Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86747
Authors: 
Creal, Drew
Koopman, Siem Jan
Lucas, André
Year of Publication: 
2008
Series/Report no.: 
Tinbergen Institute Discussion Paper 08-108/4
Abstract: 
We propose a new class of observation driven time series models referred to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled score of the likelihood function. This approach 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, the autoregressive conditional duration, the autoregressive conditional intensity, and the single source of error models. In addition, the GAS specification provides 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.
Subjects: 
dynamic models
time-varying parameters
non-linearity
exponential family
marked point processes
copulas
JEL: 
C10
C22
C32
C51
Document Type: 
Working Paper

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