Econometrics of Ascending Auctions by Quantile Regression

• 成果类型: Article
• 署名作者: Gimenes, Nathalie
• 署名单位: Pontificia Universidade Catolica do Rio de Janeiro
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00658
• 发表日期: 2017-12
• 页码: 944-953
• 关键词: sealed-bid auctions; 1st-price auctions; empirical-models; semiparametric estimation; identification; inference; COMPETITION; estimators; entry

摘要

This paper suggests an identification and estimation approach based on quantile regression to recover the underlying distribution of bidders' private values in ascending auctions under the IPV paradigm. The quantile regression approach provides a flexible and convenient parameterization of the private values distribution, with an estimation methodology easy to implement and with several specification tests. The quantile framework provides a new focus on the quantile level of the private values distributionin particular, the seller's optimal screening level, which can be very useful for bidders and seller. An empirical application using data from the USFS timber auctions illustrates the methodology.