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Correcting for Misclassied Binary Regressors Using Instrumental Variables

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  • Haider, Steven J.

    (Michigan State University)

  • Stephens Jr., Melvin

    (University of Michigan)

Abstract

Estimators that exploit an instrumental variable to correct for misclassification in a binary regressor typically assume that the misclassification rates are invariant across all values of the instrument. We show that this assumption is invalid in routine empirical settings. We derive a new estimator that is consistent when misclassification rates vary across values of the instrumental variable. In cases where identification is weak, our moments can be combined with bounds to provide a confidence set for the parameter of interest.

Suggested Citation

  • Haider, Steven J. & Stephens Jr., Melvin, 2020. "Correcting for Misclassied Binary Regressors Using Instrumental Variables," IZA Discussion Papers 13593, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp13593
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    References listed on IDEAS

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    Cited by:

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    2. Akanksha Negi & Digvijay Singh Negi, 2022. "Difference-in-Differences with a Misclassified Treatment," Papers 2208.02412, arXiv.org.

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    More about this item

    Keywords

    misclassification; measurement error; instrumental variables;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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