On categorical time series models with covariates

Konstantinos Fokianos and Lionel Truquet

Stochastic Processes and their Applications, 2019, vol. 129, issue 9, 3446-3462

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.

Keywords: Autoregression; Categorical data; Chains with complete connection; Coupling; Covariates; Ergodicity; Markov chains (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.spa.2018.09.012

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