Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

We characterize the dynamic properties of generalized autoregressive score models by identifying the regions of the parameter space that imply stationarity and ergodicity of the corresponding nonlinear time series process. We show how these regions are affected by the choice of parameterization and scaling, which are key features for the class of generalized autoregressive score models compared to other observation driven models. All results are illustrated for the case of time-varying means, variances, or higher-order moments.
Original languageEnglish
Pages (from-to)1088-1112
JournalElectronic Journal of Statistics
Volume8
Issue number1
DOIs
Publication statusPublished - 2014

Fingerprint Dive into the research topics of 'Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes'. Together they form a unique fingerprint.

  • Cite this