A Smooth Transition Autoregressive Model for Matrix-Variate Time Series

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-04-02 DOI:10.1007/s10614-024-10568-7
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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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矩阵变量时间序列的平滑过渡自回归模型
摘要 本文提出了一种新的矩阵变量时间序列数据建模方法,该方法考虑了矩阵动态的平滑变化。尽管多个领域的典型事实表明存在平滑非线性,但现有的矩阵变量模型并不考虑非突变的制度转换。为了弥补这一缺陷,我们引入了矩阵平稳过渡自回归模型,这是一种灵活的制度转换模型,能够捕捉矩阵值数据中突然、平稳和无制度变化的情况。我们对估计过程进行了深入研究,并利用模拟数据评估了矩阵变量平稳过渡自回归模型估计器的有限样本性能。最后,我们将该模型应用于现实世界的数据。
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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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