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Nonparametric estimation of ATE and QTE: an application of Fractile Graphical Analysis


  • Montes-Rojas, G.


Nonparametric estimators for average and quantile treatment effects are constructed using Fractile Graphical Analysis, under the identifying assumption that selection to treatment is based on observable characteristics. The proposed method has two-steps: first, the propensity score is estimated, and second, a blocking estimation procedure using this estimate is used to compute treatment effects. In both cases, the estimators are proved to be consistent. Monte Carlo results show a better performance than other procedures based on the propensity score. Finally, these estimators are applied to a job training dataset.

Suggested Citation

  • Montes-Rojas, G., 2010. "Nonparametric estimation of ATE and QTE: an application of Fractile Graphical Analysis," Working Papers 10/06, Department of Economics, City University London.
  • Handle: RePEc:cty:dpaper:10/06

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    References listed on IDEAS

    1. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2006. "What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments," American Economic Review, American Economic Association, vol. 96(4), pages 988-1012, September.
    2. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
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