Search with learning in the retail gasoline market
估计了一个消费者在驾驶途中学习汽油价格分布的最优搜索模型，利用交通信息和加油站数据揭示学习对搜索行为的关键作用，并解释不对称成本传导现象。
Abstract This article estimates a model of optimal search where consumers learn the distribution of gasoline prices during their driving trips. Our model incorporates traffic information and leverages this ordered search environment to recover parameters of the search and learning process using only station‐level price and market share data. We find that learning is a crucial component of search in this market. Consumers' prior beliefs regularly deviate from the true price distribution but are updated quickly following each new price observation. Counterfactuals reveal how these learning dynamics generate asymmetric search patterns commonly associated with asymmetric cost pass‐through .