The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Robust Inference with Multi-way Clustering
Open accessAbstract
In this paper we propose a new variance estimator for OLS as well as for nonlinear estimators such as logit, probit and GMM, that provcides cluster-robust inference when there is two-way or multi-way clustering that is non-nested.The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g.Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions.Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering.The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state-year effects example of Bertrand et al. (2004) to two dimensions; and by application to two studies in the empirical public/labor literature where two-way clustering is present.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
How Much Should We Trust Differences-in-Differences Estimates?
Bertrand, M., Duflo, E., & Mullainathan, S. (2004) · The Quarterly Journal of Economics · doi:10.1162/003355304772839588 · Cited by 10,641
Cited by
Antibiotic resistance threats in the United States, 2019
(U.S.), C. F. D. C. A. P. (2019) · doi:10.15620/cdc:82532 · Cited by 5,782
Does Working from Home Work? Evidence from a Chinese Experiment *
Bloom, N., Liang, J., Roberts, J., & Ying, Z. J. (2015) · The Quarterly Journal of Economics · doi:10.1093/qje/qju032 · Cited by 1,991
Minimum wage effects across state borders: Estimates using contiguous counties
Dube, A., Lester, T. W., & Reich, M. (2010) · Review of Economics and Statistics · doi:10.1162/rest_a_00039 · Cited by 1,221
Causal inference with observational data
Nichols, A. (2007) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0800700403 · Cited by 339
Looking for Local Labor Market Effects of NAFTA
Hakobyan, S., & McLaren, J. (2016) · Review of Economics and Statistics · doi:10.1162/rest_a_00587 · Cited by 284
Metadata
- Authors
- Cameron, A. C., Gelbach, J., & Miller, D.
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2006-09
- OpenAlex
- W3122696585
- Cited by
- 409
- References
- 1
- Type
- report
- Language
- en
- Open access
- green
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Robust Inference with Multi-way Clustering. https://app.cochat.ai/papers/10.3386/t0327
- The paper
- Cameron, A. C., Gelbach, J., & Miller, D. (2006). Robust Inference with Multi-way Clustering. National Bureau of Economic Research. https://doi.org/10.3386/t0327
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
