Misclassification of the dependent variable in a discrete-response setting

Misclassification of dependent variables in a discrete-response model causes inconsistent coefficient estimates when traditional estimation techniques (e.g., probit or logit) are used. A modified maximum likelihood estimator that corrects for misclassification is proposed. A semiparametric approach, which combines the maximum rank correlation estimator of Han (1987) (Journal of Econometrics 35, 303–316) with isotonic regression, allows for more general forms of misclassification than the maximum likelihood approach. The parametric and semiparametric estimation techniques are applied to a model of job change with two commonly used data sets, the Current Population Survey (CPS) and the Panel Study of Income Dynamics (PSID).