对计数的双变量时间序列模型的监测参数变化
1Department of Statistics, Seoul National University, Seoul, 08826 South Korea.
概括
本研究引入了一种新的在线监测方法,使用累积和 (CUSUM) 过程来检测像BIGARCH和BINAR这样的双变数时间序列模型中的参数变化.
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:
- 检测时间序列中的参数变化对于准确的建模至关重要.
- 两变计数时间序列模型,如BIGARCH和BINAR,被广泛使用,但需要强大的监控程序.
- 现有的方法可能无法充分解决这些特定模型中的参数不稳定性.
研究的目的:
- 开发一种有效的在线监测程序,用于检测双变数计数时间序列中的参数变化.
- 将累积和 (CUSUM) 过程应用于用于改变检测的BIGARCH和BINAR模型的余量.
- 为拟议的监测技术建立理论控制极限.
主要方法:
- 使用从二变整数值的通用自回归异构 (BIGARCH) 和自回归 (BINAR) 模型中得出的标准化余数.
- 构建一个累积总和 (CUSUM) 过程,基于这些残留物进行在线监控.
- 开发极限定理来推导CUSUM过程的适当控制极限.
主要成果:
- 拟议的基于CUSUM的监测程序有效地检测双变数计数时间序列中的参数变化.
- 极限定理为设置控制极限提供了理论基础,确保可靠的检测.
- 模拟研究和真实数据分析证明了该方法的实际有效性和性能.
结论:
- 开发的在线监测程序为检测BIGARCH和BINAR模型中的参数转移提供了统计学上合理的方法.
- 该方法通过经验研究得到验证,显示其在现实世界应用中的实用性.
- 这项工作为复杂计数数据时间序列的统计过程控制领域做出了贡献.
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