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Estimation of quantile treatment effects with Stata


  • Markus Frolich

    () (Universitat Mannheim)

  • Blaise Melly

    (Brown University)


In this article, we discuss the implementation of various estimators proposed to estimate quantile treatment effects. We distinguish four cases involv- ing conditional and unconditional quantile treatment effects with either exogenous or endogenous treatment variables. The introduced ivqte command covers four different estimators: the classical quantile regression estimator of Koenker and Bassett (1978, Econometrica 46: 33–50) extended to heteroskedasticity consis- tent standard errors; the instrumental-variable quantile regression estimator of Abadie, Angrist, and Imbens (2002, Econometrica 70: 91–117); the estimator for unconditional quantile treatment effects proposed by Firpo (2007, Econometrica 75: 259–276); and the instrumental-variable estimator for unconditional quantile treatment effects proposed by Fr ̈olich and Melly (2008, IZA discussion paper 3288). The implemented instrumental-variable procedures estimate the causal effects for the subpopulation of compliers and are only well suited for binary instruments. ivqte also provides analytical standard errors and various options for nonpara- metric estimation. As a by-product, the locreg command implements local linear and local logit estimators for mixed data (continuous, ordered discrete, unordered discrete, and binary regressors).

Suggested Citation

  • Markus Frolich & Blaise Melly, 2010. "Estimation of quantile treatment effects with Stata," Stata Journal, StataCorp LP, vol. 10(3), pages 423-457, September.
  • Handle: RePEc:tsj:stataj:v:10:y:2010:i:3:p:423-457
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    References listed on IDEAS

    1. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    2. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    3. Markus Frölich & Blaise Melly, 2013. "Unconditional Quantile Treatment Effects Under Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 346-357, July.
    4. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    5. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    6. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    7. repec:spo:wpecon:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    9. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    10. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
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