Inference on Randomly Censored Regression Models Using Conditional Moment Inequalities∗

Under a conditional quantile restriction, randomly censored regression models can be written in terms of conditional moment inequalities. We study the identified features of these moment inequalities with respect to the regression parameters. These inequalities restrict the parameters to a set. We then show regular point identification can be achieved under a set of interpretable sufficient conditions. Our results generalize existing work on randomly censored models in that we allow for covariate dependent censoring, endogenous censoring and endogenous regressors. We then provide a simple way to convert conditional moment inequalities into unconditional ones while preserving the informational content. Our method obviates the need for nonparametric estimation, which would require the selection of smoothing parameters and trimming procedures. Maintaining the point identification conditions, we propose a quantile minimum distance estimator which converges at the parametric rate and has an asymptotically normal distribution. A small scale simulation study and an application using drug relapse data demonstrate satisfactory finite sample performance.