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  • 1709.09372

    Rights statement: 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

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On categorical time series with covariates

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On categorical time series with covariates. / Fokianos, Konstantinos; Truquet, Lionel.
In: Stochastic Processes and their Applications, Vol. 129, No. 9, 01.09.2019, p. 3446-3462.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fokianos, K & Truquet, L 2019, 'On categorical time series with covariates', Stochastic Processes and their Applications, vol. 129, no. 9, pp. 3446-3462. https://doi.org/10.1016/j.spa.2018.09.012

APA

Fokianos, K., & Truquet, L. (2019). On categorical time series with covariates. Stochastic Processes and their Applications, 129(9), 3446-3462. https://doi.org/10.1016/j.spa.2018.09.012

Vancouver

Fokianos K, Truquet L. On categorical time series with covariates. Stochastic Processes and their Applications. 2019 Sept 1;129(9):3446-3462. Epub 2018 Oct 9. doi: 10.1016/j.spa.2018.09.012

Author

Fokianos, Konstantinos ; Truquet, Lionel. / On categorical time series with covariates. In: Stochastic Processes and their Applications. 2019 ; Vol. 129, No. 9. pp. 3446-3462.

Bibtex

@article{cc0ef5b7cb694c9296d9cf7374b7ffe0,
title = "On categorical time series with covariates",
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",
author = "Konstantinos Fokianos and Lionel Truquet",
note = "This is the author{\textquoteright}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",
year = "2019",
month = sep,
day = "1",
doi = "10.1016/j.spa.2018.09.012",
language = "English",
volume = "129",
pages = "3446--3462",
journal = "Stochastic Processes and their Applications",
issn = "0304-4149",
publisher = "Elsevier",
number = "9",

}

RIS

TY - JOUR

T1 - On categorical time series with covariates

AU - Fokianos, Konstantinos

AU - Truquet, Lionel

N1 - 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

PY - 2019/9/1

Y1 - 2019/9/1

N2 - 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.

AB - 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.

KW - Autoregression

KW - Categorical data

KW - Chains with complete connection

KW - Coupling

KW - Covariates

KW - Ergodicity

KW - Markov chains

U2 - 10.1016/j.spa.2018.09.012

DO - 10.1016/j.spa.2018.09.012

M3 - Journal article

VL - 129

SP - 3446

EP - 3462

JO - Stochastic Processes and their Applications

JF - Stochastic Processes and their Applications

SN - 0304-4149

IS - 9

ER -