Artikel
Maximum likelihood inference in weakly identified dynamic stochastic general equilibrium models
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.
- Language
-
Englisch
- Bibliographic citation
-
Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 6 ; Year: 2015 ; Issue: 1 ; Pages: 123-152 ; New Haven, CT: The Econometric Society
- Classification
-
Wirtschaft
- Subject
-
Maximum likelihood
C(») test
score test
weak identification
- Event
-
Geistige Schöpfung
- (who)
-
Andrews, Isaiah
Mikusheva, Anna
- Event
-
Veröffentlichung
- (who)
-
The Econometric Society
- (where)
-
New Haven, CT
- (when)
-
2015
- DOI
-
doi:10.3982/QE331
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Andrews, Isaiah
- Mikusheva, Anna
- The Econometric Society
Time of origin
- 2015
Other Objects (12)
Frequentist inference in weakly identified dynamic stochastic general equilibrium models
Inference about non-identified SVARs
Empirical Bayes and likelihood inference
Applied statistical inference : likelihood and Bayes
Robust inference in models identified via heteroskedasticity
Inference for VARs identified with sign restrictions
Robust Bayesian inference for set-identified models
Robust Bayesian inference for set-identified models
Likelihood inference in some finite mixture models
Inference in partially identified heteroskedastic simultaneous equations models
Simple misspecification adaptive inference for interval identified parameters
Inference in partially identified heteroskedastic simultaneous equations models
Frequentist inference in weakly identified dynamic stochastic general equilibrium models
Inference about non-identified SVARs
Empirical Bayes and likelihood inference
Applied statistical inference : likelihood and Bayes
Robust inference in models identified via heteroskedasticity
Inference for VARs identified with sign restrictions
Robust Bayesian inference for set-identified models
Robust Bayesian inference for set-identified models
Likelihood inference in some finite mixture models
Inference in partially identified heteroskedastic simultaneous equations models
Simple misspecification adaptive inference for interval identified parameters
Inference in partially identified heteroskedastic simultaneous equations models
Frequentist inference in weakly identified dynamic stochastic general equilibrium models
Inference about non-identified SVARs
Empirical Bayes and likelihood inference
Applied statistical inference : likelihood and Bayes
Robust inference in models identified via heteroskedasticity
Inference for VARs identified with sign restrictions
Robust Bayesian inference for set-identified models
Robust Bayesian inference for set-identified models
Likelihood inference in some finite mixture models
Inference in partially identified heteroskedastic simultaneous equations models
Simple misspecification adaptive inference for interval identified parameters