A smooth transition autoregressive model for matrix-variate time series

TL;DR

This paper introduces a matrix smooth transition autoregressive model for matrix-variate time series, allowing for smooth regime changes in the dynamics, with estimation methods and demonstrated applications.

Contribution

It extends existing matrix-variate autoregressive models by incorporating smooth regime-switching capabilities, filling a gap in modeling gradual changes.

Findings

The model effectively captures smooth regime transitions in simulated data.

Estimation procedures have desirable asymptotic properties.

Application to real data demonstrates practical utility.

Abstract

In many applications, data are observed as matrices with temporal dependence. Matrix-variate time series modeling is a new branch of econometrics. Although stylized facts in several fields, the existing models do not account for regime switches in the dynamics of matrices that are not abrupt. In this paper, we extend linear matrix-variate autoregressive models by introducing a regime-switching model capable of accounting for smooth changes, the matrix smooth transition autoregressive model. We present the estimation processes with the asymptotic properties demonstrated with simulated and real data.