Tinbergen Institute Discussion Paper No. 08-108/4
54 Pages Posted: 11 Nov 2008
Date Written: November 5, 2008
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.
Keywords: dynamic models, time-varying parameters, non-linearity, exponential family, marked point processes, copulas
JEL Classification: C10, C22, C32, C51
Suggested Citation: Suggested Citation
Creal, Drew D. and Koopman, Siem Jan and Lucas, Andre, A General Framework for Observation Driven Time-Varying Parameter Models (November 5, 2008). Tinbergen Institute Discussion Paper No. 08-108/4. Available at SSRN: https://ssrn.com/abstract=1297183 or http://dx.doi.org/10.2139/ssrn.1297183