TY  - RPRT
        AU  - Haider, Steven J.
AU  - Jr., Melvin Stephens
        TI  - Correcting for Misclassied Binary Regressors Using Instrumental Variables
        PY  - 2020/Aug/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 13593
        UR  - https://www.iza.org/index.php/publications/dp13593
        AB  - 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.
        KW  - instrumental variables
KW  - measurement error
KW  - misclassification
        ER  -