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Modeling first bid in retail secondary market online auctions: A Bayesian approach

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  • Babak Zafari
  • Refik Soyer

Abstract

We propose a Bayesian framework to model bid placement time in retail secondary market online business‐to‐business auctions. In doing so, we propose a Bayesian beta regression model to predict the first bidder and time to first bid, and a dynamic probit model to analyze participation. In our development, we consider both auction‐specific and bidder‐specific explanatory variables. While we primarily focus on the predictive performance of the models, we also discuss how auction features and bidders' heterogeneity could affect the bid timings, as well as auction participation. We illustrate the implementation of our models by applying to actual auction data and discuss additional insights provided by the Bayesian approach, which can benefit auctioneers.

Suggested Citation

  • Babak Zafari & Refik Soyer, 2020. "Modeling first bid in retail secondary market online auctions: A Bayesian approach," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(3), pages 452-464, May.
    • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:3:p:452-464
    DOI: 10.1002/asmb.2498
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    References listed on IDEAS

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    1. Eric T. Bradlow & Young-Hoon Park, 2007. "Bayesian Estimation of Bid Sequences in Internet Auctions Using a Generalized Record-Breaking Model," Marketing Science, INFORMS, vol. 26(2), pages 218-229, 03-04.
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