The probit choice model under sequential search with an application to online retailing

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 a semi-closed form expression for the probability of choice that obeys the full set of restrictions imposed by optimal sequential search. Our joint model leads to a partial simulation-based estimation that avoids high-dimensional integrations in the evaluation of choice probabilities and that is particularly attractive when search sets are large. We illustrate the advantages of our approach using 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.