Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behaviour
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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.
KeywordsPropensity score matching Radius matching Selection on observables Empirical Monte Carlo study Finite sample properties
Martin Huber gratefully acknowledges financial support from the Swiss National Science Foundation grant PBSGP1_138770. We would like to thank Conny Wunsch (SEW) for her help in the early stages of the paper.
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