Correcting for Misclassied Binary Regressors Using Instrumental Variables

67 Pages Posted: 17 Aug 2020

See all articles by Steven Haider

Steven Haider

Michigan State University

Melvin Stephens

University of Michigan at Ann Arbor - Department of Economics; National Bureau of Economic Research (NBER)

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: misclassification, measurement error, instrumental variables

JEL Classification: C18, C26

Suggested Citation

Haider, Steven and Stephens, Melvin, Correcting for Misclassied Binary Regressors Using Instrumental Variables. IZA Discussion Paper No. 13593, Available at SSRN: https://ssrn.com/abstract=3674314 or http://dx.doi.org/10.2139/ssrn.3674314

Steven Haider (Contact Author)

Michigan State University

Agriculture Hall
East Lansing, MI 48824-1122
United States

Melvin Stephens

University of Michigan at Ann Arbor - Department of Economics ( email )

Ann Arbor, MI
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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