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

Jun B. Kim, P. Albuquerque, Bart Bronnenberg

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)3911-3929
JournalManagement Science
Volume63
Issue number11
Early online date26 Oct 2016
DOIs
Publication statusPublished - Nov 2017

Fingerprint

Online retailing
Probit
Sequential search
Choice models
Amazon
Consumer durables
Prediction
Substitution
Evaluation
Inference
Simulation-based estimation
Aggregate demand
Product category

Keywords

  • Optimal sequential search
  • discrete choice
  • consumer heterogeneity
  • aggregate demand models
  • information economics
  • market structure

Cite this

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The probit choice model under sequential search with an application to online retailing. / Kim, Jun B.; Albuquerque, P.; Bronnenberg, Bart.

In: Management Science, Vol. 63, No. 11, 11.2017, p. 3911-3929.

Research output: Contribution to journalArticleScientificpeer-review

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AB - 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.

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