GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS

SUMMARY 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…

• PU
  Princeton University
• UD
  Universidad de Alicante
• IC
  Imperial College London
• EU
  Erasmus Universiteit Rotterdam
• VU
  Vrije Universiteit Amsterdam
• NO
  Nederlandse Organisatie voor Wetenschappelijk Onderzoek