Semiparametric Estimation of First-Price Auctions with Risk-Averse Bidders
针对风险厌恶投标者的第一价格拍卖模型识别问题，提出参数化识别限制和半参数估计方法，用于估计风险厌恶参数和私人价值分布，并在美国林业局木材销售数据中拒绝风险中性假设。
In view of the non-identification of the first-price auction model with risk-averse bidders, this paper proposes some parametric identifying restrictions and a semiparametric estimator for the risk aversion parameter(s) and the latent distribution of private values. Specifically, we exploit heterogeneity across auctioned objects to establish semiparametric identification under a conditional quantile restriction of the bidders' private value distribution and a parameterization of the bidders' utility function. We develop a multistep semiparametric method and we show that our semiparametric estimator of the utility function parameter(s) converges at the optimal rate, which is slower than the parametric one but independent of the dimension of the exogenous variables thereby avoiding the curse of dimensionality. We then consider various extensions including a binding reserve price, affiliation among private values, and asymmetric bidders. The method is illustrated on U.S. Forest Service timber sales, and bidders' risk neutrality is rejected.