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arXiv · 2401.09381

Modelling clusters in network time series with an application to presidential elections in the USA

Guy Nason·Daniel Salnikov·Mario Cortina-Borja
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Abstract

Network time series are becoming increasingly relevant in the study of dynamic processes characterised by a known or inferred underlying network structure. Generalised Network Autoregressive (GNAR) models provide a parsimonious framework for exploiting the underlying network, even in the high-dimensional setting. We extend the GNAR framework by presenting the $\textit{community}$-$\alpha$ GNAR model that exploits prior knowledge and/or exogenous variables for identifying and modelling dynamic interactions across communities in the network. We further analyse the dynamics of $\textit{ Red, Blue}$ and $\textit{Swing}$ states throughout presidential elections in the USA. Our analysis suggests interesting global and communal effects.

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Guy Nason, Daniel Salnikov, Mario Cortina-Borja. 2024-01-17. Modelling clusters in network time series with an application to presidential elections in the USA. https://arxiv.org/abs/2401.09381

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