Bootstrap-Based Improvements for Inference with Clustered Errors

64 Pages Posted: 26 Sep 2007

See all articles by A. Colin Cameron

A. Colin Cameron

affiliation not provided to SSRN

Doug Miller

affiliation not provided to SSRN

Douglas L. Miller

University of California, Davis - Department of Economics

Date Written: September 2007

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 (5-30) 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 ten percent using standard methods can be reduced to the nominal size of five percent using our methods.

Suggested Citation

Cameron, A. Colin and Miller, Doug and Miller, Douglas L., Bootstrap-Based Improvements for Inference with Clustered Errors (September 2007). NBER Working Paper No. t0344, Available at SSRN: https://ssrn.com/abstract=1016963

A. Colin Cameron (Contact Author)

affiliation not provided to SSRN

No Address Available

Doug Miller

affiliation not provided to SSRN

Douglas L. Miller

University of California, Davis - Department of Economics ( email )

One Shields Drive
Davis, CA 95616-8578
United States
530-752-8490 (Phone)

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