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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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Cites
(2001) · RePEc: Research Papers in Economics · Cited by 27,577
A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity
White, H. (1980) · Econometrica · doi:10.2307/1912934 · Cited by 25,861
Longitudinal data analysis using generalized linear models
LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550
A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix
Newey, W. K., & West, K. D. (1986) · National Bureau of Economic Research · doi:10.3386/t0055 · Cited by 12,682
Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches
Petersen, M. A. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhn053 · Cited by 10,804
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
Mostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2009) · doi:10.1515/9781400829828 · Cited by 9,467
Microeconometrics: Methods and Applications
Cameron, A. C., & Trivedi, P. K. (2005) · doi:10.1017/cbo9780511811241 · Cited by 8,092
Self-citationTesting for Weak Instruments in Linear IV Regression
Stock, J. H., & Yogo, M. (2005) · Identification and Inference for Econometric Models · doi:10.1017/cbo9780511614491.006 · Cited by 7,211
Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data
Driscoll, J. C., & Kraay, A. C. (1998) · Review of Economics and Statistics · doi:10.1162/003465398557825 · Cited by 6,865
Abadie, A., Diamond, A., & Hainmueller, J. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.ap08746 · Cited by 5,513
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. (1994) · doi:10.3386/t0151 · Cited by 5,302
Multilevel and Longitudinal Modeling Using Stata
Rabe-Hesketh, S., & Skrondal, A. (2005) · Biometrics · doi:10.1111/j.1541-0420.2006.00588_16.x · Cited by 4,626
(2009) · Cited by 4,453
Bootstrap-Based Improvements for Inference With Clustered Errors
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008) · Review of Economics and Statistics · doi:10.1162/rest.90.3.414 · Cited by 4,417
Self-citationAn Approximate Distribution of Estimates of Variance Components
Satterthwaite, F. E. (1946) · Biometrics Bulletin · doi:10.2307/3002019 · Cited by 4,373
Bound, J., Jaeger, D. A., & Baker, R. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476536 · Cited by 3,898
Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence
Hoechle, D. (2007) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0700700301 · Cited by 3,462
Robust Inference With Multiway Clustering
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2011) · Journal of Business & Economic Statistics · doi:10.1198/jbes.2010.07136 · Cited by 3,203
Self-citationGeneralized reduced rank tests using the singular value decomposition
Kleibergen, F. R., & Paap, R. (2005) · Journal of Econometrics · doi:10.1016/j.jeconom.2005.02.011 · Cited by 2,831
Regression standard errors in clustered samples
Rogers, W. H. (1993) · Stata technical bulletin · Cited by 2,694
GMM Estimation with Cross Sectional Dependence
Conley, T. G. (1999) · Journal of Econometrics · doi:10.1016/s0304-4076(98)00084-0 · Cited by 2,685
An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units
Moulton, B. R. (1990) · The Review of Economics and Statistics · doi:10.2307/2109724 · Cited by 2,567
MacKinnon, J. G., & White, H. (1985) · Journal of Econometrics · doi:10.1016/0304-4076(85)90158-7 · Cited by 1,879
Enhanced Routines for Instrumental Variables/Generalized Method of Moments Estimation and Testing
(2007) · The Stata Journal Promoting communications on statistics and Stata · Cited by 1,668
Random group effects and the precision of regression estimates
Moulton, B. R. (1986) · Journal of Econometrics · doi:10.1016/0304-4076(86)90021-7 · Cited by 1,574
Simple formulas for standard errors that cluster by both firm and time
Thompson, S. B. (2010) · Journal of Financial Economics · doi:10.1016/j.jfineco.2010.08.016 · Cited by 1,516
PRACTITIONERS’ CORNER: Computing Robust Standard Errors for Within‐groups Estimators *
Arellano, M. (1987) · Oxford Bulletin of Economics and Statistics · doi:10.1111/j.1468-0084.1987.mp49004006.x · Cited by 1,511
Asymptotic Theory for Econometricians.
Robinson, P. M., & White, H. (1988) · Economica · doi:10.2307/2554258 · Cited by 1,334
Inference with Difference-in-Differences and Other Panel Data
Donald, S. G., & Lang, K. (2007) · Review of Economics and Statistics · doi:10.1162/rest.89.2.221 · Cited by 1,324
Using Randomization in Development Economics Research: A Toolkit
Duflo, E., Glennerster, R., & Kremer, M. R. (2006) · National Bureau of Economic Research · doi:10.3386/t0333 · Cited by 1,208
Cluster-Sample Methods in Applied Econometrics
Wooldridge, J. M. (2003) · American Economic Review · doi:10.1257/000282803321946930 · Cited by 1,183
The formation of risk sharing networks
Fafchamps, M., & Gubert, F. (2006) · Journal of Development Economics · doi:10.1016/j.jdeveco.2006.05.005 · Cited by 693
Inference with “Difference in Differences” with a Small Number of Policy Changes
Conley, T. G., & Taber, C. R. (2011) · Review of Economics and Statistics · doi:10.1162/rest_a_00049 · Cited by 633
Heteroskedasticity-robust Standard Errors for Fixed Effects Panel Data Regression
Stock, J. H., & Watson, M. W. (2008) · Econometrica · doi:10.1111/j.0012-9682.2008.00821.x · Cited by 609
Inference from Complex Samples
Kish, L., & Frankel, M. R. (1974) · Journal of the Royal Statistical Society Series B (Methodological) · doi:10.1111/j.2517-6161.1974.tb00981.x · Cited by 530
The wild bootstrap, tamed at last
Davidson, R., & Flachaire, E. (2008) · Journal of Econometrics · doi:10.1016/j.jeconom.2008.08.003 · Cited by 513
Wild Bootstrap Inference for Wildly Different Cluster Sizes
MacKinnon, J. G., & Webb, M. D. (2016) · Journal of Applied Econometrics · doi:10.1002/jae.2508 · Cited by 461
The Effects of High Stakes High School Achievement Awards: Evidence from a Randomized Trial
Angrist, J. D., & Lavy, V. (2009) · American Economic Review · doi:10.1257/aer.99.4.1384 · Cited by 392
Robust Standard Error Estimation in Fixed-Effects Panel ModelsPreprint
Kézdi, G. (2004) · SSRN Electronic Journal · doi:10.2139/ssrn.596988 · Cited by 347
Bias reduction in standard errors for linear regression with multi-stage samples
McCaffrey, D. F., & Bell, R. M. (2003) · Quality Engineering · Cited by 318
t -Statistic Based Correlation and Heterogeneity Robust Inference
Ibragimov, R., & Müller, U. K. (2009) · Journal of Business and Economic Statistics · doi:10.1198/jbes.2009.08046 · Cited by 300
Asymptotic properties of a robust variance matrix estimator for panel data when is large
Hansen, C. B. (2007) · Journal of Econometrics · doi:10.1016/j.jeconom.2006.10.009 · Cited by 293
Fixed effects estimation of structural parameters and marginal effects in panel probit models
Fernández‐Val, I. (2009) · Journal of Econometrics · doi:10.1016/j.jeconom.2009.02.007 · Cited by 275
Handbook of Empirical Economics and Finance
(2010) · Chapman & Hall/CRC eBooks · doi:10.1201/b10440 · Cited by 259
Inference with dependent data using cluster covariance estimators
Bester, C. A., Conley, T. G., & Hansen, C. B. (2011) · Journal of Econometrics · doi:10.1016/j.jeconom.2011.01.007 · Cited by 258
The Effect of Two-Stage Sampling on Ordinary Least Squares Methods
Scott, A. J., & Holt, D. (1982) · Journal of the American Statistical Association · doi:10.1080/01621459.1982.10477897 · Cited by 243
Estimating multi-way error components models with unbalanced data structures
Davis, P. R. (2002) · Journal of Econometrics · doi:10.1016/s0304-4076(01)00087-2 · Cited by 236
The reduced form: A simple approach to inference with weak instruments
Chernozhukov, V., & Hansen, C. (2007) · Economics Letters · doi:10.1016/j.econlet.2007.11.012 · Cited by 234
Robust Inference with Clustered Data
Cameron, A. C., & Miller, D. L. (2010) · Statistics, a series of textbooks and monographs · doi:10.1201/b10440-2 · Cited by 221
Self-citationImplementing Weak-Instrument Robust Tests for a General Class of Instrumental-Variables Models
Finlay, K., & Magnusson, L. M. (2009) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0900900304 · Cited by 218
Schaffer, M. E., & Stillman, S. (2006) · RePEc: Research Papers in Economics · Cited by 202
Inference with Weak Instruments
Andrews, D. W. K. (2008) · Cambridge University Press eBooks · doi:10.1017/ccol0521871549.006 · Cited by 190
Solon, G., Haider, S. J., & Wooldridge, J. M. (2013) · National Bureau of Economic Research · doi:10.3386/w18859 · Cited by 159
Clustering, Spatial Correlations, and Randomization Inference
Barrios, T., Diamond, R., Imbens, G. W., & Kolesár, M. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2012.682524 · Cited by 146
Instrumental Variables Regression
Muller, C. D., Winship, C., & Morgan, S. L. (2014) · SAGE Publications Ltd eBooks · doi:10.4135/9781446288146.n13 · Cited by 142
Reworking Wild Bootstrap Based Inference for Clustered ErrorsPreprint
Webb, M. D., & Webb, M. D. (2014) · RePEc: Research Papers in Economics · doi:10.22004/ag.econ.274640 · Cited by 134
Marginal Modeling of Nonnested Multilevel Data using Standard Software
Miglioretti, D. L., & Heagerty, P. J. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwk020 · Cited by 134
Compensating Differentials for Gender-Specific Job Injury Risks
Hersch, J. (1998) · American Economic Review · Cited by 133
Kloek, T. (1981) · Econometrica · doi:10.2307/1911134 · Cited by 129
Rabe‐Hesketh, S., & Skrondal, A. (2012) · Cited by 128
Asymptotic Behavior of a t-Test Robust to Cluster Heterogeneity
Carter, A. V., Schnepel, K., & Steigerwald, D. G. (2016) · The Review of Economics and Statistics · doi:10.1162/rest_a_00639 · Cited by 121
Minimum Wages and On-the-Job TrainingPreprint
Pischke, J., & Acemoğlu, D. (2001) · SSRN Electronic Journal · doi:10.2139/ssrn.288292 · Cited by 118
The Effect of High School Matriculation Awards: Evidence from Randomized Trials
Angrist, J. D., & Lavy, V. (2002) · National Bureau of Economic Research · doi:10.3386/w9389 · Cited by 106
Identification and Inference for Econometric ModelsPreprint
Andrews, D. W. K., & Stock, J. H. (2005) · RePEc: Research Papers in Economics · Cited by 82
A Menu-Driven Facility for Sample-Size Calculations in Cluster Randomized Controlled Trials
Hemming, K., & Marsh, J. L. (2013) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x1301300109 · Cited by 82
Hausman, J. A., & Kuersteiner, G. M. (2008) · Journal of Econometrics · doi:10.1016/j.jeconom.2008.04.003 · Cited by 77
A Score Based Approach to Wild Bootstrap Inference
Kline, P., & Santos, A. (2010) · National Bureau of Economic Research · doi:10.3386/w16127 · Cited by 72
Multilevel and Longitudinal Modeling
Schoemann, A. M., Rhemtulla, M., & Little, T. D. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9780511996481.026 · Cited by 64
A general analysis of bias in the estimated standard errors of least squares coefficients
Greenwald, B. C. N. (1983) · Journal of Econometrics · doi:10.1016/0304-4076(83)90108-2 · Cited by 61
Robust Inference with Multi-way ClusteringPreprint
Gelbach, J. B., & Miller, D. (2009) · RePEc: Research Papers in Economics · Cited by 61
Self-citationRegression Analysis of Data from a Cluster Sample
Pfeffermann, D., & Nathan, G. (1981) · Journal of the American Statistical Association · doi:10.1080/01621459.1981.10477704 · Cited by 57
Inference about regression models from sample survey data
Shah, B. V., Holt, M. M., & Folsom, R. E. (1976) · Cited by 55
A PORTMANTEAU TEST FOR SERIALLY CORRELATED ERRORS IN FIXED EFFECTS MODELS
Inoue, A., & Solon, G. (2006) · Econometric Theory · doi:10.1017/s0266466606060385 · Cited by 53
Asymptotic inference from multi-stage samples
Bhattacharya, D. (2004) · Journal of Econometrics · doi:10.1016/j.jeconom.2004.01.002 · Cited by 51
Robust Standard Errors in Small Samples: Some Practical Advice
Imbens, G. W., & Kolesár, M. (2012) · National Bureau of Economic Research · doi:10.3386/w18478 · Cited by 50
Space and Time in Macroeconomic Panel Data: Young Workers and State-Level Unemployment RevisitedPreprint
Foote, C. L. (2007) · SSRN Electronic Journal · doi:10.2139/ssrn.1021697 · Cited by 42
The Precision of Instrumental Variables Estimates With Grouped DataPreprint
Shore-Sheppard, L. D. (1996) · RePEc: Research Papers in Economics · Cited by 30
Asymptotic Behavior of a t Test Robust to Cluster Heterogeneity
Steigerwald, D. G. (2013) · Cited by 22
Household responses to information on child nutrition: experimental evidence from Malawi
Fitzsimons, E., Malde, B., Mesnard, A., & Vera‐Hernández, M. (2017) · doi:10.1920/wp.ifs.2012.1207 · Cited by 15
Yoon, J., & Galvao, A. F. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2952580 · Cited by 6
Flagged first, then by citations
2 of the 83 references couldn’t be matched to a known paper.
Research works printed but not linked: 8
- Andrews, Donald W. K. and James H. Stock. 2007. 'Inference with Weak Instruments.' In Advances in Economics and Econometrics, Theory and Applications: Ninth World Congress of the Econometric Society , Vol. III, ed. Richard Blundell, Whitney K. Newey, and T. Persson, 122-173, Cambridge: Cambridge University Press.
- Arellano, Manuel 1987. 'Computing Robust Standard Errors for Within-Group Estimators.' Oxford Bulletin of Economics and Statistic 49(4): 431-434.
- Baum, Christopher F., Mark E. Schaffer, Steven Stillman. 2007. 'Enhanced Routines for Instrumental Variables/GMM Estimation and Testing.' The Stata Journal 7(4): 465-506.
- Hansen, Christian. 2007b. 'Generalized Least Squares Inference in Panel and Multi-level Models with Serial Correlation and Fixed Effects.' Journal of Econometrics 141(2): 597-620.
- Kish, Leslie. 1965. Survey Sampling. New York: John Wiley.
- Kish, Leslie, and Martin R. Frankel. 1974. 'Inference from Complex Surveys with Discussion.' Journal Royal Statistical Society B 36(1): 1-37.
- McCaffrey, Daniel F., Bell, Robert M., and Carsten H. Botts. 2001. 'Generalizations of Bias Reduced Linearization.' Proceedings of the Survey Research Methods Section , American Statistical Association.
- Thompson, Samuel. 2006. 'Simple Formulas for Standard Errors that Cluster by Both Firm and Time.' SSRN paper. http://ssrn.com/abstract=914002.
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Wing, C., Simon, K., & Bello-Gomez, R. A. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-013507 · Cited by 1,470
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“3 Following Petersen (2009) , Thompson (2011) and Cameron and Miller (2015) , we cluster by country (61 countries) and use a full set of”
When Should You Adjust Standard Errors for Clustering?
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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
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