IDENTIFICATION OF REGRESSION MODELS WITH A MISCLASSIFIED AND ENDOGENOUS BINARY REGRESSOR

We study identification in nonparametric regression models with a misclassified and endogenous binary regressor when an instrument is correlated misclassification error. show that the function nonparametrically identified if one variable covariate satisfy following conditions. The instrumental corrects endogeneity; must be unobserved true underlying variable, uncorrelated error term outcome equation, but allowed to misclassification; this can of regressors also propose mixture-based framework for modeling heterogeneous treatment effects effect related observed another observable variable.