Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 922

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Bootstrap-Based Improvements for Inference with Clustered Errors

Open access
Cameron, A. Colin, Gelbach, Jonah B., Miller, Douglas L.·2007·SSRN Electronic Journal
DOI 10.2139/ssrn.956890
Open this paper in CoChat — free account

Abstract

Microeconometrics researchers have increasingly realized the essential need to account for any within-group dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate cluster-robust or sandwich standard errors that permit quite general heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. In applications with few (5-30) clusters, standard asymptotic tests can over-reject considerably. We investigate more accurate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the much-cited di¤erences-in-di¤erences example of Bertrand, Mullainathan and Duflo (2004). In situations where standard methods lead to rejection rates in excess of ten percent (or more) for tests of nominal size 0:05, our methods can reduce this to five percent. In principle a pairs cluster bootstrap should work well, but in practice a Wild cluster bootstrap performs better.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2139/ssrn.956890

Cites

5 of 5 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Research works printed but not linked: 43

  1. Lavy Angrist, 2002, The E�ect of High School Matriculation Awards: Evidence from Randomized Trials, 10.3386/w9389 · DOI
  2. M Arellano, 1987, Computing Robust Standard Errors for Within-Group Estimators, Oxford Bulletin of Economics and Statistics, 49, 431, 10.1111/j.1468-0084.1987.mp49004006.x · DOI · Paper page
  3. M Baker, 1987, Occupational Gender Composition and Wages in Canada, Canadian Journal of Economics, 343, 10.1111/0008-4085.00078 · DOI
  4. R M Bell, 2002, Bias Reduction in Standard Errors for Linear Regression with Multi-Stage Samples, Survey Methodology, 169
  5. D Brownstone, 2001, The Bootstrap and Multiple Imputations: Harnessing Increased Computing Power for Improved Statistical Tests, 15, 129, 10.1257/jep.15.4.129 · DOI
  6. A Chesher, 1991, The Finite-Sample Distributions of Heteroskedasticity Robust Wald Statistics, Journal of Econometrics, 47, 153, 10.1016/0304-4076(91)90082-o · DOI
  7. A Chesher, 1987, The Bias of A Heteroskedasticity Consistent Covariance Matrix Estimator, Econometrica, 55, 1217, 10.2307/1911269 · DOI · Paper page
  8. R Davidson, 2001, The Wild Bootstrap, Tamed at Last, 10.1016/j.jeconom.2008.08.003 · DOI · Paper page
  9. R Davidson, 1999, The Size Distortion of the Bootstrap, Econometric Theory, 15, 361, 10.1017/s0266466699153040 · DOI · Paper page
  10. R Davidson, 2004, Econometric Theory and Methods
  11. S G Donald, 2004, Inference with Di�erences in Di�erences and Other Panel Data
  12. B Efron, 1979, Bootstrapping Methods: Another Look at the Jackknife, Annals of Statistics, 7, 1, 10.1214/aos/1176344552 · DOI · Paper page
  13. B Efron, 1981, Nonparametric Standard Errors and Con?dence Intervals, Canadian Journal of Statistics, 9, 139, 10.2307/3314608 · DOI · Paper page
  14. B Efron, 1987, Better Bootstrap Con?dence Intervals, 82, 171, 10.2307/2289144 · doi:10.2307/2289144 · Paper page
  15. B Efron, 1993, An Introduction to the Bootstrap, London, 10.1007/978-1-4899-4541-9 · DOI · Paper page
  16. B C Greenwald, 1983, A General Analysis of Bias in the Estimated Standard Errors of Least Squares Coe� cients, Journal of Econometrics, 22, 323, 10.1016/0304-4076(83)90108-2 · DOI
  17. J Gruber, 1994, Tax Incentives and the Decision to Purchase Health Insurance: Evidence from the Self-Employed, Quarterly Journal of Economics, 109, 701, 10.2307/2118419 · DOI · Paper page
  18. P Hall, 1992, The Bootstrap and Edgeworth Expansion, 10.1007/978-1-4612-4384-7 · DOI · Paper page
  19. J L Horowitz, 1997, Bootstrap Methods in Econometrics: Theory and Numerical Performance, Advances in Economics and Econometrics: Theory and Applications, Seventh World Congress, 3, 188, 10.1017/cbo9781139051996.007 · DOI
  20. J L Horowitz, 2001, The Bootstrap, Handbook of Econometrics, 3159, 10.1016/s1573-4412(01)05005-x · DOI · Paper page
  21. G Kauermann, 2001, A Note on the E� ciency of Sandwich Covariance Matrix Estimation, Journal of the American Statistical Association, 96, 1387, 10.1198/016214501753382309 · DOI · Paper page
  22. G K�zdi, 2004, Robust Standard Error Estimation in Fixed-E�ects Panel Models, Hungarian Statistical Review, Special Number, 9, 95, 10.2139/ssrn.596988 · DOI · Paper page
  23. L Kish, 1974, Inference from Complex Surveys with Discussion, Journal of the Royal Statistical Society, Series B, 36, 1
  24. T Kloek, 1981, OLS Estimation in a Model where a Microvariable is Explained by Aggregates and Contemporaneous Disturbances are Equicorrelated, Econometrica, 49, 205, 10.2307/1911134 · DOI
  25. K.-Y Liang, 1986, Longitudinal Data Analysis Using Generalized Linear Models, Biometrika, 73, 13, 10.1093/biomet/73.1.13 · DOI · Paper page
  26. R Y Liu, 1988, Bootstrap Procedures under Some Non-iid Models, 16, 1696, 10.1214/aos/1176351062 · DOI · Paper page
  27. J G Mackinnon, 2002, Bootstrap Inference in Econometrics, Canadian Journal of Economics, 35, 615, 10.1111/0008-4085.00147 · DOI · Paper page
  28. J G Mackinnon, 1985, Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties, Journal of Econometrics, 29, 305, 10.1016/0304-4076(85)90158-7 · DOI · Paper page
  29. D A Macpherson, 1995, Wages and Gender Occupation: Why Do Women's Jobs Pay Less, Journal of Labor Economics, 426, 10.1086/298381 · DOI · Paper page
  30. L A Mancl, A Covariance Estimator for GEE with Improved Finite-Sample Properties, Biometrics, 57, 126, 10.1111/j.0006-341x.2001.00126.x · DOI · Paper page
  31. E Mammen, 1993, Bootstrap and Wild Bootstrap for High Dimensional Linear Models, Annals of Statistics, 21, 255, 10.1214/aos/1176349025 · DOI · Paper page
  32. D F Mcca�rey, 2001, Generalizations of bias Reduced Linearization, Proceedings of the Survey Research Methods Section
  33. B R Moulton, 1986, Random Group E�ects and the Precision of Regression Estimates, Journal of Econometrics, 32, 385, 10.1016/0304-4076(86)90021-7 · DOI · Paper page
  34. B R Moulton, 1990, An Illustration of a Pitfall in Estimating the E�ects of Aggregate Variables on Micro Units, Review of Economics and Statistics, 72, 334, 10.2307/2109724 · DOI · Paper page
  35. W Pan, 2002, Small-sample Adjustments in Using the Sandwich Variance Estimator in Generalized Estimating Equation, Statistics in Medicine, 21, 1429, 10.1002/sim.1142 · DOI · Paper page
  36. T Rothenberg, 1988, Approximate Power Functions for Some Robust Tests of Regression Coe� cients, Econometrica, 56, 997, 10.2307/1911356 · DOI
  37. F F Satterthwaite, 1941, Synthesis of Variance, Psychometrika, 6, 309, 10.1007/bf02288586 · DOI · Paper page
  38. A J Scott, 1982, The E�ect of Two-Stage Sampling on Ordinary Least Squares Methods, Journal of the American Statistical Association, 77, 848, 10.1080/01621459.1982.10477897 · DOI · Paper page
  39. M Sherman, 1997, A Comparison Between Bootstrap Methods and Generalized Estimating Equations for Correlated Outcomes in Generalized Linear Models, Communications in Statistics, 10.1080/03610919708813417 · DOI · Paper page
  40. H White, 1980, A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity, Econometrica, 48, 817, 10.2307/1912934 · DOI · Paper page
  41. H White, 1984, Asymptotic Theory for Econometricians
  42. J M Wooldridge, 2003, Cluster-Sample Methods in Applied Econometrics, American Economic Review, 93, 133, 10.1257/000282803321946930 · DOI · Paper page
  43. C F G Wu, 1986, Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis, Annals of Statistics, 14

Cited by

See all 922

Metadata

Authors
Cameron, A. C., Gelbach, J. B., & Miller, D. L.
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2007
ISSN
1556-5068 (ISSN-L)
DOI
10.2139/ssrn.956890
OpenAlex
W2140968572
Cited by
922
References
5
Type
posted content
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

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.