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.

翻译：本文提出了一种检测矩条件模型中识别失败的方法。通过引入一个拟雅可比矩阵（quasi-Jacobian matrix），该矩阵计算为矩条件在识别集估计上线性近似斜率，当局部和/或全局识别失败时，该矩阵渐近奇异；而当模型满足点识别和局部识别时，该矩阵等价于满秩的通常雅可比矩阵。基于这一性质，本文引入了一个具有卡方临界值的简单检验，可在无需预先了解底层识别结构的情况下，对强识别、半强识别和弱识别进行子向量推断。蒙特卡洛模拟以及对长期风险模型（Long-Run Risks model）的实证应用验证了上述结果。