A General Framework for Observation Driven Time-Varying Parameter Models

Drew Creal, Siem Jan Koopman and Andre Lucas

No 08-108/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

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: 2008-11-06
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (40)

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Working Paper: A General Framework for Observation Driven Time-Varying Parameter Models (2009)
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