This paper provides a framework for identifying preferences in a large network where links are pairwise stable. Network formation models present difficulties for identifi cation, especially when links can be interdependent: e.g., when indirect connections matter. We show how one can use the...

In complicated/nonlinear parametric models, it is generally hard to know whether the model parameters are point identified. We provide computationally attractive procedures to construct confidence sets (CSs) for identified sets of full parameters and of subvectors in models defined through a...

We explore inference on regression coefficients in semiparametric multinomial response models. We consider cross-sectional, and both static and dynamic panel settings where we focus throughout on inference under sufficient conditions for point identification. The approach to identification uses...