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Inference on Local Average Treatment Effects for Misclassified Treatment

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  • Takahide Yanagi
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 non-classical 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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  • Takahide Yanagi, 2018. "Inference on Local Average Treatment Effects for Misclassified Treatment," Papers 1804.03349, arXiv.org.
    • Handle: RePEc:arx:papers:1804.03349
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    Cited by:

    1. 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.
    2. Haider, Steven J. & Stephens Jr., Melvin, 2020. "Correcting for Misclassied Binary Regressors Using Instrumental Variables," IZA Discussion Papers 13593, Institute of Labor Economics (IZA).
    3. Acerenza, Santiago & Ban, Kyunghoon & Kedagni, Desire, 2021. "Marginal Treatment Effects with Misclassified Treatment," ISU General Staff Papers 202106180700001132, Iowa State University, Department of Economics.
    4. 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.
    5. Kruse, Herman & Myhre, Andreas, 2021. "Early Retirement Provision for Elderly Displaced Workers," MPRA Paper 118689, University Library of Munich, Germany, revised 21 Sep 2023.
    6. Santiago Acerenza & Kyunghoon Ban & D'esir'e K'edagni, 2021. "Local Average and Marginal Treatment Effects with a Misclassified Treatment," Papers 2105.00358, arXiv.org, revised Sep 2024.
    7. Tommasi, Denni & Zhang, Lina, 2024. "Bounding program benefits when participation is misreported," Journal of Econometrics, Elsevier, vol. 238(1).
    8. Akanksha Negi & Digvijay Singh Negi, 2022. "Difference-in-Differences with a Misclassified Treatment," Papers 2208.02412, arXiv.org.
    9. Kruse, Herman & Myhre, Andreas, 2021. "Early Retirement Provision for Elderly Displaced Workers," MPRA Paper 109431, University Library of Munich, Germany.
    10. 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.
    11. 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

    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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