A Random Attention Model

• 成果类型: Article
• 署名作者: Cattaneo, Matias D.; Ma, Xinwei; Masatlioglu, Yusufcan; Suleymanov, Elchin
• 署名单位: Princeton University; University of California System; University of California San Diego; University System of Maryland; University of Maryland College Park
• 刊物名称: JOURNAL OF POLITICAL ECONOMY
• ISSN/ISSBN: 0022-3808
• DOI: 10.1086/706861
• 发表日期: 2020
• 页码: 2796-2836
• 关键词: PARTIALLY IDENTIFIED MODELS; stochastic choice; confidence-intervals; revealed preference; rationality; inference

摘要

This paper illustrates how one can deduce preference from observed choices when attention is both limited and random. We introduce a random attention model where we abstain from any particular attention formation and instead consider a large class of nonparametric random attention rules. Our intuitive condition, monotonic attention, captures the idea that each consideration set competes for the decision maker's attention. We then develop a revealed preference theory and obtain testable implications. We propose econometric methods for identification, estimation, and inference for the revealed preferences. Finally, we provide a general-purpose software implementation of our estimation and inference results and simulation evidence.