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A Practitioner’s Guide to Cluster-Robust Inference
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.
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“This type of standard errors ensures valid inference even when certain model assumptions are violated.”
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“Although CRVE-based t-statistics work well in many cases, this approach can fail (sometimes disastrously) when the number of clusters is small, cluster sizes vary a lot, or the variable(s) of interest take non-zero values for only a few clusters; see Cameron and Miller (2015) for a recent survey. The wild cluster boot…”
BackgroundMethodInfluential
Metadata
- Authors
- Colin Cameron, A., & Miller, D. L.
- ORCID
- Miller, Douglas L.
- Venue
- Journal of Human Resources
- Publisher
- University of Wisconsin Press
- Published
- 2015
- Volume
- 50
- Issue
- 2
- Pages
- 317-372
- ISSN
- 0022-166X (ISSN-L)
- 1548-8004
- OpenAlex
- W2118836230
- Cited by
- 5,037
- References
- 83
- Citations by year
- 2026: 992 · 2025: 497 · 2024: 370 · 2023: 385 · 2022: 445 · 2021: 436 · 2020: 425 · 2019: 358 · 2018: 396 · 2017: 344 · 2016: 225 · 2015: 115
- FWCI
- 321.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Human Resources (publishedVersion)
- Project Muse (Johns Hopkins University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- cluster-robust standard errors, clustered data, intra-cluster correlation, regression inference, ordinary least squares, estimator precision
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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