On categorical time series with covariates

Fokianos, Konstantinos and Truquet, Lionel (2019) On categorical time series with covariates. Stochastic Processes and their Applications, 129 (9). pp. 3446-3462. ISSN 0304-4149

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Abstract

We study the problem of stationarity and ergodicity for autoregressive multinomial logistic time series models which possibly include a latent process and are defined by a GARCH-type recursive equation. We improve considerably upon the existing conditions about stationarity and ergodicity of those models. Proofs are based on theory developed for chains with complete connections. A useful coupling technique is employed for studying ergodicity of infinite order finite-state stochastic processes which generalize finite-state Markov chains. Furthermore, for the case of finite order Markov chains, we discuss ergodicity properties of a model which includes strongly exogenous but not necessarily bounded covariates.

Item Type:
Journal Article
Journal or Publication Title:
Stochastic Processes and their Applications
Additional Information:
This is the author’s version of a work that was accepted for publication in Stochastic Processes and their Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Stochastic Processes and their Applications, 129, 9, 2019 DOI: 10.1016/j.spa.2018.09.012
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2611
Subjects:
?? autoregressioncategorical datachains with complete connectioncouplingcovariatesergodicitymarkov chainsmodelling and simulationapplied mathematicsstatistics and probability ??
ID Code:
128312
Deposited By:
Deposited On:
17 Oct 2018 12:54
Refereed?:
Yes
Published?:
Published
Last Modified:
03 Jan 2024 00:22