Assessment of uncertainty in bid arrival times: A Bayesian mixture model

Abstract

In this paper, we propose a Bayesian approach to model uncertainty in the bid arrival time by focusing on the time of the first bid in secondary (retail) market online business-to-business auctions. The proposed model is based on a Bayesian finite mixture of beta distributions. Our main objectives is to study potential heterogeneity of different auctions. In doing so, we incorporate some auction-specific features into the model and analyze their effect on the first bid time. We consider multiple competing models both in terms of fit and predictive performance. We also discuss managerial implications of the study and suggest how auctioneers can benefit from both the explanatory and predictive aspects of the model.

Keywords:

• Arrival time modeling
• auction segmentation
• mixture modeling
• beta regression
• Bayesian model comparison
• risk management

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Per-unit-price is defined as the pallet’s declared retail value divided by the pallet’s quantity.

2 Reserve price is the lowest price at which auctioneer is willing to sell the item and it is usually not visible to the bidders.

3 Since models with covariates outperform models without covariate (see Table 1), the comparative results are only shown for the models with covariates; similar findings are obtained for the models without covariates.