Semiparametric Estimation of First-Price Auction Models
提出两步半参数法估计一级价格拍卖模型，先用局部多项式估计投标密度和分布，再用矩法估计私人价值密度参数，达到参数收敛速度且渐近正态，适用于多协变量拍卖场景。
In this paper we propose a two-step semiparametric procedure to estimate first-price auction models. In the first-step, we estimate the bid density and distribution using local polynomial method, and recover a sample of (pseudo) private values. In the second-step, we apply the method of moments to the sample of private values to estimate a finite set of parameters that characterize the density of private values. We show that our estimator attains the parametric consistency rate and is asymptotically normal. And we also determine its asymptotic variance. The advantage of our approach is that it can accommodate multiple auction covariates. Monte Carlo exercises show that the estimator performs well both in estimating the value density and in choosing the revenue maximizing reserve price.