The Journal of Human Resources· 2015Q1
A Practitioner’s Guide to Cluster-Robust Inference
- 4,589citations
- Q1SCImago
- 2015year
Short summary
When data is grouped into clusters, standard statistical error calculations can overestimate precision. This guide explains how to use cluster-robust standard errors for more accurate inference, especially with a large number of clusters.
AI-generated from the title and abstract; the full text is not read.
Key points
- Default standard errors are unreliable in clustered data settings.
- Cluster-robust standard errors are recommended for large numbers of clusters.
- The guide addresses complications like cluster-specific fixed effects and multi-way clustering.
- Accurate inference is crucial for understanding estimator precision in grouped data.
AI-generated from the title and abstract; the full text is not read.
Abstract
We consider statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters. Examples include data on individuals with clustering on village or region or other category such as industry, and state-year differences-in-differences studies with clustering on state. In such settings default standard errors can greatly overstate estimator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster-robust standard errors. We outline the basic method as well as many complications that can arise in practice. These include cluster-specific fixed efects, few clusters, multi-way clustering, and estimators other than OLS.
The authors' abstract, as published at the source. The Journal of Human Resources, 2015 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics