Structural models that admit multiple reduced forms, such as game-theoretic models with multiple equilibria, pose challenges in practice, especially when parameters are set-identified and the identified set is large. In such cases, researchers often choose to focus on a particular subset of equilibria for counterfactual analysis, but this choice can be hard to justify. This paper shows that some parameter values can be more “desirable” than others for counterfactual analysis, even if they are empirically equivalent given the data. In particular, within the identified set, some counterfactual predictions can exhibit more robustness than others, against local perturbations of the reduced forms (e.g. the equilibrium selection rule). We provide a representation of this subset which can be used to simplify the implementation. We illustrate our message using moment inequality models, and provide an empirical application based on a model with top-coded data.

中文翻译：

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局部识别下的反事实分析使用局部鲁棒细化

允许多种简化形式的结构模型，例如具有多个均衡的博弈论模型，在实践中提出了挑战，尤其是当参数是集合识别的并且识别的集合很大时。在这种情况下，研究人员通常选择专注于特定的均衡子集进行反事实分析，但这种选择很难证明是合理的。本文表明，对于反事实分析，某些参数值可能比其他参数值更“合乎需要”，即使它们在给定数据的经验上是等效的。特别是，在识别的集合中，一些反事实预测可以比其他预测表现出更强的鲁棒性，对抗简化形式的局部扰动（例如均衡选择规则）。我们提供了该子集的表示，可用于简化实现。