标题： 带有双向聚类的分析推断 显示英文标题

标题： Analytic inference with two-way clustering

摘要： 本文从聚类的两个维度研究了分析推断。 在这样的设置中，常用的方法有两个缺点。 首先，相应的方差估计量不一定是正的。 其次，在非高斯情况下推断是无效的，即当感兴趣参数的估计量不是渐近高斯的时候。 我们考虑了一个简单的修正方法，可以解决这两个问题。 在高斯情况下，相应的检验渐近精确，并且与通常的检验等价。 否则，新的检验渐近保守。 我们还建立了它们在一个特定的数据生成过程类中的统一有效性。 独立于我们的检验，我们强调了在双重聚类下多重检验和非线性估计量可能存在的问题。 最后，我们通过模拟将我们的方法与现有方法进行了比较。 显示英文摘要

摘要： This paper studies analytic inference along two dimensions of clustering. In such setups, the commonly used approach has two drawbacks. First, the corresponding variance estimator is not necessarily positive. Second, inference is invalid in non-Gaussian regimes, namely when the estimator of the parameter of interest is not asymptotically Gaussian. We consider a simple fix that addresses both issues. In Gaussian regimes, the corresponding tests are asymptotically exact and equivalent to usual ones. Otherwise, the new tests are asymptotically conservative. We also establish their uniform validity over a certain class of data generating processes. Independently of our tests, we highlight potential issues with multiple testing and nonlinear estimators under two-way clustering. Finally, we compare our approach with existing ones through simulations.