Weak Identification in Maximum Likelihood: A Question of Information
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Mikusheva_Weak identification.pdf
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482.84 KB
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503367e7a53d642b3348328f8b3aab64
Author(s)
Andrews, Isaiah
Mikusheva, Anna
Date Issued
May 2014
Journal
American Economic Review
Publisher
American Economic Association
Citation
Andrews, Isaiah, and Anna Mikusheva. “ Weak Identification in Maximum Likelihood: A Question of Information † .” American Economic Review 104, no. 5 (May 2014): 195–199.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Economics
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DOI of Published Version
https://doi.org/10.1257/aer.104.5.195
