Inference on Auctions with Weak Assumptions on Information
弱信息结构假设下的拍卖推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要通过拍卖均衡限制从出价识别估值分布、福利与反事实对象，直接对应微观结构模型的识别和推断。
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弱信息结构假设下的拍卖推断
- 研究问题
- 仅观察独立拍卖的出价，且不固定跨拍卖的信息结构时，能够对估值分布、福利和反事实对象作出哪些推断？
- 方法与思路
- 作者利用贝叶斯相关均衡与贝叶斯纳什均衡的联系，将识别集成员检验转化为线性规划，并构造集合置信区域。
- 材料陈述的贡献
- 摘要称给出私人价值和共同价值拍卖的识别集，提出基于尾部不等式的有限样本推断，并报告模拟和 OCS 拍卖应用。
- 阅读关注点
- 应核对保留的信息与均衡假设、识别集是否尖锐、线性规划的离散化方式，以及不同集合推断方法的覆盖保证。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明完整拍卖环境及数值结果，弱信息假设不等于无假设识别。
原文摘要
Given a sample of bids from independent auctions, this paper examines the question of inference on auction fundamentals (e.g. valuation distributions, welfare measures) under weak assumptions on information structure. The question is important as it allows us to learn about the valuation distribution in a robust way, i.e., without assuming that a particular information structure holds across observations. We leverage the recent contributions of \cite{Bergemann2013} in the robust mechanism design literature that exploit the link between Bayesian Correlated Equilibria and Bayesian Nash Equilibria in incomplete information games to construct an econometrics framework for learning about auction fundamentals using observed data on bids. We showcase our construction of identified sets in private value and common value auctions. Our approach for constructing these sets inherits the computational simplicity of solving for correlated equilibria: checking whether a particular valuation distribution belongs to the identified set is as simple as determining whether a {\it linear} program is feasible. A similar linear program can be used to construct the identified set on various welfare measures and counterfactual objects. For inference and to summarize statistical uncertainty, we propose novel finite sample methods using tail inequalities that are used to construct confidence regions on sets. We also highlight methods based on Bayesian bootstrap and subsampling. A set of Monte Carlo experiments show adequate finite sample properties of our inference procedures. We illustrate our methods using data from OCS auctions.