Robust Standard Errors in Small Samples: Some Practical Advice
In this paper we discuss the properties of confidence intervals for regression parameters based on robust standard errors. We discuss the motivation for a modification suggested by Bell and McCaffrey (2002) to improve the finite sample properties of the confidence intervals based on the conventional robust standard errors. We show that the Bell-McCaffrey modification is the natural extension of a principled approach to the Behrens-Fisher problem, and suggest a further improvement for the case with clustering. We show that these standard errors can lead to substantial improvements in coverage rates even for sample sizes of fifty and more. We recommend researchers calculate the Bell-McCaffrey degrees-of-freedom adjustment to assess potential problems with conventional robust standard errors and use the modification as a matter of routine.
|Date of creation:||Oct 2012|
|Publication status:||published as Guido W. Imbens & Michal Kolesár, 2016. "Robust Standard Errors in Small Samples: Some Practical Advice," Review of Economics and Statistics, vol 98(4), pages 701-712.|
|Note:||LS PE TWP|
|Contact details of provider:|| Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.|
Web page: http://www.nber.org
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- Chesher, Andrew & Austin, Gerard, 1991. "The finite-sample distributions of heteroskedasticity robust Wald statistics," Journal of Econometrics, Elsevier, vol. 47(1), pages 153-173, January.
- James H. Stock & Mark W. Watson, 2008.
"Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression,"
Econometric Society, vol. 76(1), pages 155-174, 01.
- James H. Stock & Mark W. Watson, 2006. "Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression," NBER Technical Working Papers 0323, National Bureau of Economic Research, Inc.
- Stephen G. Donald & Kevin Lang, 2007. "Inference with Difference-in-Differences and Other Panel Data," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 221-233, May.
- Hausman, Jerry & Palmer, Christopher, 2012. "Heteroskedasticity-robust inference in finite samples," Economics Letters, Elsevier, vol. 116(2), pages 232-235.
- Jerry A. Hausman & Christopher J. Palmer, 2011. "Heteroskedasticity-Robust Inference in Finite Samples," NBER Working Papers 17698, National Bureau of Economic Research, Inc.
- Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
- Cragg, John G, 1983. "More Efficient Estimation in the Presence of Heteroscedasticity of Unknown Form," Econometrica, Econometric Society, vol. 51(3), pages 751-763, May.
- MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
- James G. MacKinnon & Halbert White, 1983. "Some Heteroskedasticity Consistent Covariance Matrix Estimators with Improved Finite Sample Properties," Working Papers 537, Queen's University, Department of Economics.
- Bester, C. Alan & Conley, Timothy G. & Hansen, Christian B., 2011. "Inference with dependent data using cluster covariance estimators," Journal of Econometrics, Elsevier, vol. 165(2), pages 137-151. Full references (including those not matched with items on IDEAS)