A Random Attention Model: Identification, Estimation and Testing

揭示偏好理论是现代经济学和其他社会和行为科学的基石之一。然而，它假设家庭、政客或公司等决策者充分考虑所有选择，这在许多情况下是不太可能的。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。研究人员将把理论和计量经济学结合起来，以发展有限关注下的决策的可测试性理论和实证实施。这个项目的结果将帮助政策制定者和社会科学家更好地了解决策过程，并进一步制定更好的政策或干预措施，如劳动力市场、选举或行业。研究人员将首先开发一个随机有限注意力模型，与大类不同的决策过程兼容。该模型推广了经典的完全注意下的显性偏好理论，以及前人提出的确定性有限注意框架。基于这一模型，本项目将获得具体的可检验含义，并将利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。详细研究了非参数辨识、估计和推理方法。作为该项目的一部分，在随机有限注意模型的背景下，将提出并使用真实经验数据来获得未观察到的、不同种类的偏好的方法。

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.