Identification in econometric models maps prior assumptions and the data to information about a parameter of interest. The partial identification approach to inference recognizes that this process should not result in a binary answer that consists of whether the parameter is point identified....

We introduce a notion of median uncorrelation that is a natural extension of mean (linear) uncorrelation. A scalar random variable Y is median uncorrelated with a kdimensional random vector X if and only if the slope from an LAD regression of Y on X is zero. Using this simple definition, we...

This paper analyzes the identification question in censored panel data models, where the censoring can depend on both observable and unobservable variables in arbitrary ways. Under some general conditions, we derive the tightest sets on the parameter of interest. These sets (which can be...

We provide an econometric framework for estimating a game of simultaneous entry and pricing decisions while allowing for correlations between unobserved cost and demand shocks. We use our framework to account for selection in the pricing stage. We estimate the model using data from the US...

Given data on a large network, this paper provides a framework for identification of preferences under the assumption of pairwise stability of network links. Network data present difficulties for identification, especially when one allows for links between nodes in a network to be...