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摘要翻译：
我们分析了在差异中差异模型中忽略空间相关性对推理有问题的条件。我们证明了空间相关性对于推理的相关性（当它被忽略时）取决于在我们控制了时间和群不变的不可观察项之后仍然存在的空间相关性的数量。因此，当忽略空间相关性时，诸如在估计中使用的时间框架和估计器的选择等细节将是我们应该期望的失真程度的关键决定因素。用真实数据集进行的模拟证实了这些结论。这些发现提供了更好的理解何时空间相关性应该更多的问题，并为如何减少由于空间相关性引起的推理问题提供了重要的指导。
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英文标题：
《Inference in Differences-in-Differences: How Much Should We Trust in
Independent Clusters?》
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作者：
Bruno Ferman
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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：
We analyze the conditions in which ignoring spatial correlation is problematic for inference in differences-in-differences models. We show that the relevance of spatial correlation for inference (when it is ignored) depends on the amount of spatial correlation that remains after we control for the time- and group-invariant unobservables. As a consequence, details such as the time frame used in the estimation, and the choice of the estimator, will be key determinants on the degree of distortions we should expect when spatial correlation is ignored. Simulations with real datasets corroborate these conclusions. These findings provide a better understanding on when spatial correlation should be more problematic, and provide important guidelines on how to minimize inference problems due to spatial correlation.
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PDF链接：
https://arxiv.org/pdf/1909.01782
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