UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Inference for Product Competition and Separable Demand

Smith, A; Rossi, P; Allenby, G; (2019) Inference for Product Competition and Separable Demand. Marketing Science , 38 (4) pp. 690-710. 10.1287/mksc.2019.1159. Green open access

[thumbnail of separable demand.pdf]
Preview
Text
separable demand.pdf - Accepted Version

Download (2MB) | Preview

Abstract

This paper presents a methodology for identifying groups of products that exhibit similar patterns in demand and responsiveness to changes in price using store-level sales data. We use the concept of economic separability as the basis for establishing similarity between products and build a weakly separable model of aggregate demand. A common issue with separable demand models is that the partition of products into separable groups must be known a priori, which severely shrinks the set of admissible substitution patterns. We develop a methodology that allows the partition to be an estimated model parameter. In particular, we specify a log-linear demand system in which weak separability induces equality restrictions on a subset of cross-price elasticity parameters. An advantage of our approach is that we are able to find groups of separable products rather than just test whether a given set of groups is separable. Our method is applied to two aggregate, store-level data sets. We find evidence that the separable structure of demand can be inconsistent with category labels, which has implications for optimal category marketing strategies.

Type: Article
Title: Inference for Product Competition and Separable Demand
Open access status: An open access version is available from UCL Discovery
DOI: 10.1287/mksc.2019.1159
Publisher version: https://doi.org/10.1287/mksc.2019.1159
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: price elasticities, category demand, dimension reduction, random partition models, Bayesian inference
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management
URI: https://discovery.ucl.ac.uk/id/eprint/10070811
Downloads since deposit
361Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item