在高维非静止VAR模型中联合结构断裂检测和参数估计.
Abolfazl Safikhani1, Ali Shojaie2
1Department of Statistics, University of Florida.
这项研究引入了一种分析随时间变化的时间序列数据的新方法. 它准确地识别了结构断裂,并估计了高维断面向量自回归 (VAR) 模型中的模型参数.
科学领域:
- 统计 统计 统计 统计
- 时间序列分析时间序列分析
- 计量经济学 计量经济学
背景情况:
- 对于现实世界时间序列,静态性假设往往是不现实的.
- 零件式静止,允许在多个变化点进行模型更改,提供了一个更现实的框架.
- 高维数据为变化点检测和参数估计带来了独特的挑战.
研究的目的:
- 开发一种可靠的方法,同时估计高维块向量自回归 (VAR) 模型中的变化点和参数.
- 解决时间序列分析中传统静态性假设的局限性.
- 为复杂,不断变化的数据集提供可靠的程序.
主要方法:
- 一个三阶段程序,将处罚最小方程与初始变化点估计的总变化处罚相结合.
- 重构变化点检测作为一个高维变量选择问题.
- 开发一个选择标准,以精确估计过高的变化点,并随后对细分市场进行VAR参数估计.
主要成果:
- 拟议的方法始终检测变化点的数量和位置.
- 准确和一致的估计VAR参数在识别的细分市场内.
- 通过模拟和现实世界的数据应用来证明有效性.
结论:
- 开发的程序提供了一个统计学上合理和有效的方法来分析静止时间序列.
- 它克服了与高维度和多个变化点相关的挑战.
- 为研究人员和从业人员处理动态时间序列数据提供了有价值的工具.
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