Multicategory Purchase Incidence Models for Partitions of Product Categories

Multicategory Purchase Incidence Models for Partitions of Product Categories

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Article ID: iaor2017813
Volume: 36
Issue: 3
Start Page Number: 230
End Page Number: 240
Publication Date: Apr 2017
Journal: Journal of Forecasting
Authors:
Keywords: time series: forecasting methods, simulation, stochastic processes, statistics: regression
Abstract:

We analyze multicategory purchases of households by means of heterogeneous multivariate probit models that relate to partitions formed from a total of 25 product categories. We investigate both prior and post hoc partitions. We search model structures by a stochastic algorithm and estimate models by Markov chain Monte Carlo simulation. The best model in terms of cross‐validated log‐likelihood refers to a post hoc partition with two groups; the second‐best model considers all categories as one group. Among prior partitions with at least two category groups a five‐group model performs best. Effects on average basket value differ for the model with five prior category groups from those for the best‐performing model in 40% and 24% of the investigated categories for features and displays, respectively. In addition, the model with five prior category groups also underestimates total sales revenue across all categories by about 28%.

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