Bootstrap-Based Improvements for Inference with Clustered Errors

Jonah B. Gelbach, Doug Miller and A. Cameron

No 128, Working Papers from University of California, Davis, Department of Economics

Abstract: Microeconometrics researchers have increasingly realized the essential need to account for any within-group dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate cluster-robust or sandwich standard errors that permit quite general heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. In applications with few (5-30) clusters, standard asymptotic tests can over-reject considerably. We investigate more accurate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the much-cited differences-in-differences example of Bertrand, Mullainathan and Duflo (2004). In situations where standard methods lead to rejection rates in excess of ten percent (ormore) for tests of nominal size 0.05, our methods can reduce this to five percent. In principle a pairs cluster bootstrap should work well, but in practice a Wild cluster bootstrap performs better.

Keywords: clustered errors; random effects; cluster robust; sandwich; bootstrap; bootstrap-t; clustered bootstrap; pairs bootstrap; wild bootstrap. (search for similar items in EconPapers)
JEL-codes: C12 C15 C21 (search for similar items in EconPapers)
Pages: 52
Date: 2006-07-13
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Related works:
Working Paper: Bootstrap-Based Improvements for Inference with Clustered Errors (2007)
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