Department of Economics and Business Economics

Generalized autoregressive score models with applications

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  • D. Creal, University of Chicago Booth School of Business
  • ,
  • S.J. Koopman
  • A. Lucas, Duisenberg School of Finance
We propose a class of observation-driven time series models referred to as generalized autoregressive score (GAS) models. The mechanism to update the parameters over time is the scaled score of the likelihood function. This new approach provides a unified and consistent framework for introducing time-varying parameters in a wide class of nonlinear models. The GAS model encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity, autoregressive conditional duration, autoregressive conditional intensity, and Poisson count models with time-varying mean. In addition, our approach can lead to new formulations of observation-driven models. We illustrate our framework by introducing new model specifications for time-varying copula functions and for multivariate point processes with time-varying parameters. We study the models in detail and provide simulation and empirical evidence.
Original languageEnglish
JournalJournal of Applied Econometrics
Volume28
Issue5
Pages (from-to)777-795
Number of pages19
ISSN0883-7252
DOIs
Publication statusPublished - 1 Aug 2013

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