A Binary Choice Model with Sample Selection and Covariate-Related Misclassification

Misclassification of a binary response variable and nonrandom sample selection are data issues frequently encountered by empirical researchers. For cases in which both issues feature simultaneously in a data set, we formulate a sample selection model for a misclassified binary outcome in which the conditional probabilities of misclassification are allowed to depend on covariates. We use Bollinger and David’s pseudo-maximum likelihood technique to estimate the model. The performance of the estimator accounting for misclassification and sample selection is compared to that of estimators offering a partial correction in a Monte Carlo simulation study. An empirical example illustrates the proposed framework