Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2008-11-06
Number: 08-108/4
Author-Name: Drew Creal
Author-Email: dcreal@feweb.vu.nl
Author-Workplace-Name: VU University Amsterdam
Author-Name: Siem Jan Koopman
Author-Email: s.j.koopman@feweb.vu.nl
Author-Workplace-Name: VU University Amsterdam
Author-Name: André Lucas
Author-Email: alucas@feweb.vu.nl
Author-Workplace-Name: VU University Amsterdam
Title: A General Framework for Observation Driven Time-Varying Parameter Models
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.
Classification-JEL: C10, C22, C32, C51
Keywords: dynamic models, time-varying parameters, non-linearity, exponential family, marked point processes, copulas
File-Url: https://papers.tinbergen.nl/08108.pdf
File-Format: application/pdf
File-Size: 2019993 bytes
Handle: RePEc:tin:wpaper:20080108