A General Framework for Observation Driven Time-Varying Parameter Models

Drew Creal, Siem Jan Koopman and Andre Lucas

Global COE Hi-Stat Discussion Paper Series from Institute of Economic Research, Hitotsubashi University

Abstract: We propose a new class of observation driven time series models that we refer to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled likelihood score. This provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity, autoregressive conditional duration, autoregressive conditional intensity and single source of error models. In addition, the GAS specification gives rise to a wide range of new observation driven models. Examples include non-linear regression models with time-varying parameters, observation driven analogues of unobserved components time series models, multivariate point process models with time-varying parameters and pooling restrictions, new models for time-varying copula functions and models for time-varying higher order moments. We study the properties of GAS models and provide several non-trivial examples of their application.

Keywords: dynamic models; time-varying parameters; non-linearity; exponential family; marked point processes; copulas (search for similar items in EconPapers)
JEL-codes: C10 C22 C32 C51 (search for similar items in EconPapers)
Date: 2009-03
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://gcoe.ier.hit-u.ac.jp/research/discussion/2008/pdf/gd08-038.pdf (application/pdf)

Related works:
Working Paper: A General Framework for Observation Driven Time-Varying Parameter Models (2008)
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:hst:ghsdps:gd08-038

Access Statistics for this paper

More papers in Global COE Hi-Stat Discussion Paper Series from Institute of Economic Research, Hitotsubashi University Contact information at EDIRC.
Bibliographic data for series maintained by Tatsuji Makino ().