Bayesian estimation of a multivariate TAR model when the noise process distribution belongs to the class of Gaussian variance mixtures
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AI 根据所提供的论文材料归类：摘要明确研究多变量门限自回归时间序列的贝叶斯估计和预测。
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高斯方差混合噪声下多变量门限自回归模型的贝叶斯估计
- 研究问题
- 多变量非线性时间序列存在厚尾或异常观测时，如何在门限自回归模型中进行贝叶斯估计、推断和预测，并选择区制数量与滞后阶数？
- 方法与思路
- 作者用高斯方差混合分布描述噪声，通过 MCMC 估计模型参数，并以 DIC 或 WAIC 选择区制数及自回归阶数。
- 材料陈述的贡献
- 摘要称，方法允许 Student-t 等多种厚尾噪声分布，可能减弱异常观测的影响，并提供 R 库、模拟及两组真实时间序列分析。
- 阅读关注点
- 应核对先验设定、门限与区制识别、MCMC 收敛诊断，以及 DIC 和 WAIC 的使用方式、厚尾稳健性与样本外预测表现。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出先验、算法诊断或预测数值，不能确认厚尾设定在各类数据中的改进。
原文摘要
A threshold autoregressive (TAR) model is a powerful tool for analyzing nonlinear multivariate time series, which includes special cases like self-exciting threshold autoregressive (SETAR) models and vector autoregressive (VAR) models. In this paper, estimation, inference, and forecasting using the Bayesian approach are developed for multivariate TAR (MTAR) models considering a flexible setup, under which the noise process behavior can be described using not only the Gaussian distribution but also other distributions that belong to the class of Gaussian variance mixtures, which includes Student-t, Slash, symmetric hyperbolic, and contaminated normal distributions, which are also symmetric but are more flexible and with heavier tails than the Gaussian one. Inferences from MTAR models based on that kind of distribution may be less affected by extreme or outlying observations than those based on the Gaussian one. All parameters in the MTAR model are included in the proposed MCMC-type algorithm, except the number of regimes and the autoregressive orders, which can be chosen using the Deviance Information Criterion (DIC) and/or the Watanabe-Akaike Information Criterion (WAIC). A library for the language and environment for statistical computing R was also developed to assess the effectiveness of the proposed methodology using simulation studies and analysis of two real multivariate time series.