The Probit Choice Model Under Sequential Search with an Application to Online Retailing
提出了一个在最优序贯搜索下的Probit选择模型，用于分析消费者耐用品加总需求，并利用亚马逊摄像机产品数据证明联合使用搜索和选择数据比仅用搜索数据能更准确推断和预测消费者替代模式。
We develop a probit choice model under optimal sequential search and apply it to the study of aggregate demand of consumer durable goods. In our joint model of search and choice, we derive an expression for the probability of choice that obeys the full set of restrictions imposed by optimal sequential search. Estimation of our partially analytic model avoids the computation of high-dimensional integrations in the evaluation of choice probabilities, which is of particular benefit when search sets are large. We demonstrate the advantages of our approach in data experiments and apply the model to aggregate search and choice data from the camcorder product category at Amazon.com. We show that the joint use of search and choice data provides better performance in terms of inferences and predictions than using search data alone and leads to realistic estimates of consumer substitution patterns. Data, as supplemental material, are available at https://doi.org/10.1287/mnsc.2016.2545 . This paper was accepted by Pradeep Chintagunta, marketing.