Bootstrap-Based Improvements for Inference with Clustered Errors
Jonah Gelbach and
Douglas Miller ()
No 344, NBER Technical Working Papers from National Bureau of Economic Research, Inc
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.
JEL-codes: C12 C15 C21 (search for similar items in EconPapers)
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Published as August 2008, Vol. 90, No. 3, Pages 414-427 Posted Online July 22, 2008. (doi:10.1162/rest.90.3.414)
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Journal Article: Bootstrap-Based Improvements for Inference with Clustered Errors (2008)
Working Paper: Bootstrap-Based Improvements for Inference with Clustered Errors (2006)
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