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American Economic Review Vol. 104 No. 5 2014

Weak Identification in Maximum Likelihood: A Question of Information

Isaiah Andrews1; Anna Mikusheva2

1 Department of Economics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, E19-750, Cambridge, MA 02142 (e-mail: ) · 2 Department of Economics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, E18-224, Cambridge, MA 02142 (e-mail: )

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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.

DOI
10.1257/aer.104.5.195
Volume
104
Issue
5
Pages
195-199
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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