论文标题
具有非参数口味不确定性和个人异质性的消费者理论
Consumer Theory with Non-Parametric Taste Uncertainty and Individual Heterogeneity
论文作者
论文摘要
我们介绍了需求系统非参数随机实用程序的两个模型：随机绝对风险规避（SARA）模型和随机安全 - 首先（SSF）模型。在每个模型中，个体级异质性的特征是分布$π\inπ$的味觉参数，并且在$π$中使用分布$ f $的分布$ f $引入了消费者的异质性。需求是不可分割的，异质性是无限维的。两种型号都允许转角解决方案。我们考虑估算的两个框架：贝叶斯框架，其中$ f $是已知的，以及一个超参数（或经验贝叶斯）框架，其中$ f $是已知参数家族的成员。我们的方法是通过应用于美国大量扫描仪数据饮酒数据的大量扫描仪数据来说明的。
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 $π\inΠ$ of taste parameters, and heterogeneity across consumers is introduced using a distribution $F$ over the distributions in $Π$. 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.
