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Controlling for overlap in matching

Author

Listed:
  • Paweł Strawiński

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

The overlap problem is crucial in propensity score matching. Recently, Crump et al. (2009) showed that trimming provides a simple and robust solution to the overlap problem. In this study, we use a simulation approach to show that trimming is inferior to the caliper mechanism. We show that in most cases, both techniques provide unbiased estimates, but trimming is less efficient.

Suggested Citation

  • Paweł Strawiński, 2013. "Controlling for overlap in matching," Working Papers 2013-10, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2013-10
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    File URL: http://www.wne.uw.edu.pl/inf/wyd/WP/WNE_WP95.pdf
    File Function: First version, 2013
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    References listed on IDEAS

    as
    1. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    2. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, IZA Network @ LISER.
    3. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    4. Lee, Myoung-jae, 2005. "Micro-Econometrics for Policy, Program and Treatment Effects," OUP Catalogue, Oxford University Press, number 9780199267699.
    5. Busso, Matias & DiNardo, John & McCrary, Justin, 2009. "New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators," IZA Discussion Papers 3998, IZA Network @ LISER.
    6. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    7. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    8. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    9. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
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    Cited by:

    1. Wenlian Gao & Feifei Zhu & Kai Chen, 2023. "The role of bank lenders in firm leverage adjustments," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(1), pages 63-97, February.

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    More about this item

    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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