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分类依据与纠错

AI 根据所提供的论文材料归类:摘要明确从博弈行动识别私人信息这一结构对象,并估计其分位数、分布与密度。

反馈分类问题

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代码、数据与补充材料

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关注这篇论文的作者
Andreea Enache

“风险率”博弈模型的分位数分析

研究问题
在行动取决于私人信息及其分布与密度之比的不完全信息博弈中,能否从观察到的行动恢复私人信息分布?
方法与思路
作者通过联系行动分位数与私人信息分位数的微分方程,分析局部和全局识别、逆问题适定性及非参数估计。
材料陈述的贡献
摘要称在适当假设下建立识别与适定性;条件于数据分位数的 root-n 估计,讨论潜在分位数、分布和密度的同速估计。
阅读关注点
应核对策略映射、微分方程边界条件及适定性所用拓扑,尤其是密度估计达到所述速度需要怎样的导数控制。
阅读边界
摘要未列出适当假设和边界条件,所述速度具有条件性;政策应用仅为潜在用途,未提供具体政策评估结果。

原文摘要

This paper consists of an econometric analysis of a broad class of games of incomplete information. In these games, a player’s action depends both on her unobservable characteristic (the private information), as well as on the ratio of the distribution of the unobservable characteristic and its density function (which we call the ”hazard-rate”). The goal is to use data on players’ actions to recover the distribution of private information. We show that the structural parameter (the quantile of the unobservable characteristic, denoted by L) can be related to the reduced form parameter (the quantile of the data, denoted by Q) through a differential equation Q(α)=a(L(α),αL′(α)). We analyse the local and global identification of this equation in L. At the same time, under suitable assumptions, we establish the local and global well-posedness of the inverse problem Q=T(L). We solve the differential equation and, as a consequence of the well-posedness, we estimate nonparametrically the quantile and the c.d.f. function of the unobserved variables at a root-n speed of convergence (conditional on the quantile of the data being estimated at root-n). Moreover as the transformation of Q in L is continuous with respect to a topology that implies also the derivatives, we estimate nonparametrically the quantile density and the density the unobserved variables (that are continuous functions with respect to Q) at root-n speed of convergence. We provide also several generalisations of the model accounting for exogenuous variables, implicit expression of the strategy function and integral expression of the strategy function. Our results have several policy applications, including better design of auctions, regulation models, estimation of war of attrition in patent races, and public good contracts.

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