Bootstrap-Based Improvements for Inference with Clustered Errors

A. Cameron, Jonah Gelbach and Douglas Miller

The Review of Economics and Statistics, 2008, vol. 90, issue 3, 414-427

Abstract: Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (five to thirty) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo, and Mullainathan (2004). Rejection rates of 10% using standard methods can be reduced to the nominal size of 5% using our methods. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Date: 2008
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Working Paper: Bootstrap-Based Improvements for Inference with Clustered Errors (2007)
Working Paper: Bootstrap-Based Improvements for Inference with Clustered Errors (2006)
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