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摘要翻译：
本文介绍了需求系统的两种非参数随机效用模型：随机绝对风险厌恶(SARA)模型和随机安全优先(SSF)模型。在每个模型中，个体级别的异质性由口味参数的分布$\pi\In\pi$来表征，而跨消费者的异质性是通过在$\pi$中的分布上使用分布$f$来引入的。需求是不可分的，异质性是无限维的。两种模型都承认拐角解。我们考虑了两个估计框架：一个贝叶斯框架，其中$f$是已知的，另一个超参数（或经验贝叶斯）框架，其中$f$是已知参数族的成员。我们的方法通过应用于美国一个关于酒精消费的大型扫描仪数据面板来说明。
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英文标题：
《Consumer Theory with Non-Parametric Taste Uncertainty and Individual
Heterogeneity》
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作者：
Christopher Dobronyi and Christian Gouri\'eroux
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最新提交年份：
2021
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/2010.13937
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