Estimating Retail Demand with Poisson Mixtures and Out-of-Sample Likelihood

Applied Stochastic Models in Business and Industry, 30(4):455-463, 2013

21 Pages Posted: 7 Mar 2012 Last revised: 10 Jan 2017

See all articles by Howard Hao-Chun Chuang

Howard Hao-Chun Chuang

National Chengchi University - College of Commerce

Rogelio Oliva

Mays Business School, Texas A&M University

Date Written: March 6, 2013

Abstract

Estimation of retail demand is critical to decisions about procuring, shipping, and shelving. The idea of Poisson demand process is central to retail inventory management and numerous studies suggest that negative binomial (NB) distribution characterize retail demand well. In this study we reassess the adequacy of estimating retail demand with the NB distribution. We propose two Poisson mixtures – the Poisson-Tweedie (PT) family and the Conway-Maxwell Poisson (CMP) distribution – as generic alternatives to the NB distribution. Based on the principle of likelihood and information theory, we adopt out-of-sample likelihood (OSL) as a metric for model selection. We test the procedure on consumer demand for 580 SKU-store sales datasets. Overall the PT family and the CMP distribution outperform the NB distribution for 70% of the tested samples. As a general case of the NB model, the PTF family has particularly strong performance for datasets with relatively small means and high dispersion. Our finding carries useful implications for researchers and practitioners who seek for flexible alternatives to the oft-used NB distribution in characterizing retail demand.

Keywords: retail demand, Poisson mixtures, maximum likelihood, model selection

JEL Classification: C13

Suggested Citation

Chuang, Howard Hao-Chun and Oliva, Rogelio, Estimating Retail Demand with Poisson Mixtures and Out-of-Sample Likelihood (March 6, 2013). Applied Stochastic Models in Business and Industry, 30(4):455-463, 2013. Available at SSRN: https://ssrn.com/abstract=2018010 or http://dx.doi.org/10.2139/ssrn.2018010

Howard Hao-Chun Chuang (Contact Author)

National Chengchi University - College of Commerce ( email )

64 Sec 2 Zhinan Rd
Wens
Taipei, Taiwan 11605
Taiwan

Rogelio Oliva

Mays Business School, Texas A&M University ( email )

430 Wehner
College Station, TX 77843-4218
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
31
Abstract Views
1,051
PlumX Metrics