Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/224035
Authors: 
Haider, Steven J.
Stephens Jr., Melvin
Year of Publication: 
2020
Series/Report no.: 
IZA Discussion Papers 13593
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.
Subjects: 
misclassification
measurement error
instrumental variables
JEL: 
C18
C26
Document Type: 
Working Paper

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