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A Smooth Transition Autoregressive Model for Matrix-Variate Time Series

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  • Andrea Bucci

    (University of Macerata)

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.

Suggested Citation

  • Andrea Bucci, 2025. "A Smooth Transition Autoregressive Model for Matrix-Variate Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 429-458, January.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:1:d:10.1007_s10614-024-10568-7
    DOI: 10.1007/s10614-024-10568-7
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    References listed on IDEAS

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