Statistical analysis of multivariate discrete-valued time series

This work gives an overview of statistical analysis for some models for multivariate discrete-valued (MDV) time series. We present observation-driven models and models based on higher-order Markov chains. Several extensions are highlighted including non-stationarity, network autoregressions, conditi...

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Bibliographic Details
Published in:Journal of multivariate analysis 2022-03, Vol.188, p.104805, Article 104805
Main Authors: Fokianos, Konstantinos, Fried, Roland, Kharin, Yuriy, Voloshko, Valeriy
Format: Article
Language:English
Subjects:
Autoregression
Categorical time series
Integer-valued time series
Markov chains
Maximum likelihood estimation
Multivariate discrete distributions
Robust estimation
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Summary:This work gives an overview of statistical analysis for some models for multivariate discrete-valued (MDV) time series. We present observation-driven models and models based on higher-order Markov chains. Several extensions are highlighted including non-stationarity, network autoregressions, conditional non-linear autoregressive models, robust estimation, random fields and spatio-temporal models.
ISSN:0047-259X
1095-7243
DOI:10.1016/j.jmva.2021.104805