provided ways to achieve identification of these primitive functions and distributions without imposing parametric restrictions … taken to develop nonparametric identification results in those models. …

unobserved variables allows identification analysis under mean and quantile independence restrictions on the distributions of …

. We use tools from random set theory to study identification in such models and provide a sharp characterization of the …

. We use tools from random set theory to study identification in such models and provide a sharp characterisation of the …

random sets defined on the space of unobserved variables. This allows identification analysis under mean and quantile …

In this article, we review quantile models with endogeneity. We focus on models that achieve identification through the … use of instrumental variables and discuss conditions under which partial and point identification are obtained. We discuss … the identification results of <xref ref-type="bibr" rid="B11">Chernozhukov & Hansen (2005). We illustrate the modeling …

. Conditioning on additional covariates, however, may be necessary for identification of these treatment effects. With conditional …

. We use tools from random set theory to study identification in such models and provide a sharp characterisation of the …

unobserved variables allows identification analysis under mean and quantile independence restrictions on the distributions of …

random sets defined on the space of unobserved variables. This allows identification analysis under mean and quantile …