We introduce two models of non-parametric random utility for demand systems: the stochastic absolute risk aversion (SARA) model, and the stochastic safety-first (SSF) model. In each model, individual-level heterogeneity is characterized by a distribution \pi\in\Pi of taste parameters, and heterogeneity across consumers is introduced using a distribution F over the distributions in \Pi. Demand is non-separable and heterogeneity is infinite-dimensional. Both models admit corner solutions. We consider two frameworks for estimation: a Bayesian framework in which F is known, and a hyperparametric  framework in which F is a member of a  parametric family. Our methods are illustrated by an application to a large  panel of scanner data on alcohol consumption.
We introduce two models of non-parametric random utility for demand systems: the stochastic absolute risk aversion (SARA) model, and the stochastic safety-first (SSF) model. In each model, individual-level heterogeneity is characterized by a distribution \pi\in\Pi of taste parameters, and heterogeneity across consumers is introduced using a distribution F over the distributions in \Pi. Demand is non-separable and heterogeneity is infinite-dimensional. Both models admit corner solutions. We consider two frameworks for estimation: a Bayesian framework in which F is known, and a hyperparametric (or empirical Bayesian) framework in which F is a member of a known parametric family. Our methods are illustrated by an application to a large U.S. panel of scanner data on alcohol consumption.
A
(or empirical Bayesian)
A
known
A
U.S.