Nonparametric Identification and Inference of First-Price Auctions with Heterogeneous Bidders
提出非参数方法分析第一价格拍卖中投标者的异质性风险偏好和不对称价值分布，利用美国林业局木材拍卖数据验证了风险偏好异质性模型。
In the auction literature, it is well established that bidders’ asymmetry plays an important role in determining auction revenues. In this article, we propose a nonparametric methodology to analyze first-price auctions with two popularly adopted asymmetries: heterogeneous risk preferences and asymmetric value distributions. We find that the two competing models provide distinct implications for the bid distributions conditional on heterogeneity. By modeling bidders’ asymmetry as unobserved heterogeneity, we show that the conditional bid distributions are identified nonparametrically. These results enable researchers to test the two competing models. In an application using the US Forest Service timber auction data, we find that the data supports the model with heterogeneity in risk preference.