Working Paper: NBER ID: w15147
Authors: Jeremy T. Fox; Amit Gandhi
Abstract: 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 functions in both the first and second stages. This instrumental variables strategy applies equally to multinomial choice models with endogeneity.
Keywords: Heterogeneity; Economic Choice Models; Identification; Nonparametric; Utility Functions
JEL Codes: C14; C25; L0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| separability condition (D10) | identification of the distribution of random functions in nonadditive regression models (C46) |
| identification of the distribution of random functions in nonadditive regression models (C46) | identification of marginal effects (C51) |
| separability condition (D10) | identification of the distribution of utility functions in multinomial choice models (C35) |
| identification of the distribution of utility functions in multinomial choice models (C35) | computation of counterfactual measures and welfare analysis (D69) |
| instrumental variables strategy (C26) | identification of distributions in multinomial choice models with endogeneity (C35) |
| identification of the distribution of types (D39) | understanding the behavior of agents in economic contexts (D01) |
| identification results (F50) | policy implications (D78) |