题目：Generalized multivariate threshold autoregressive models with linearly partitioned threshold space

报告人：袁淦

讲座时间：2026年10月14日 15：00

地点： 综合楼644会议室

报告人简介：

袁淦，现任香港城市大学生物统计学系助理教授。香港中文大学计量金融与风险管理学士及硕士，美国哥伦比亚大学统计学博士。目前担任《数据科学期刊》(Journal of Data Science) 副主编。在学术研究领域，研究兴趣广泛，主要聚焦于非参数统计、时间序列分析、经验过程理论、函数型数据分析。研究成果陆续发表于《统计学年刊》(The Annals of Statistics)、《机器学习研究期刊》(JMLR)、《美国国家科学院院刊》(PNAS) 以及《SIAM 数据科学数学期刊》(SIMODS)等国际学术期刊上发表。

报告摘要：

We consider a k-dimensional multiple-regime vector threshold autoregressive model, in which the regime-switching mechanism is governed by a bivariate threshold variable. Specifically, the regimes are induced by a partition of the threshold space by an unknown number of threshold lines. Within each regime, the process follows a specific vector autoregressive (VAR) model. We formulate the model selection and parameter estimation into a minimization problem based on the Minimum Description Length (MDL) principle and estimate the number of threshold lines, parametric forms of threshold lines and VAR model parameters in each regime simultaneously. Theoretically, we show that the MDL estimators of threshold lines are n-consistent and characterize their limiting distribution. This requires novel proving techniques of introducing a new functional space  for the local MDL difference functions and establishing weak convergence results therein. Finally, we conduct some empirical studies with simulated datasets and perform real data analyses on U.S. interest rates and U.S. GNP Data.