@TechReport{iza:izadps:dp13593,
            	author={Haider, Steven J. and Jr., Melvin Stephens},
            	title={Correcting for Misclassied Binary Regressors Using Instrumental Variables},
            	year={2020},
	month={Aug},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={13593},
            	url={https://www.iza.org/publications/dp13593},
	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.},
	keywords={instrumental variables;measurement error;misclassification},
            }