论文标题
群集 - 刺激性推理的非舒适性：提出一种新的强大方法的提议
Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method
论文作者
论文摘要
常规的群集弹药（CR）标准误差可能不强大。它们容易受到包含少量大簇的数据。当研究人员使用美国51个州作为集群时，最大的集群（加利福尼亚州）约占样本总样本的10％。实际上，这样的例子违反了广泛使用的CR方法可行的假设。我们正式表明，如果群集大小的分布遵循指数少于两个的功率定律，则常规CR方法会失败。除了51个州群集的示例外，还列出了一些最新的原始研究文章列表，该文章发表在《顶级期刊》上。鉴于有关现有CR方法的这些负面结果，我们提出了一种加权CR（WCR）方法作为简单的修复。仿真研究支持我们的论点，即WCR方法是强大的，而常规CR方法则不强。
The conventional cluster-robust (CR) standard errors may not be robust. They are vulnerable to data that contain a small number of large clusters. When a researcher uses the 51 states in the U.S. as clusters, the largest cluster (California) consists of about 10% of the total sample. Such a case in fact violates the assumptions under which the widely used CR methods are guaranteed to work. We formally show that the conventional CR methods fail if the distribution of cluster sizes follows a power law with exponent less than two. Besides the example of 51 state clusters, some examples are drawn from a list of recent original research articles published in a top journal. In light of these negative results about the existing CR methods, we propose a weighted CR (WCR) method as a simple fix. Simulation studies support our arguments that the WCR method is robust while the conventional CR methods are not.
