Abstract

http://ssrn.com/abstract=912426
 
 

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On the Failure of the Bootstrap for Matching Estimators


Alberto Abadie


Harvard University - Harvard Kennedy School (HKS); National Bureau of Economic Research (NBER)

Guido W. Imbens


Stanford Graduate School of Business

June 2006

NBER Working Paper No. t0325

Abstract:     
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.

Number of Pages in PDF File: 39

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Date posted: June 29, 2006  

Suggested Citation

Abadie, Alberto and Imbens, Guido W., On the Failure of the Bootstrap for Matching Estimators (June 2006). NBER Working Paper No. t0325. Available at SSRN: http://ssrn.com/abstract=912426

Contact Information

Alberto Abadie (Contact Author)
Harvard University - Harvard Kennedy School (HKS) ( email )
79 John F. Kennedy Street
Cambridge, MA 02138
United States
617-496-4547 (Phone)
617-495-2575 (Fax)
National Bureau of Economic Research (NBER)
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Guido W. Imbens
Stanford Graduate School of Business ( email )
518 Memorial Way
Stanford, CA 94305-5015
United States

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