%0 Report
        %A Haider, Steven J.
%A Jr., Melvin Stephens
        %T Correcting for Misclassied Binary Regressors Using Instrumental Variables
        %D 2020
        %8 2020 Aug
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 13593
        %U https://www.iza.org/publications/dp13593
        %X 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.
        %K instrumental variables
%K measurement error
%K misclassification