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

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  • Huber, Martin
  • Lechner, Michael
  • Steinmayr, Andreas

Abstract

Using a simulation design that is based on empirical data, a recent study by Huber, Lechner and Wunsch (2012) finds that distance-weighted radius matching with bias adjustment as proposed in Lechner, Miquel and Wunsch (2011) is competitive among a broad range of propensity score-based estimators used to correct for mean differences due to observable covariates. In this 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 as "radiusmatch" command in the software packages GAUSS, STATA and the R package "radiusmatching".

Suggested Citation

  • Huber, Martin & Lechner, Michael & Steinmayr, Andreas, 2012. "Radius matching on the propensity score with bias adjustment: finite sample behaviour, tuning parameters and software implementation," Economics Working Paper Series 1226, University of St. Gallen, School of Economics and Political Science.
    • Handle: RePEc:usg:econwp:2012:26
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    File URL: https://ux-tauri.unisg.ch/RePEc/usg/econwp/EWP-1226.pdf
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    More about this item

    Keywords

    Propensity score matching; radius matching; selection on observables; empirical Monte Carlo study; finite sample properties;
    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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