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