are valid even when the relevant distance metric across units is unavailable. We also present an empirical application that highlights some common misunderstandings in the use of randomization inference in DID applications, and illustrates how our results can be used to provide proper inference.
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PDF链接：
https://arxiv.org/pdf/2006.16997