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

Published Online:https://doi.org/10.1287/mksc.1080.0363

References

  • Ariely D., Simonson I. Buying, bidding, playing or competing? Value assessment and decision dynamics in online auctions. J. Consumer Psych. (2003) 13(1–2):113–123CrossrefGoogle Scholar
  • Bapna R., Jank W., Shmueli G. Price formation and its dynamics in online auctions. (2004) . Working paper, University of Maryland, College ParkCrossrefGoogle Scholar
  • Bass F. M., Bruce N., Majumdar S., Murthi B. P. S. Wearout effects of different advertising themes: A dynamic Bayesian model of the advertising-sales relationship. Marketing Sci. (2007) 26(2):179–195LinkGoogle Scholar
  • Beall S., Carter C., Carter P. L., Germer T., Hendrick T., Jap S., Kaufmann L., Maciejewski D., Monczka R., Petersen K. The role of reverse auctions in strategic sourcing. (2003) . Research paper, Center for Advanced Purchasing Studies (CAPS), Tempe, AZGoogle Scholar
  • Bikhchandani S., Hirshleifer D., Welch I. A theory of fads, fashion, custom, and cultural change as informational cascades. J. Political Econom. (1992) 100(5):992–1026CrossrefGoogle Scholar
  • Bradlow E. T., Park Y.-H. Bayesian estimation of bid sequences in Internet auctions using a generalized record-breaking model. Marketing Sci. (2007) 26(2):218–229LinkGoogle Scholar
  • Burnham K. P., Anderson D. R.Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2002) 2nd ed.(Springer-Verlag, New York) Google Scholar
  • Dekimpe M. G., Franses P. H., Hanssens D. M., Naik P. A., Wierenga B. Time series models in marketing. Handbook of Marketing Decisions-Making Models (2008) (Springer, New York) 373–398CrossrefGoogle Scholar
  • Durbin J., Koopman S. J.Time Series Analysis by State Space Methods (2001) (Oxford University Press, Oxford, UK) Google Scholar
  • Emiliani M. L. Business-to-business online auctions: Key issues for purchasing process improvement. Supply Chain Management (2000) 5(4):176–186CrossrefGoogle Scholar
  • Frühwirth-Schnatter S.Finite Mixture and Markov Switching Models (2006) (Springer, New York) Google Scholar
  • Häubl G., Popkowski-Leszczyc P. T. L. Bidding frenzy: How the speed of competitor reaction influences product valuations in auctions. (2006) . Working paper, University of Alberta, Edmonton, Alberta, CanadaGoogle Scholar
  • Heyman J., Orhun Y., Ariely D. Auction fever: The effect of opponents and quasi endowment on product valuations. J. Interactive Marketing (2004) 18(4):7–21CrossrefGoogle Scholar
  • Hohner G., Rich J., Ng E., Reid G., Davenport A. J., Kalagnanam J. R., Lee H. S., An C. Combinatorial and quantity-discount procurement auctions benefit Mars, Incorporated and its suppliers. Interfaces (2003) 33(1):23–35LinkGoogle Scholar
  • Hurvich C. M., Tsai C.-L. Regression and time series model selection in small samples. Biometrika (1989) 76(2):297–307CrossrefGoogle Scholar
  • Jap S. D. Online reverse auctions: Issues, themes, and prospects for the future. J. Acad. Marketing Sci. (2002) 30(4):506–525CrossrefGoogle Scholar
  • Katok E., Kwasnica A. M. Time is money: The effect of clock speed on seller's revenue in Dutch auctions. (2002) . ISBM Report 11-2002, The Pennsylvania State University, University ParkGoogle Scholar
  • Klemperer P. Auction theory: A guide to the literature. J. Econom. Surveys (1999) 13(3):227–286CrossrefGoogle Scholar
  • Ku G., Malhotra D., Murnighan J. K. Towards a competitive arousal model of decision making: A study of auction fever in live and Internet auctions. Organ. Behav. Human Decision Processes (2005) 96:89–103CrossrefGoogle Scholar
  • Mabert V. A., Skeels J. A. Internet reverse auctions: Valuable tools in experienced hands. Business Horizons (2002) 70–76CrossrefGoogle Scholar
  • McAfee R. P., McMillan J. Auctions and bidding. J. Econom. Literature (1987) 25(2):699–738Google Scholar
  • Metty T., Harlan R., Samelson Q., Moore T., Morris T., Sorenson R., Schneur A., Schneur O. R. R., Kanner J., Potts K., Robbins J. Reinventing the supplier negotiation process at Motorola. Interfaces (2005) 35(1):7–23LinkGoogle Scholar
  • Naik P. A., Raman K. Understanding the impact of synergy in multimedia communications. J. Marketing Res. (2003) 40(4):375–388CrossrefGoogle Scholar
  • Naik P. A., Mantrala M., Sawyer A. Planning media schedules in the presence of dynamic advertising quality. Marketing Sci. (1998) 17(3):214–235LinkGoogle Scholar
  • Naik P. A., Raman K., Winer R. Planning marketing-mix strategies in the presence of interactions. Marketing Sci. (2005) 24(1):25–34LinkGoogle Scholar
  • Naik P. A., Shi P., Tsai C.-L. Extending the Akaike information criterion to mixture regression models. J. Amer. Statist. Assoc. (2007) 102(477):244–254CrossrefGoogle Scholar
  • Oren S. S., Rothkopf M. H. Optimal bidding in sequential auctions. Oper. Res. (1975) 23:1080–1090LinkGoogle Scholar
  • Park Y.-H., Rhee S. B., Bradlow E. An integrated model for who, when and how much in Internet auctions. (2003) . Working paper, Wharton Business School, University of Pennsylvania, PhiladelphiaGoogle Scholar
  • Pinker E., Seidmann A., Vakrat Y. Managing online auctions: Current business and research issues. Management Sci. (2003) 49(11):1457–1484LinkGoogle Scholar
  • Sandholm T., Levine D., Concordia M., Martyn P., Hughes R., Jacobs J., Begg D. Changing the game in strategic sourcing at Procter & Gamble: Expressive competition enabled by optimization. Interfaces (2006) 36(1):55–72LinkGoogle Scholar
  • Seshadri S., Chatterjee K., Lilien G. L. Multiple source procurement competitions. Marketing Sci. (1991) 10(3):246–263LinkGoogle Scholar
  • Sheffi Y. Combinatorial auctions in the procurement of transportation services. Interfaces (2004) 34(4):245–252LinkGoogle Scholar
  • Shugan S. M. Marketing and designing transaction games. Marketing Sci. (2005) 24(4):525–530LinkGoogle Scholar
  • Shumway R. H., Stoffer D. S. An approach to time series smoothing and forecasting using the EM algorithm. J. Time Ser. Anal. (1982) 3:253–264CrossrefGoogle Scholar
  • Stein A., Hawking P., Wyld D. C. The 20% solution?: A case study on the efficacy of reverse auctions. Management Res. News (2003) 26(5):1–20CrossrefGoogle Scholar
  • Tuunainen V. K., van Heck E., Koppius O. Auction speed as a design variable for Internet auctions. (2001) . Working paper, Rotterdam School of Management, Erasmus University, Rotterdam, The NetherlandsGoogle Scholar
  • Wilcox R. T. Experts and amateurs: The role of experience in Internet auctions. Marketing Lett. (2000) 11(4):363–374CrossrefGoogle Scholar
  • Zeithammer R. Forward-looking bidding in online auctions. J. Marketing Res. (2006) 43(3):462–476CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.