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Small sample properties of matching with caliper

Author

Listed:
  • Paweł Strawiński

    () (University of Warsaw, Faculty of Economic Sciences)

Abstract

A caliper mechanism is a common tool used to prevent from inexact matches. The existing literature discusses asymptotic properties of matching with caliper. In this simulation study we investigate properties in small and medium sized samples. We show that caliper causes a significant bias of the ATT estimator and raises its variance in comparison to one-to-one matching.

Suggested Citation

  • Paweł Strawiński, 2012. "Small sample properties of matching with caliper," Working Papers 2012-13, Faculty of Economic Sciences, University of Warsaw.
  • Handle: RePEc:war:wpaper:2012-13
    as

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    File URL: http://www.wne.uw.edu.pl/inf/wyd/WP/WNE_WP79.pdf
    File Function: First version, 2012
    Download Restriction: no

    References listed on IDEAS

    as
    1. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    2. 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.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 605-654.
    4. Richard Blundell & Monica Costa Dias, 2000. "Evaluation methods for non-experimental data," Fiscal Studies, Institute for Fiscal Studies, vol. 21(4), pages 427-468, January.
    5. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute for the Study of Labor (IZA).
    6. Abadie, Alberto & Imbens, Guido W., 2011. "Bias-Corrected Matching Estimators for Average Treatment Effects," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 1-11.
    7. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    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. 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.
    10. 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.
    11. 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, Institute for the Study of Labor (IZA).
    12. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    propensity score matching; caliper; Monte Carlo experiment; finite sample properties;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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