Asymptotic results under multiway clustering
Asymptotic results under multiway clustering
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多路聚类下的渐近结果
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Yannick Guyonvarch
中科院分区：
文献类型：
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作者：
L. Davezies;Xavier d'Haultfoeuille;Yannick Guyonvarch
如果在应用经济学中经常使用多向聚类稳健标准误，令人惊讶的是，很少有理论结果证明这种做法是正确的。本文旨在填补这一空白。我们首先证明，在几乎与i.i.d.相同的条件下。数据，弱收敛的经验过程下多路聚类。这一结果意味着样本平均值的中心极限定理，但也是显示非线性估计，如GMM估计的渐近正态性的关键。然后，我们建立了各种渐近方差估计的一致性，包括卡梅隆等人（2011），但也是一个新的估计，是积极的建设。接下来，我们展示了一般的一致性，为线性和非线性估计，鸽子洞的引导，一个rescent计划，适用于多路聚类。蒙特卡罗模拟表明，基于我们的两种优选方法的推断即使在非常少的聚类的情况下也可能是准确的，并且显著改进了基于卡梅隆等人（2011）的推断。
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).