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Matching using semiparametric propensity scores

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  • Steven Lehrer

    ()

  • 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

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Bibliographic Info

Article provided by Springer in its journal Empirical Economics.

Volume (Year): 44 (2013)
Issue (Month): 1 (February)
Pages: 13-45

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Handle: RePEc:spr:empeco:v:44:y:2013:i:1:p:13-45

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Related research

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

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References

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Cited by:
  1. 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.
  2. Apps, Patricia & Mendolia, Silvia & Walker, Ian, 2012. "The Impact of Pre-school on Adolescents' Outcomes: Evidence from a Recent English Cohort," IZA Discussion Papers 6971, Institute for the Study of Labor (IZA).
  3. 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.
  4. Jose C. GALDO & Jeffrey SMITH & Dan BLACK, 2008. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," Annales d'Economie et de Statistique, ENSAE, issue 91-92, pages 189-216.

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