Identification and estimation of endogenous selection models in the presence of misclassification errors

Abstract

This paper shows the semi-parametric identification and estimation of sample selection models when the primary equation contains a discrete mismeasured endogenous covariate. Assuming that appropriate instruments for the presence of endogeneity are available, I apply a control function approach to remove the possible endogeneity. Based on the conditional mean independence between the model error and the selection error, the model can be regarded as a semi-parametric regression model with a discrete mismeasured covariate, thereby permitting a non-classical measurement error. Additional identification assumptions include monotonicity restrictions on the regression function and an empirical testable rank condition. I then use the identification result to construct a sieve maximum likelihood estimation estimator to estimate the model parameters consistently and recover the selection rule and joint probabilities of the accurately measured endogenous variable and the mismeasured observed variable. The proposed estimation method allows for a rather flexible functional form of the mismeasured endogenous covariate, requires only one valid instrument to control for both endogeneity and measurement errors for the variable of interest, and imposes no distribution assumptions on the selection rule.