When parameters are weakly identified, bounds on the parameters may provide a valuable source of information. Existing weak identification estimation and inference results are unable to combine weak identification with bounds. Within a class of minimum distance models, this paper proposes identification-robust inference that incorporates information from bounds when parameters are weakly identified. The inference is based on limit theory that combines weak identification theory with parameter-on-the-boundary theory. This paper demonstrates the role of the bounds and identification-robust inference in two example factor models. This paper also demonstrates the identification-robust inference in an empirical application, a factor model for parental investments in children.

翻译：当参数识别薄弱时,参数的界限可能提供一个宝贵的信息来源; 现有的识别估计和推断结果薄弱,无法将薄弱的识别与界限结合起来; 在一个最低距离模型类别中,本文件提议了在参数识别薄弱时纳入界限信息的身份识别-紫外线推断; 推断基于将薄弱的识别理论与边界参数理论相结合的限值理论; 本文在两个示例要素模型中显示了界限和识别-紫外线推断的作用; 本文还展示了在经验应用中的识别-紫外线推断,这是父母对儿童投资的一个要素模型。