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 nonparametrically 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, it shows that point identification can be attained even when the consideration changes with preferences as well as with (many of the) product characteristics, without the need of panel data or menu variation. We compare our model with standard models in the Logit family (Mixed Logit and Nested Logit) and with a pure random coefficients model. We illustrate the properties of our approach and its computational advantages in simulations.
Bio: Francesca Molinari is the H. T. Warshow and Robert Irving Warshow Professor of Economics and Professor of Statistics and Data Science at Cornell University. She received her Ph.D. from the Department of Economics at Northwestern University, after obtaining a BA and Masters in Economics at the Università degli Studi di Torino (Italy). Her research interests are in econometrics, both theoretical and applied. Her theoretical work is concerned with the study of identification problems, and with proposing new methods for statistical inference in partially identified models. In her applied work she has focused primarily on the analysis of decision making under risk and uncertainty. She has worked on estimation of risk preferences using market level data, and on the analysis of individuals' probabilistic expectations using survey data. Molinari is a Fellow of the Econometric Society and a Fellow of the International Association for Applied Econometrics. She is currently serving as Joint Managing Editor at the Review of Economic Studies and as Board of Editors member at the Journal of Economic Literature.