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A wild bootstrap algorithm for propensity score matching estimators

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    01.07.2016

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Inference
Propensity Score Matching Estimators
Wild Bootstrap
English We introduce a wild bootstrap algorithm for the approximation of the sampling distribution of pair or one-to-many propensity score matching estimators. Unlike the conventional iid bootstrap, the proposed wild bootstrap approach does not construct bootstrap samples by randomly resampling from the observations with uniform weights. Instead, it fixes the covariates and constructs the bootstrap approximation by perturbing the martingale representation for matching estimators. We also conduct a simulation study in which the suggested wild bootstrap performs well even when the sample size is relatively small. Finally, we provide an empirical illustration by analyzing an information intervention in rural development programs.
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Faculty
Faculté des sciences économiques et sociales et du management
Language
  • English
Classification
Economics
Series statement
  • Working Papers SES ; 470
License
License undefined
Identifiers
  • RERO DOC 261179
  • RERO R008465663
Persistent URL
https://folia.unifr.ch/unifr/documents/304923
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