When and How to Deal with Clustered Errors in Regression Models
When and How to Deal with Clustered Errors in Regression Models
复制标题
何时以及如何处理回归模型中的集群错误
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Matthew D. Webb
中科院分区：
文献类型：
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作者：
J. MacKinnon;Matthew D. Webb
我们讨论了何时以及如何处理线性回归模型中可能出现的聚集误差。具体来说，我们讨论的情况下，一个回归模型可能会被视为具有误差项，是任意相关的已知集群，但不相关的。我们讨论的方法包括各种协方差矩阵估计，可能与各种方法相结合，获得临界值，几个自助程序，和随机化推理。特别注意的是，很少处理的集群和集群的大小变化很大，推断可能是有问题的模型。两个实证例子说明了我们讨论的方法和我们提出的问题，和模拟实验说明了过度聚类和欠聚类的后果。
We discuss when and how to deal with possibly clustered errors in linear regression models. Specifically, we discuss situations in which a regression model may plausibly be treated as having error terms that are arbitrarily correlated within known clusters but uncorrelated across them. The methods we discuss include various covariance matrix estimators, possibly combined with various methods of obtaining critical values, several bootstrap procedures, and randomization inference. Special attention is given to models with few treated clusters and clusters that vary a lot in size, where inference may be problematic. Two empirical examples illustrate the methods we discuss and the concerns we raise, and a simulation experiment illustrates the consequences of over-clustering and under-clustering.
影响因子：
5
作者：
Miglioretti, Diana L.;Heagerty, Patrick J.
通讯作者：
Heagerty, Patrick J.