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Inference on Local Average Treatment Effects for Misclassified Treatment

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  • YANAGI, Takahide
  • 柳, 貴英

Abstract

We develop point-identification for the local average treatment effect when the binary treatment contains a measurement error. The standard instrumental variable estimator is inconsistent for the parameter since the measurement error is nonclassical by construction. We correct the problem by identifying the distribution of the measurement error based on the use of an exogenous variable that can even be a binary covariate. The moment conditions derived from the identification lead to generalized method of moments estimation with asymptotically valid inferences. Monte Carlo simulations and an empirical illustration demonstrate the usefulness of the proposed procedure.
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  • YANAGI, Takahide & 柳, 貴英, 2017. "Inference on Local Average Treatment Effects for Misclassified Treatment," Discussion Papers 2017-02, Graduate School of Economics, Hitotsubashi University.
    • Handle: RePEc:hit:econdp:2017-02
    Note: This version: February 21, 2017 First version: January 27, 2017
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    Cited by:

    1. Santiago Acerenza & Kyunghoon Ban & D'esir'e K'edagni, 2021. "Marginal Treatment Effects with a Misclassified Treatment," Papers 2105.00358, arXiv.org, revised Apr 2023.
    2. Kruse, Herman & Myhre, Andreas, 2021. "Early Retirement Provision for Elderly Displaced Workers," MPRA Paper 118689, University Library of Munich, Germany, revised 21 Sep 2023.
    3. Tommasi, Denni & Zhang, Lina, 2024. "Bounding program benefits when participation is misreported," Journal of Econometrics, Elsevier, vol. 238(1).
    4. Akanksha Negi & Digvijay Singh Negi, 2022. "Difference-in-Differences with a Misclassified Treatment," Papers 2208.02412, arXiv.org.
    5. Denni Tommasi & Arthur Lewbel & Rossella Calvi, 2017. "LATE with Mismeasured or Misspecified Treatment: An application to Women's Empowerment in India," Working Papers ECARES ECARES 2017-27, ULB -- Universite Libre de Bruxelles.
    6. Steven J. Haider & Melvin Stephens Jr., 2020. "Correcting for Misclassified Binary Regressors Using Instrumental Variables," NBER Working Papers 27797, National Bureau of Economic Research, Inc.
    7. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    8. Kasahara, Hiroyuki & Shimotsu, Katsumi, 2022. "Identification Of Regression Models With A Misclassified And Endogenous Binary Regressor," Econometric Theory, Cambridge University Press, vol. 38(6), pages 1117-1139, December.
    9. Kruse, Herman & Myhre, Andreas, 2021. "Early Retirement Provision for Elderly Displaced Workers," MPRA Paper 109431, University Library of Munich, Germany.
    10. Wossen, Tesfamicheal & Abay, Kibrom A. & Abdoulaye, Tahirou, 2022. "Misperceiving and misreporting input quality: Implications for input use and productivity," Journal of Development Economics, Elsevier, vol. 157(C).

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    More about this item

    Keywords

    misclassification; instrumental variable; non-differential measurement error; nonparametric method; causal inference;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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