Econometrics of Ascending Auctions by Quantile Regression
提出基于分位数回归的方法，从升序拍卖数据中识别和估计竞拍者私人价值的分布，并通过美国林务局木材拍卖数据展示其应用，对拍卖参与者和卖家有实际价值。
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 distribution—in 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.