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Bootstrap-Based Improvements for Inference with Clustered Errors

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  • A. Colin Cameron

    (Department of Economics, University of California-Davis)

  • Jonah B. Gelbach

    (Department of Economics, University of Arizona)

  • Douglas L. Miller

    (Department of Economics, University of California-Davis)

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.

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Bibliographic Info

Article provided by MIT Press in its journal The Review of Economics and Statistics.

Volume (Year): 90 (2008)
Issue (Month): 3 (August)
Pages: 414-427

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Handle: RePEc:tpr:restat:v:90:y:2008:i:3:p:414-427

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