Affiliation: Cornell University
Title: "Identification and Inference for Pure Random Coefficients Models with Limited Consideration," Levon Barseghyan and Francesca Molinari
Abstract:
This paper proposes a discrete choice model where decision makers differ both in their
preferences as well as in the products they consider – their consideration sets. The
paper shows how to point identify both the preference distribution and the consideration
set formation mechanism under a wide range of assumptions and consideration
set formation mechanisms, using cross-sectional data on choices and attributes. In
particular, we show that point identification can be attained even when consideration
depends on preferences as well as on (many of the) product characteristics, without
the need of panel data or menu variation. We propose a sieve-likelihood estimator
for the nonparametric distribution of preferences and consideration sets, and obtain
its asymptotic properties. We compare our model with models in the Logit family.
We illustrate the properties of our approach and its computational advantages in large
scale simulations and an empirical application.