A non-parametric estimator for setting reservation prices in procurement auctions

  • Authors:

  • Martin Bichler;Jayant R. Kalagnanam

  • Affiliations:

  • Department of Informatics, Technical University of Munich, Department of Informatics (I 18), Technical University of Munich, Garching/Munich, Germany 85748;IBM T. J. Watson Research Center, Yorktown Heights, USA 10598

  • Venue:
  • Information Technology and Management
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Electronic auction markets collect large amounts of auction field data. This enables a structural estimation of the bid distributions and the possibility to derive optimal reservation prices. In this paper we propose a new approach to setting reservation prices. In contrast to traditional auction theory we use the buyer's risk statement for getting a winning bid as a key criterion to set an optimal reservation price. The reservation price for a given probability can then be derived from the distribution function of the observed drop-out bids. In order to get an accurate model of this function, we propose a nonparametric technique based on kernel distribution function estimators and the use of order statistics. We improve our estimator by additional information, which can be observed about bidders and qualitative differences of goods in past auctions rounds (e.g. different delivery times). This makes the technique applicable to RFQs and multi-attribute auctions, with qualitatively differentiated offers.