矩条件模型中识别失效的检测
Detecting Identification Failure in Moment Condition Models
AI总结：
本文针对矩条件模型的识别失效检测问题，提出基于拟雅可比矩阵的卡方检验方法，可在未知识别结构下进行多识别强度的子向量推断，经模拟和实证验证有效。
摘要：
本文提出了一种检测矩条件模型中识别失效的方法。该方法通过引入一个拟雅可比矩阵实现，该矩阵被定义为矩条件在识别集估计值上的线性近似的斜率。当局部和/或全局识别失效时，该矩阵渐近奇异；当模型为点识别且局部识别时，它等价于通常的满秩雅可比矩阵。基于这一性质，本文提出了一种采用卡方临界值的简单检验方法，可在无需先验了解潜在识别结构的情况下，针对强识别、半强识别和弱识别情形进行子向量推断。蒙特卡洛模拟以及对长期风险（Long-Run Risks）模型的实证应用验证了上述结果。
英文摘要：
This paper develops an approach to detect identification failure in moment condition models. This is achieved by introducing a quasi-Jacobian matrix computed as the slope of a linear approximation of the moments on an estimate of the identified set. It is asymptotically singular when local and/or global identification fails, and equivalent to the usual Jacobian matrix which has full rank when the model is point and locally identified. Building on this property, a simple test with chi-squared critical values is introduced to conduct subvector inferences allowing for strong, semi-strong, and weak identification without \textit{a priori} knowledge about the underlying identification structure. Monte-Carlo simulations and an empirical application to the Long-Run Risks model illustrate the results.
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