Regression models for choice-based samples with misclassification in the response variable

In this paper, we provide a general framework to deal with the presence of misclassification in the response variable in choice-based samples. The contaminated data sampling distribution is written as a function of the error-free conditional distribution of the dependent variable given the covariates and the conditional misclassification probabilities of the observable variable of interest given: its latent values. We propose an extension of Imbens' (Econometrica 60 (1992),1187) efficient generalized method of moments to estimate this model and outline a specification test to detect the presence of this sort of measurement error. The performance of both the estimators and the test is investigated in a Monte Carlo simulation study, which shows very encouraging results. (C) 2002 Elsevier Science S.A. All rights reserved.