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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 6 ; Year: 2015 ; Issue: 1 ; Pages: 123-152 ; New Haven, CT: The Econometric Society
- Klassifikation
-
Wirtschaft
- Thema
-
Maximum likelihood
C(») test
score test
weak identification
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Andrews, Isaiah
Mikusheva, Anna
- Ereignis
-
Veröffentlichung
- (wer)
-
The Econometric Society
- (wo)
-
New Haven, CT
- (wann)
-
2015
- DOI
-
doi:10.3982/QE331
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Andrews, Isaiah
- Mikusheva, Anna
- The Econometric Society
Entstanden
- 2015
Ähnliche Objekte (12)
Frequentist inference in weakly identified dynamic stochastic general equilibrium models
Empirical Bayes and likelihood inference
Inference about non-identified SVARs
Applied statistical inference : likelihood and Bayes
Likelihood inference in some finite mixture models
Empirical likelihood inference for monotone index model
Robust Bayesian inference for set-identified models
Robust inference in models identified via heteroskedasticity
Robust Bayesian inference for set-identified models
Inference for VARs identified with sign restrictions
Likelihood inference for a nonstationary fractional autoregressive model
Inference in partially identified heteroskedastic simultaneous equations models
Frequentist inference in weakly identified dynamic stochastic general equilibrium models
Empirical Bayes and likelihood inference
Inference about non-identified SVARs
Applied statistical inference : likelihood and Bayes
Likelihood inference in some finite mixture models
Empirical likelihood inference for monotone index model
Robust Bayesian inference for set-identified models
Robust inference in models identified via heteroskedasticity
Robust Bayesian inference for set-identified models
Inference for VARs identified with sign restrictions
Likelihood inference for a nonstationary fractional autoregressive model
Inference in partially identified heteroskedastic simultaneous equations models
Frequentist inference in weakly identified dynamic stochastic general equilibrium models
Empirical Bayes and likelihood inference
Inference about non-identified SVARs
Applied statistical inference : likelihood and Bayes
Likelihood inference in some finite mixture models
Empirical likelihood inference for monotone index model
Robust Bayesian inference for set-identified models
Robust inference in models identified via heteroskedasticity
Robust Bayesian inference for set-identified models
Inference for VARs identified with sign restrictions
Likelihood inference for a nonstationary fractional autoregressive model