The BARISTA: A model for bid arrivals in online auctions
Galit Shmueli, Ralph P. Russo, Wolfgang Jank

TL;DR
This paper introduces the BARISTA model, a flexible distribution family that accurately captures bid arrival patterns in online auctions, accounting for early, late, and self-similar bidding behaviors, improving over the traditional Poisson assumption.
Contribution
The paper develops the BARISTA process, a novel model for bid arrivals that incorporates varying intensities and self-similarity, with methods for simulation, estimation, and inference.
Findings
BARISTA effectively fits real eBay bid data.
It captures bid clustering and self-similarity.
Poisson bidder arrivals relate to BARISTA bid processes.
Abstract
The arrival process of bidders and bids in online auctions is important for studying and modeling supply and demand in the online marketplace. A popular assumption in the online auction literature is that a Poisson bidder arrival process is a reasonable approximation. This approximation underlies theoretical derivations, statistical models and simulations used in field studies. However, when it comes to the bid arrivals, empirical research has shown that the process is far from Poisson, with early bidding and last-moment bids taking place. An additional feature that has been reported by various authors is an apparent self-similarity in the bid arrival process. Despite the wide evidence for the changing bidding intensities and the self-similarity, there has been no rigorous attempt at developing a model that adequately approximates bid arrivals and accounts for these features. The goal…
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