Journal article

The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators

  • Bodory, Hugo Department of Economics, University of St. Gallen, St. Gallen, Switzerland
  • Camponovo, Lorenzo Department of Economics, University of Surrey, Surrey, UK
  • Huber, Martin Department of Economics, University of Fribourg, Fribourg, Switzerland
  • Lechner, Michael Department of Economics, University of St. Gallen, St. Gallen, Switzerland
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    2020
Published in:
  • Journal of Business and Economic Statistics. - 2020, vol. 38, no. 1, p. 183-200
English This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity, effect heterogeneity, share of treated, and sample size. The results suggest that in general, theoretically justified bootstrap procedures (i.e., wild bootstrapping for pair matching and standard bootstrapping for “smoother” treatment effect estimators) dominate the asymptotic approximations in terms of coverage rates for both matching and weighting estimators. Most findings are robust across simulation designs and estimators.
Faculty
Faculté des sciences économiques et sociales
Department
Département d'économie politique
Language
  • English
Classification
Economics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/309266
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