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Mixture Matrix-valued Autoregressive Model
Preprint   Open access

Mixture Matrix-valued Autoregressive Model

Fei Wu and Kung-Sik Chan
arXiv.org
Cornell University
12/11/2023
DOI: 10.48550/arxiv.2312.06098
url
https://doi.org/10.48550/arxiv.2312.06098View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Time series of matrix-valued data are increasingly available in various areas including economics, finance, social science, etc. These data may shed light on the inter-dynamical relationships between two sets of attributes, for instance countries and economic indices. The matrix autoregressive (MAR) model provides a parsimonious approach for analyzing such data. However, the MAR model, being a linear model with parametric constraints, cannot capture the nonlinear patterns in the data, such as regime shifts in the dynamics. We propose a mixture matrix autoregressive (MMAR) model for analyzing potential regime shifts in the dynamics between two attributes, for instance, due to recession vs. blooming, or quiet period vs. pandemic. We propose an EM algorithm for maximum likelihood estimation. We derive some theoretical properties of the proposed method including consistency and asymptotic distribution, and illustrate its performance via simulations and real applications.
 Mathematics - Statistics Theory Statistics - Methodology Statistics - Theory

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