A note on the closed-form identification of regression models with a mismeasured binary regressor

Abstract

This note considers the identification of a nonparametric regression model with an unobserved 0–1 dichotomous regressor. The sample consists of a dependent variable and a 0–1 dichotomous proxy of the unobserved regressor. We obtain nonparametric identification of every element in the model as a closed-form function of the observed moments or densities. Our identification strategy does not require any additional sample information, such as instrumental variables or a secondary sample. The closed-form solution may be used to construct estimators of the unknowns.