This paper derives sufficient conditions for nonparametric trans-formation models to be identified and develops estimators of the iden-tified components. Our nonparametric identification result is global, and allows for endogenous regressors. In particular, we show that a completeness assumption combined with conditional independence with respect to one of the regressors suffices for the model to be non-parametrically identified. The identification result is also constructive in the sense that it yields explicit expressions of the functions of in-terest. We show how natural estimators can be developed from these expressions, and analyze their theoretical properties. Importantly, it is demonstrated that the proposed estimator of the unknown transfor-mation function converges at a parametric rate. A test for whether a candidate regressor indeed satisfies the conditional independence assumption required for identification is developed. A Monte Carlo experiment illustrates the performance of our method in the context of a duration model with endogenous regressors. 1. Introduction. A