Maximum likelihood inference in weakly identified dynamic stochastic general equilibrium models

Isaiah Andrews and Anna Mikusheva

Quantitative Economics, 2015, vol. 6, issue 1, 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.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
http://hdl.handle.net/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:quante:v:6:y:2015:i:1:p:123-152

Ordering information: This journal article can be ordered from
https://www.econometricsociety.org/membership

Access Statistics for this article

More articles in Quantitative Economics from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().