Detecting identification failure in moment condition models
提出一种通过拟雅可比矩阵检测矩条件模型识别失败的方法，并构建卡方检验用于子向量推断，适用于强、半强和弱识别情形，无需先验识别结构知识。
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.