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A sequential choice model for multiple discrete demand

DC Field Value Language
dc.contributor.authorLee, Sanghak-
dc.contributor.authorKim, Sunghoon-
dc.contributor.authorPark, Sungho-
dc.date.accessioned2022-10-05T04:16:48Z-
dc.date.available2022-10-05T04:16:48Z-
dc.date.created2022-08-17-
dc.date.issued2022-06-
dc.identifier.citationQuantitative Marketing and Economics, Vol.20 No.2, pp.141-178-
dc.identifier.issn1570-7156-
dc.identifier.urihttps://hdl.handle.net/10371/185413-
dc.description.abstractConsumer demand in a marketplace is often characterized to be multiple discrete in that discrete units of multiple products are chosen together. This paper develops a sequential choice model for such demand and its estimation technique. Given an inherently high-dimensional problem to solve, a consumer is assumed to simplify it to a sequence of one-unit choices, which eventually leads to a shopping basket of multiple discreteness. Our model and its estimation method are flexible enough to be extended to various contexts such as complementary demand, non-linear pricing, and multiple constraints. The sequential choice process generally finds an optimal solution of a convex problem (e.g., maximizing a concave utility function over a convex feasible set), while it might result in a sub-optimal solution for a non-convex problem. Therefore, in case of a convex optimization problem, the proposed model can be viewed as an econometrician's means for establishing the optimality of observed demand, offering a practical estimation algorithm for discrete optimization models of consumer demand. We demonstrate the strengths of our model in a variety of simulation studies and an empirical application to consumer panel data of yogurt purchase.-
dc.language영어-
dc.publisherKluwer Academic Publishers-
dc.titleA sequential choice model for multiple discrete demand-
dc.typeArticle-
dc.identifier.doi10.1007/s11129-022-09250-9-
dc.citation.journaltitleQuantitative Marketing and Economics-
dc.identifier.wosid000779222900001-
dc.identifier.scopusid2-s2.0-85127719936-
dc.citation.endpage178-
dc.citation.number2-
dc.citation.startpage141-
dc.citation.volume20-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorPark, Sungho-
dc.type.docTypeArticle-
dc.description.journalClass1-
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