On the Failure of the Bootstrap for Matching Estimators
Alberto Abadie () and
Guido Imbens ()
No 325, NBER Technical Working Papers from National Bureau of Economic Research, Inc
Matching estimators are widely used for the evaluation of programs or treatments. Often researchers use bootstrapping methods for inference. However, no formal justification for the use of the bootstrap has been provided. Here we show that the bootstrap is in general not valid, even in the simple case with a single continuous covariate when the estimator is root-N consistent and asymptotically normally distributed with zero asymptotic bias. Due to the extreme non-smoothness of nearest neighbor matching, the standard conditions for the bootstrap are not satisfied, leading the bootstrap variance to diverge from the actual variance. Simulations confirm the difference between actual and nominal coverage rates for bootstrap confidence intervals predicted by the theoretical calculations. To our knowledge, this is the first example of a root-N consistent and asymptotically normal estimator for which the bootstrap fails to work.
JEL-codes: C14 C21 C52 (search for similar items in EconPapers)
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Published as -Abadie, Alberto and Guido W. Imbens. "On the Failure of the Bootstrap for Matching Estimators." Econometrica 76, 6 (2008): 1537-1557.
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