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BidAnalyzer: A Method for Estimation and Selection of Dynamic Bidding Models

Author

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  • Sandy D. Jap

    (Goizueta Business School, Emory University, Atlanta, Georgia 30322)

  • Prasad A. Naik

    (Graduate School of Management, University of California, Davis, Davis, California 95616)

Abstract

Online reverse auctions generate real-time bidding data that could be used via appropriate statistical estimation to assist the corporate buyer's procurement decision. To this end, we develop a method, called BidAnalyzer, which estimates dynamic bidding models and selects the most appropriate of them. Specifically, we enable model estimation by addressing the problem of ; i.e., only one of suppliers' bids is realized, and the other (-1) bids remain unobserved. To address partial observability, BidAnalyzer estimates the latent price distributions of bidders by applying the Kalman filtering theory. In addition, BidAnalyzer conducts model selection by applying multiple information criteria. Using empirical data from an automotive parts auction, we illustrate the application of BidAnalyzer by estimating several dynamic bidding models to obtain empirical insights, retaining a model for forecasting, and assessing its predictive performance in out-of-sample. The resulting one-step-ahead price forecast is accurate up to 2.95% median absolute percentage error. Finally, we suggest how BidAnalyzer can serve as a device for price discovery in online reverse auctions.

Suggested Citation

  • Sandy D. Jap & Prasad A. Naik, 2008. "BidAnalyzer: A Method for Estimation and Selection of Dynamic Bidding Models," Marketing Science, INFORMS, vol. 27(6), pages 949-960, 11-12.
  • Handle: RePEc:inm:ormksc:v:27:y:2008:i:6:p:949-960
    DOI: 10.1287/mksc.1080.0363
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    References listed on IDEAS

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    1. Marnik G. Dekimpe & Philip Hans Franses & Dominique M. Hanssens & Prasad A. Naik, 2008. "Time-Series Models in Marketing," International Series in Operations Research & Management Science, in: Berend Wierenga (ed.), Handbook of Marketing Decision Models, chapter 0, pages 373-398, Springer.
      • Dekimpe, M.G. & Franses, Ph.H.B.F. & Hanssens, D.M. & Naik, P., 2006. "Time-Series Models in Marketing," ERIM Report Series Research in Management ERS-2006-049-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Mabert, Vincent A. & Skeels, Jack A., 2002. "Internet reverse auctions: Valuable tool in experienced hands," Business Horizons, Elsevier, vol. 45(4), pages 70-76.
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    4. Paul Klemperer, 1999. "Auction Theory: A Guide to the Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 13(3), pages 227-286, July.
    5. Klemperer, Paul, 1999. " Auction Theory: A Guide to the Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 13(3), pages 227-86, July.
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    Citations

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    Cited by:

    1. Jason Shachat & Lijia Wei, 2012. "Procuring Commodities: First-Price Sealed-Bid or English Auctions?," Marketing Science, INFORMS, vol. 31(2), pages 317-333, March.
    2. Dass, Mayukh & Reddy, Srinivas K. & Iacobucci, Dawn, 2014. "A Network Bidder Behavior Model in Online Auctions: A Case of Fine Art Auctions," Journal of Retailing, Elsevier, vol. 90(4), pages 445-462.
    3. Marie BLUM & Régis BLAZY, 2021. "The three stages of an auction: how do the bid dynamics influence auction prices? Evidence from live art auctions," Working Papers of LaRGE Research Center 2021-10, Laboratoire de Recherche en Gestion et Economie (LaRGE), Université de Strasbourg.
    4. Leeflang, Peter, 2011. "Paving the way for “distinguished marketing”," International Journal of Research in Marketing, Elsevier, vol. 28(2), pages 76-88.
    5. repec:wyi:journl:002158 is not listed on IDEAS
    6. Zhang, Shu & Jank, Wolfgang & Shmueli, Galit, 2010. "Real-time forecasting of online auctions via functional K-nearest neighbors," International Journal of Forecasting, Elsevier, vol. 26(4), pages 666-683, October.
    7. Wolfgang Jank & Galit Shmueli & Shu Zhang, 2010. "A flexible model for estimating price dynamics in on‐line auctions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 781-804, November.
    8. De Cock, Robin & Denoo, Lien & Clarysse, Bart, 2020. "Surviving the emotional rollercoaster called entrepreneurship: The role of emotion regulation," Journal of Business Venturing, Elsevier, vol. 35(2).
    9. Ballesteros-Pérez, Pablo & del Campo-Hitschfeld, Maria Luisa & Mora-Melià, Daniel & Domínguez, David, 2015. "Modeling bidding competitiveness and position performance in multi-attribute construction auctions," Operations Research Perspectives, Elsevier, vol. 2(C), pages 24-35.
    10. A. Ronald Gallant & Han Hong & Ahmed Khwaja, 2018. "The Dynamic Spillovers of Entry: An Application to the Generic Drug Industry," Management Science, INFORMS, vol. 64(3), pages 1189-1211, March.
    11. Shachat, Jason, 2009. "Procuring Commodities: Request for Quote or Reverse Auctions?," MPRA Paper 13418, University Library of Munich, Germany.
    12. Kim, Ju-Young & Brünner, Tobias & Skiera, Bernd & Natter, Martin, 2014. "A comparison of different pay-per-bid auction formats," International Journal of Research in Marketing, Elsevier, vol. 31(4), pages 368-379.

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