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

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.