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A Smooth Transition Autoregressive Model for Matrix-Variate Time Series

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Abstract

In this paper, we present a new approach for modelling matrix-variate time series data that accounts for smooth changes in the dynamics of matrices. Although stylized facts in several fields suggest the existence of smooth nonlinearities, the existing matrix-variate models do not account for regime switches that are not abrupt. To address this gap, we introduce the matrix smooth transition autoregressive model, a flexible regime-switching model capable of capturing abrupt, smooth and no regime changes in matrix-valued data. We provide a thorough examination of the estimation process and evaluate the finite-sample performance of the matrix-variate smooth transition autoregressive model estimators with simulated data. Finally, the model is applied to real-world data.

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  1. Department of Economics and Law, University of Macerata, Via Crescimbeni, Macerata, Italy

    Andrea Bucci

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  1. Andrea Bucci
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Bucci, A. A Smooth Transition Autoregressive Model for Matrix-Variate Time Series. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10568-7

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