论文标题
聚类回归模型中的杠杆，影响力和折刀：使用SummClust的可靠推理
Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust
论文作者
论文摘要
我们介绍了一个名为SummClust的新STATA软件包，该软件包总结了具有群集干扰的线性回归模型数据集的群集结构。这种模型的关键观察单位是群集。因此，我们建议在大多数情况下如何快速计算它们的杠杆率，部分杠杆作用和影响集群级别。杠杆率和部分杠杆的度量可以用作诊断工具，以识别数据集和回归设计，其中群集射击推理可能具有挑战性。影响的度量可以提供有关结果如何依赖各个集群中数据的宝贵信息。我们还展示了如何有效地计算两个折刀方差矩阵估计器作为我们其他计算的副产品。这些估计器在Stata中已经可用，通常比常规方差矩阵估计器更为保守。 SummClust软件包计算我们讨论的所有数量。
We introduce a new Stata package called summclust that summarizes the cluster structure of the dataset for linear regression models with clustered disturbances. The key unit of observation for such a model is the cluster. We therefore propose cluster-level measures of leverage, partial leverage, and influence and show how to compute them quickly in most cases. The measures of leverage and partial leverage can be used as diagnostic tools to identify datasets and regression designs in which cluster-robust inference is likely to be challenging. The measures of influence can provide valuable information about how the results depend on the data in the various clusters. We also show how to calculate two jackknife variance matrix estimators efficiently as a byproduct of our other computations. These estimators, which are already available in Stata, are generally more conservative than conventional variance matrix estimators. The summclust package computes all the quantities that we discuss.
