Weak Identification in Maximum Likelihood: A Question of Information
发现Fisher信息的两种估计量（基于得分二次变差和基于对数似然负海森矩阵）在弱识别模型中不等价，这会影响最大似然估计的行为，并用一个DSGE模型展示了这种差异在弱识别时很大。
In this paper we connect the discrepancy between two estimates of Fisher information, one based on the quadratic variation of the score and the other based on the negative Hessian of the log-likelihood, to weak identification. Classical asymptotic approximations assume that these two estimates are asymptotically equivalent, but we show that this equivalence fails in many weakly identified models, which can distort the behavior of the MLE. Using a stylized DSGE model we show that the discrepancy between information estimates is large when identification is weak.