Counterfactual analysis under partial identification using locally robust refinement
研究了在结构模型存在多个简化形式（如多重均衡）时，如何从识别集中选出对反事实预测更稳健的参数值，并给出简化表示方法，适用于矩不等式模型和截断数据应用。
Summary 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.