Identification in Auction Models with Interdependent Costs
提出非参数识别方法，用于不对称竞标者、依赖私人信息和相互依赖成本的采购模型，利用密歇根州公路采购数据估计收益相关要素并评估影响赢家诅咒的政策。
This paper provides a nonparametric identification result for procurement models with asymmetric bidders, dependent private information, and interdependent costs. For risk-neutral bidders, the model’s payoff-relevant primitives are the joint distribution of private information and each bidder’s full-information expected cost. The joint distribution of bids identifies the joint distribution of signals. First-order conditions identify the expected cost conditional on tying with at least one competitor for the lowest bid. I show identification of each bidder’s full-information cost, using variation in competitors’ cost shifters that are excludable from bidders’ own full-information costs, and generate variation in the set of competitors’ signals that induce a tie for the lowest bid. I estimate the relevant payoff primitives using data from Michigan highway procurements and evaluate policies that affect the winner’s curse’s severity.