When Should You Adjust Standard Errors for Clustering?
何时应对标准误进行聚类调整？AI 解读 ↓
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何时应对标准误进行聚类调整？
- 研究问题
- 何种抽样过程或处理分配机制需要聚类标准误，传统聚类调整为何可能过于保守，以及聚类选择何时会影响推断？
- 方法与思路
- 作者从抽样推断与设计推断出发，区分两阶段抽样产生的未观测总体簇问题，以及处理分配与簇成员身份相关的问题。
- 材料陈述的贡献
- 摘要称，该框架解释聚类调整的不同来源，并提出适用于中间情形的方差估计量，以处理传统聚类过保守、稳健标准误偏小的问题。
- 阅读关注点
- 应核对目标总体、抽样层级与处理分配机制如何进入方差公式，以及新估计量的适用条件和传统标准误保守性的判定依据。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明新方差估计量的具体公式、适用条件及有限样本表现。
原文摘要
In empirical work it is common to estimate parameters of models and report associated standard errors that account for "clustering" of units, where clusters are defined by factors such as geography. Clustering adjustments are typically motivated by the concern that unobserved components of outcomes for units within clusters are correlated. However, this motivation does not provide guidance about questions such as: (i) Why should we adjust standard errors for clustering in some situations but not others? How can we justify the common practice of clustering in observational studies but not randomized experiments, or clustering by state but not by gender? (ii) Why is conventional clustering a potentially conservative "all-or-nothing" adjustment, and are there alternative methods that respond to data and are less conservative? (iii) In what settings does the choice of whether and how to cluster make a difference? We address these questions using a framework of sampling and design inference. We argue that clustering can be needed to address sampling issues if sampling follows a two stage process where in the first stage, a subset of clusters are sampled from a population of clusters, and in the second stage, units are sampled from the sampled clusters. Then, clustered standard errors account for the existence of clusters in the population that we do not see in the sample. Clustering can be needed to account for design issues if treatment assignment is correlated with membership in a cluster. We propose new variance estimators to deal with intermediate settings where conventional cluster standard errors are unnecessarily conservative and robust standard errors are too small.