论文标题
估计完整信息的离散游戏：将Logit重新带回游戏中
Estimating Discrete Games of Complete Information: Bringing Logit Back in the Game
论文作者
论文摘要
由于部分识别和缺乏封闭形式的表征，通常在计算上估算完整信息的离散游戏通常很困难。本文提出了可消除与平衡枚举，数值模拟和网格搜索相关的计算负担的估计和推理的计算方法。对于无序和有序的演奏游戏，我构建了一个确定的集合，其特征是以有限的基于广义的有条件力矩不等式为特征，这些不平等现象是（一个子向量）结构模型参数，该结构模型参数在无法观察到的标准logit假设下。我使用仿真和经验示例表明，所提出的方法会产生信息性识别的集合，并且可以比现有估计方法快几个数量级。
Estimating discrete games of complete information is often computationally difficult due to partial identification and the absence of closed-form moment characterizations. This paper proposes computationally tractable approaches to estimation and inference that remove the computational burden associated with equilibria enumeration, numerical simulation, and grid search. Separately for unordered and ordered-actions games, I construct an identified set characterized by a finite set of generalized likelihood-based conditional moment inequalities that are convex in (a subvector of) structural model parameters under the standard logit assumption on unobservables. I use simulation and empirical examples to show that the proposed approaches generate informative identified sets and can be several orders of magnitude faster than existing estimation methods.
