Correcting for Misclassified Binary Regressors Using Instrumental Variables
Steven J. Haider, Melvin Stephens · National Bureau of Economic Research · 2020
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.