A Smooth Transition Autoregressive Model for Matrix-Variate Time Series

Andrea Bucci ()
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Andrea Bucci: University of Macerata

Computational Economics, 2025, vol. 65, issue 1, No 15, 429-458

Abstract: 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.

Keywords: Matrix-valued time series; Smooth transition; Multivariate time series (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10568-7

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