Consumer Theory with Non-Parametric Taste Uncertainty and Individual Heterogeneity

TL;DR

This paper develops non-parametric models of consumer demand incorporating taste uncertainty and heterogeneity, providing new tools for demand analysis with complex individual differences.

Contribution

It introduces two novel non-parametric random utility models, SARA and SSF, that handle infinite-dimensional heterogeneity and corner solutions in demand systems.

Findings

Models successfully applied to U.S. scanner data on alcohol consumption.

Frameworks accommodate complex heterogeneity and non-separable demand.

Methodology advances demand estimation with non-parametric taste uncertainty.

Abstract

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.