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

Marketing Science - Tập 27 Số 6 - Trang 949-960 - 2008
Sandy D. Jap1, P. A. Naik2
1Goizueta Business School, Emory University, Atlanta, Georgia 30322
2Graduate School of Management, University of California Davis, Davis, California 95616

Tóm tắt

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 partial observability; i.e., only one of N suppliers' bids is realized, and the other (N-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.

Từ khóa


Tài liệu tham khảo

10.1207/S15327663JCP13-1&2_10

Bapna R., Jank W., Shmueli G. Price formation and its dynamics in online auctions. (2004) . Working paper, University of Maryland, College Park

10.1287/mksc.1060.0208

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, AZ

10.1086/261849

10.1287/mksc.1060.0225

Burnham K. P., 2002, Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2

10.1007/978-0-387-78213-3_11

Durbin J., 2001, Time Series Analysis by State Space Methods

10.1108/13598540010347299

Frühwirth-Schnatter S., 2006, Finite Mixture and Markov Switching Models

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, Canada

10.1002/dir.20020

10.1287/inte.33.1.23.12717

10.1093/biomet/76.2.297

10.1177/009207002236925

10.1111/1467-6419.00083

10.1016/j.obhdp.2004.10.001

10.1016/S0007-6813(02)00229-X

McAfee R. P., 1987, J. Econom. Literature, 25, 699

10.1287/inte.1040.0119

10.1509/jmkr.40.4.375.19385

10.1287/mksc.17.3.214

10.1287/mksc.1040.0083

10.1198/016214506000000861

10.1287/opre.23.6.1080

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, Philadelphia

10.1287/mnsc.49.11.1457.20584

10.1287/inte.1050.0185

10.1287/mksc.10.3.246

10.1287/inte.1040.0075

10.1287/mksc.1050.0174

10.1111/j.1467-9892.1982.tb00349.x

10.1108/01409170310783439

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 Netherlands

10.1023/A:1008141313927

10.1509/jmkr.43.3.462

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Marketing Science

Tập 27 Số 6

949-960

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