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Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behaviour

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  • Martin Huber
  • Michael Lechner
  • Andreas Steinmayr

Abstract

Using a simulation design that is based on empirical data, a recent study by Huber et al. (J Econom 175:1–21, 2013 ) finds that distance-weighted radius matching with bias adjustment as proposed in Lechneret et al. (J Eur Econ Assoc 9:742–784, 2011 ) is competitive among a broad range of propensity score-based estimators used to correct for mean differences due to observable covariates. In this companion paper, we further investigate the finite sample behaviour of radius matching with respect to various tuning parameters. The results are intended to help the practitioner to choose suitable values of these parameters when using this method, which has been implemented in the software packages GAUSS, STATA and R. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Martin Huber & Michael Lechner & Andreas Steinmayr, 2015. "Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behaviour," Empirical Economics, Springer, vol. 49(1), pages 1-31, August.
    • Handle: RePEc:spr:empeco:v:49:y:2015:i:1:p:1-31
    DOI: 10.1007/s00181-014-0847-1
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    More about this item

    Keywords

    Propensity score matching; Radius matching; Selection on observables; Empirical Monte Carlo study; Finite sample properties; C21;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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