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

Dieses Objekt wird bereitgestellt von:
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

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