Weak Identification in Maximum Likelihood: A Question of Information
- (pp. 195-99)
AbstractIn 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.
CitationAndrews, Isaiah, and Anna Mikusheva. 2014. "Weak Identification in Maximum Likelihood: A Question of Information." American Economic Review, 104 (5): 195-99. DOI: 10.1257/aer.104.5.195
- C22 Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- E13 General Aggregative Models: Neoclassical