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Title:Bootstrap-based improvements for inference with clustered errors PDF Logo
Authors:Cameron, A. Colin
Miller, Douglas L.
Gelbach, Jonah B.
Issue Date:2006
Series/Report no.:Working papers // University of California, Department of Economics 06,21
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 (or more) 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.
JEL:C12
C15
C21
Document Type:Working Paper
Appears in Collections:Working Papers, Department of Economics, UC Davis

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