Correcting for Misclassied Binary Regressors Using Instrumental Variables

Steven Haider and Melvin Stephens

No 13593, IZA Discussion Papers from Institute of Labor Economics (IZA)

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 (search for similar items in EconPapers)
JEL-codes: C18 C26 (search for similar items in EconPapers)
Pages: 67 pages
Date: 2020-08
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published - published online in: Journal of Business & Economic Statistics , 21 Oct 2024

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Working Paper: Correcting for Misclassified Binary Regressors Using Instrumental Variables (2020)
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