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Matching using semiparametric propensity scores

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  • Steven Lehrer

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  • Gregory Kordas

Abstract

This paper considers the application of semiparametric methods to estimate propensity scores or probabilities of program participation, which are central to certain program evaluation methods. To evaluate the practical benefits, we first conduct a Monte Carlo study. Second, we use data from the NSW experiment, CPS, and PSID. We compare treatment effect and evaluation bias estimates using propensity scores estimated from parametric logit, semiparametric single index, and semiparametric binary quantile regression models. Our results suggest that it is important to account for very general forms of heterogeneity in (semiparametric) estimation of the propensity score, particularly when the treatment effects vary in an unsystematic manner with the true propensity score. Copyright Springer-Verlag 2013

Suggested Citation

  • Steven Lehrer & Gregory Kordas, 2013. "Matching using semiparametric propensity scores," Empirical Economics, Springer, vol. 44(1), pages 13-45, February.
  • Handle: RePEc:spr:empeco:v:44:y:2013:i:1:p:13-45
    DOI: 10.1007/s00181-012-0591-3
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    File URL: http://hdl.handle.net/10.1007/s00181-012-0591-3
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Bernd Fitzenberger & Michael Lechner & Jeffrey Smith, 2013. "Estimation of treatment effects: recent developments and applications," Empirical Economics, Springer, vol. 44(1), pages 1-11, February.
    2. Apps, Patricia & Mendolia, Silvia & Walker, Ian, 2013. "The impact of pre-school on adolescents’ outcomes: Evidence from a recent English cohort," Economics of Education Review, Elsevier, vol. 37(C), pages 183-199.
    3. Christos Makridis, 2015. "The Elasticity of Air Quality: Evidence from Millions of Households Across the United States," Discussion Papers 15-020, Stanford Institute for Economic Policy Research.
    4. Miana Plesca & Jeffrey Smith, 2007. "Evaluating multi-treatment programs: theory and evidence from the U.S. Job Training Partnership Act experiment," Empirical Economics, Springer, vol. 32(2), pages 491-528, May.
    5. repec:eee:quaeco:v:67:y:2018:i:c:p:45-50 is not listed on IDEAS
    6. Jose C. Galdo & Jeffrey Smith & Dan Black, 2008. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," Annals of Economics and Statistics, GENES, issue 91-92, pages 189-216.

    More about this item

    Keywords

    Propensity score matching; Treatment effects; Semiparametric binary choice estimators; Heterogeneity; Binary quantile regression; C14; C81; C99; H53; I38;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • J00 - Labor and Demographic Economics - - General - - - General

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