  We establish nonparametric identification in a class of so-called index
models using a novel approach that relies on general topological results. Our
proof strategy requires substantially weaker conditions on the functions and
distributions characterizing the model compared to existing strategies; in
particular, it does not require any large support conditions on the regressors
of our model. We apply the general identification result to additive random
utility and competing risk models.
