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Bootstrap-Based Improvements for Inference with Clustered Errors

A. Colin Cameron¹; Jonah B. Gelbach²; Douglas L. Miller¹

¹ University of California, Davis · ² University of Arizona

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.

DOI

10.1162/rest.90.3.414

Volume

90 (3)

Pages

414-427

Language

en

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