NBER Publications by Amit Gandhi

We show how to nonparametrically identify the distribution that characterizes heterogeneity among agents in a general class of structural choice models. We introduce an axiom that we term separability and prove that separability of a structural model ensures identification. The main strength of separability is that it makes verifying the identification of nonadditive models a tractable task because it is a condition that is stated directly in terms of the choice behavior of agents in the model. We use separability to prove several new results. We prove the identification of the distribution of random functions and marginal effects in a nonadditive regression model. We also identify the distribution of utility functions in the multinomial choice model. Finally, we extend 2SLS to have random...