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Bootstrap-Based Improvements for Inference with Clustered Errors

  • A. Colin Cameron
  • Jonah B. Gelbach
  • Douglas L. Miller

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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0344.

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Date of creation: Sep 2007
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Publication status: published as August 2008, Vol. 90, No. 3, Pages 414-427 Posted Online July 22, 2008. (doi:10.1162/rest.90.3.414)
Handle: RePEc:nbr:nberte:0344
Note: TWP
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