摘要翻译：
如果在应用经济学中经常使用多向聚类稳健标准误差，那么令人惊讶的是，很少有理论结果证明这种做法是合理的。本文旨在填补这一空白。我们首先证明，在与I.I.D.几乎相同的条件下。数据，多向聚类下经验过程的弱收敛性。这一结果意味着样本平均的中心极限定理，但也是显示非线性估计（如GMM估计）渐近正态性的关键。然后我们建立了各种渐近方差估计的相合性，包括Cameron等人的估计。（2011）但也是一个新的估计量，是正的构造。其次，我们给出了适用于多向聚类的重采样方案鸽子洞自举的线性估计和非线性估计的一般相合性。Monte Carlo仿真表明，即使在很少的聚类情况下，基于我们的两种首选方法的推理也可能是准确的，并显著改善了基于Cameron等人的推理。（2011年）。
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英文标题：
《Asymptotic results under multiway clustering》
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作者：
Laurent Davezies, Xavier D'Haultfoeuille and Yannick Guyonvarch
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最新提交年份：
2018
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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：
  If multiway cluster-robust standard errors are used routinely in applied economics, surprisingly few theoretical results justify this practice. This paper aims to fill this gap. We first prove, under nearly the same conditions as with i.i.d. data, the weak convergence of empirical processes under multiway clustering. This result implies central limit theorems for sample averages but is also key for showing the asymptotic normality of nonlinear estimators such as GMM estimators. We then establish consistency of various asymptotic variance estimators, including that of Cameron et al. (2011) but also a new estimator that is positive by construction. Next, we show the general consistency, for linear and nonlinear estimators, of the pigeonhole bootstrap, a resampling scheme adapted to multiway clustering. Monte Carlo simulations suggest that inference based on our two preferred methods may be accurate even with very few clusters, and significantly improve upon inference based on Cameron et al. (2011).
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PDF链接：
https://arxiv.org/pdf/1807.07925