论文标题
在一类最小距离模型中的弱标识和边界
Weak Identification with Bounds in a Class of Minimum Distance Models
论文作者
论文摘要
当参数被弱标识时，参数的界限可能会提供有价值的信息来源。现有的较弱的识别估计和推理结果无法将弱识别与边界结合起来。在一系列最小距离模型中，本文提出了识别式推断，该推论在弱标识时结合了来自边界的信息。该推论基于极限理论，该理论将弱识别理论与边界理论的参数结合在一起。本文展示了在两个示例因子模型中的边界和识别式推断的作用。本文还展示了经验应用中的识别式推断，这是父母投资儿童的因素模型。
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.
