The Probit Choice Model Under Sequential Search with an Application to Online Retailing

The authors develop a probit choice model under optimal sequential search and apply it to the study of aggregate demand of consumer durable goods. In their joint model of search and choice, the authors derive an expression for the probability of choice that obeys the full set of restrictions imposed by optimal sequential search. Estimation of this 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. The authors 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. The authors 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.