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摘要翻译：
本文阐述了当注意不仅是有限的而且是随机的时，人们如何从观察到的选择中推断出偏好。与以前的方法不同，我们引入了随机注意模型(RAM)，其中我们不考虑任何特定的注意形成，而是考虑一大类非参数随机注意规则。我们的模型强加了一个直观的条件，称为单调注意，它抓住了这样一个想法，即每个考虑集都在争夺决策者的注意。然后，我们在RAM中发展了揭示偏好理论，并获得了对可观察选择概率的精确可检验的含义。基于这些理论发现，我们提出了计量经济方法来识别、估计和推断决策者的偏好。为了说明我们的结果及其具体经验内容在特定环境中的适用性，我们还在二元选择问题的考虑集上的附加非参数假设下发展了揭示偏好理论和伴随的计量经济方法。最后，我们提供了估计和推断结果的通用软件实现，并通过仿真展示了它们的性能。
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英文标题：
《A Random Attention Model》
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作者：
Matias D. Cattaneo, Xinwei Ma, Yusufcan Masatlioglu, Elchin Suleymanov
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最新提交年份：
2019
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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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一级分类：Economics 经济学
二级分类：Theoretical Economics 理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
This paper illustrates how one can deduce preference from observed choices when attention is not only limited but also random. In contrast to earlier approaches, we introduce a Random Attention Model (RAM) where we abstain from any particular attention formation, and instead consider a large class of nonparametric random attention rules. Our model imposes one intuitive condition, termed Monotonic Attention, which captures the idea that each consideration set competes for the decision-maker's attention. We then develop revealed preference theory within RAM and obtain precise testable implications for observable choice probabilities. Based on these theoretical findings, we propose econometric methods for identification, estimation, and inference of the decision maker's preferences. To illustrate the applicability of our results and their concrete empirical content in specific settings, we also develop revealed preference theory and accompanying econometric methods under additional nonparametric assumptions on the consideration set for binary choice problems. Finally, we provide general purpose software implementation of our estimation and inference results, and showcase their performance using simulations.
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PDF链接：
https://arxiv.org/pdf/1712.03448
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