This paper proposes an information-based inference method for partially identified parameters in incomplete models that is valid both when the model is correctly specified and when it is misspecified. Key features of the method are: (i) it is based on minimizing a suitably defined Kullback-Leibler information criterion that accounts for incompleteness of the model and delivers a non-empty pseudo-true set; (ii) it is computationally tractable; (iii) its implementation is the same for both correctly and incorrectly specified models; (iv) it exploits all information provided by variation in discrete and continuous covariates; (v) it relies on Rao's score statistic, which is shown to be asymptotically pivotal.

翻译：本文提出一种基于信息推断的部分识别参数方法，该方法适用于不完整模型，且在模型正确设定与误设情况下均有效。该方法的核心特征包括：(i) 基于最小化适当定义的Kullback-Leibler信息准则，该准则能够处理模型不完整性并生成非空伪真集；(ii) 计算处理可行；(iii) 对正确设定与误设模型采用相同实施流程；(iv) 充分利用离散与连续协变量变化提供的全部信息；(v) 基于Rao得分统计量，该统计量被证明具有渐近枢轴性质。