Arbeitspapier
Maximum Likelihood Estimation for Generalized Autoregressive Score Models
We study the strong consistency and asymptotic normality of the maximum likelihood estimator for a class of time series models driven by the score function of the predictive likelihood. This class of nonlinear dynamic models includes both new and existing observation driven time series models. Examples include models for generalized autoregressive conditional heteroskedasticity, mixed-measurement dynamic factors, serial dependence in heavy-tailed densities, and other time varying parameter processes. We formulate primitive conditions for global identification, invertibility, strong consistency, asymptotic normality under correct specification and under mis-specification. We provide key illustrations of how the theory can be applied to specific dynamic models.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 14-029/III
- Klassifikation
-
Wirtschaft
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Hypothesis Testing: General
- Thema
-
time-varying parameter models
GAS
score driven models
Markov processes estimation
stationarity
invertibility
consistency
asymptotic normality
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Blasques, Francisco
Koopman, Siem Jan
Lucas, Andre
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2014
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Blasques, Francisco
- Koopman, Siem Jan
- Lucas, Andre
- Tinbergen Institute
Entstanden
- 2014