Copula-Based Simultaneous Approach to Multivariate Alternative Choice and Quantity Choice

43 Pages Posted: 16 Dec 2011  

Duk Bin Jun

Korea Advanced Institute of Science and Technology (KAIST) - College of Business

Chul Kim

KAIST Business School

Date Written: November 1, 2011

Abstract

This paper aims to examine correlations in shopping situations. First, there is a certain amount of correlation between alternative choices. Specifically, the alternatives from different categories but from a same brand might be purchased together. Second, alternative choice and quantity choice could be correlated each other. A consumer tends to purchase tooth paste with large amount, but hand cream with small amount. Third, quantity choices could be correlated each other. The purchased quantity of fabric softener should depend on the purchased quantity of laundry detergent. To explain these correlations, the model must deal with multivariate incidence and quantity outcomes. Therefore, we developed a new copula-based approach to simultaneously deal with them, so that it could directly control and capture the correlations. Also, we found that if the copula function is a multivariate-FGM copula, then the likelihood is closed form that is easy to estimate. We apply this model to IRI scanner panel data and estimate the model by using Bayesian method. In this data set, we could find strong dependencies between alternative choices, between alternative choice and quantity choice, and between quantity choices. In addition, more efficient promotion strategy of two products from a same brand but different categories is drawn from our model.

Keywords: Copula, Choice Model, Multiple Choice, Multivariate Choice, Quantity Choice, Promotion Strategy, Direct Utility

Suggested Citation

Jun, Duk Bin and Kim, Chul, Copula-Based Simultaneous Approach to Multivariate Alternative Choice and Quantity Choice (November 1, 2011). KAIST Business School Working Paper Series No. 2011-005. Available at SSRN: https://ssrn.com/abstract=1972584 or http://dx.doi.org/10.2139/ssrn.1972584

Duk Bin Jun (Contact Author)

Korea Advanced Institute of Science and Technology (KAIST) - College of Business ( email )

85 Hoegiro, Dongdaemoon-gu
Seoul, 130-722
Korea, Republic of (South Korea)

Chul Kim

KAIST Business School ( email )

85 Hoegiro Dongdaemun-Gu
Seoul 130-722
Korea, Republic of (South Korea)

Paper statistics

Downloads
104
Rank
212,736
Abstract Views
627