A Random Attention Model: Identification, Estimation and Testing

随机注意力模型：识别、估计和测试

• 批准号: 1628883
• 负责人: Matias Cattaneo
• 金额: $ 33.43万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1628883&HistoricalAwards=false
• 关键词: Random; Attention; Model; Identification; Estimation

Revealed preference theory is one of the cornerstones of modern economics and other social and behavioral sciences. However, it assumes that decision makers such as households, politicians or firms take all choices into full consideration, which is unlikely in many situations. This project thus aims to improve upon revealed preference theory by allowing decision makers to pay limited attention to their choices. The investigators will combine both theory and econometrics to develop testable theory and empirical implementations of decision-making under limited attention. The results from this project will help policy makers and social scientists to better understand the decision-making process and further develop better policies or interventions in different settings such as labor markets, elections, or industries.The investigators will first develop a random limited attention model, compatible with a large class of different decision-making process. This model will generalize classical revealed preference theory under full attention as well as previously introduced deterministic limited attention frameworks. Based on this model, this project will obtain concrete testable implications, and will employ modern econometrics and statistical techniques to construct inference procedures with good finite samples properties. Nonparametric identification, estimation and inference methods will be studied in detail. As part of this project, methods for eliciting unobserved, heterogeneous preferences in the context of the random limited attention model will be proposed and implemented using real empirical data.

显性偏好理论是现代经济学和其他社会行为科学的基石之一。然而，它假设家庭、政治家或公司等决策者充分考虑了所有选择，这在许多情况下是不可能的。因此，这个项目旨在通过允许决策者对他们的选择给予有限的关注来改进揭示偏好理论。研究人员将结合理论和计量经济学发展可测试的理论和实证实施决策在有限的关注。该项目的研究结果将有助于政策制定者和社会科学家更好地理解决策过程，并进一步在劳动力市场、选举或行业等不同环境中制定更好的政策或干预措施。研究者将首先建立一个随机的有限注意模型，该模型兼容于大类不同的决策过程。该模型将推广经典的充分注意下的显性偏好理论以及之前引入的确定性有限注意框架。基于该模型，本项目将获得具体的可测试含义，并将采用现代计量经济学和统计技术来构建具有良好有限样本性质的推理程序。将详细研究非参数识别、估计和推理方法。作为该项目的一部分，将提出在随机有限注意模型背景下引出未观察到的异质性偏好的方法，并使用真实的经验数据进行实施。

项目成果

A Random Attention Model

随机注意力模型

• DOI: 10.1086/706861
• 发表时间: 2020
• 期刊: Journal of Political Economy
• 影响因子: 8.2
• 作者: Cattaneo, Matias D.;Ma, Xinwei;Masatlioglu, Yusufcan;Suleymanov, Elchin
• 通讯作者: Suleymanov, Elchin