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英文文献：Structural Analysis of First-Price Auction Data: Insights from the Laboratory-首价拍卖数据的结构分析:来自实验室的见解
英文文献作者：Paul Pezanis-Christou,Andres Romeu
英文文献摘要：
We use laboratory data from first-price auctions and manipulate the quantity and the quality of information available to assess the robustness of structural inferences (i.e., estimates, revenue predictions and optimal reserve price recommendations). We show that the latter are sensitive to the quantity of information when quality is low such as in field settings, and that improving quality in such settings dampens the effect of quantity and unveils out-of-equilibrium bidding patterns. Yet, a counterfactual analysis of the seller's revenues and optimal reserve prices indicates that behavior is best explained by the usual Nash equilibrium model with either risk aversion or a power form of probability misperception. When the information available is of the highest quality, as in laboratory settings, this model is typically rejected because of a nonlinear bidding behavior. We consider two rationales for such behavior and find that they leave revenue predictions and optimal price recommendations hardly affected.
我们使用来自首价拍卖的实验室数据，并操纵可用信息的数量和质量，以评估结构推断(即估计、收入预测和最优保留价格建议)的稳健性。我们发现，当质量较低时，如在现场环境中，后者对信息的数量很敏感，而在这种环境中提高质量会减弱数量的影响，并揭示出非均衡投标模式。然而，对卖方收入和最优保留价格的反事实分析表明，这种行为最好的解释是通常的纳什均衡模型的风险规避或概率误解的权力形式。当可获得的信息是最高质量的，如在实验室环境中，这个模型通常被拒绝，因为非线性投标行为。我们考虑了这一行为的两个理由，发现它们几乎没有影响收入预测和最优价格建议。
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