Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/150382
Authors: 
Andrews, Isaiah
Mikusheva, Anna
Year of Publication: 
2015
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 6 [Year:] 2015 [Issue:] 1 [Pages:] 123-152
Abstract: 
This paper examines the issue of weak identification in maximum likelihood, motivated by problems with estimation and inference in a multidimensional dynamic stochastic general equilibrium model. We show that two forms of the classical score (Lagrange multiplier) test for a simple hypothesis concerning the full parameter vector are robust to weak identification. We also suggest a test for a composite hypothesis regarding a subvector of parameters. The suggested subset test is shown to be asymptotically exact when the nuisance parameter is strongly identified. We pay particular attention to the question of how to estimate Fisher information and we make extensive use of martingale theory.
Subjects: 
Maximum likelihood
C(») test
score test
weak identification
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/3.0/
Document Type: 
Article

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