We establish nonparametric identification in a class of so-called index models by using a novel approach that relies on general topological results. Our proof strategy requires substantially weaker conditions on the functions and distributions characterising the model than those required by 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.

中文翻译：

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识别一类索引模型：一种拓扑方法

通过使用依赖于一般拓扑结果的新颖方法，我们在一类所谓的索引模型中建立了非参数识别。我们的证明策略需要比现有策略所需的条件弱得多的条件来表征模型的功能和分布。特别是，它不需要我们模型的回归器具有任何大支持条件。我们将一般识别结果应用于加性随机效用和竞争风险模型。