摘要翻译：
我们建立了一个离散选择的动态模型，将对等效应纳入随机考虑集。我们刻画了均衡行为并研究了模型的经验内容。在我们的设置中，朋友选择的变化会影响考虑集的分布。我们利用这种变化来恢复偏好、注意力机制和网络连接的排名。这些非参数识别结果允许人们之间不受限制的异质性，并且不依赖于协变量或可用选项集的变化。我们的方法导致了一个最大似然估计器，在模拟中表现良好。我们将我们的结果应用于一个实验数据集，该数据集被设计用来研究视觉注意焦点。
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英文标题：
《Peer Effects in Random Consideration Sets》
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作者：
Nail Kashaev, Natalia Lazzati
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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 develop a dynamic model of discrete choice that incorporates peer effects into random consideration sets. We characterize the equilibrium behavior and study the empirical content of the model. In our setup, changes in the choices of friends affect the distribution of the consideration sets. We exploit this variation to recover the ranking of preferences, attention mechanisms, and network connections. These nonparametric identification results allow unrestricted heterogeneity across people and do not rely on the variation of either covariates or the set of available options. Our methodology leads to a maximum-likelihood estimator that performs well in simulations. We apply our results to an experimental dataset that has been designed to study the visual focus of attention.
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PDF链接：
https://arxiv.org/pdf/1904.06742