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Estimation of relative average treatment effects with misclassification

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  • Fu, Lianyan
  • Gao, Wei
  • Shi, Ning-Zhong

Abstract

This paper identifies and estimates the relative average treatment effect in the presence of misclassification. We propose consistent estimators based on nonparametric methods. The simulation results reported illustrate the performance of the proposed estimators.

Suggested Citation

  • Fu, Lianyan & Gao, Wei & Shi, Ning-Zhong, 2011. "Estimation of relative average treatment effects with misclassification," Economics Letters, Elsevier, vol. 111(1), pages 95-98, April.
    • Handle: RePEc:eee:ecolet:v:111:y:2011:i:1:p:95-98
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Chen, Xiaohong & Hu, Yingyao & Lewbel, Arthur, 2008. "A note on the closed-form identification of regression models with a mismeasured binary regressor," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1473-1479, September.
    3. Hu, Yingyao, 2008. "Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution," Journal of Econometrics, Elsevier, vol. 144(1), pages 27-61, May.
    4. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
    5. Yingyao Hu & Arthur Lewbel, 2007. "Identifying the Returns to Lying When the Truth is Unobserved," Economics Working Paper Archive 540, The Johns Hopkins University,Department of Economics.
    6. Aprajit Mahajan, 2006. "Identification and Estimation of Regression Models with Misclassification," Econometrica, Econometric Society, vol. 74(3), pages 631-665, May.
    7. Das, M., 2005. "Instrumental variables estimators of nonparametric models with discrete endogenous regressors," Journal of Econometrics, Elsevier, vol. 124(2), pages 335-361, February.
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