格兰杰因果关系:一篇评论和最近的进展
1Department of Biostatistics, University of Washington, Seattle, Washington 98195-4322, USA.
Annual review of statistics and its application
|October 16, 2023
概括
格兰杰因果关系,一个时间序列分析工具,为因果推理的有效性进行辩论. 最近的进步扩大了其应用范围,超越了简单的模型,将其应用于复杂,高维和非线性数据.
科学领域:
- 时间序列分析时间序列分析
- 计量经济学 计量经济学
- 神经科学是一个神经科学.
- 基因组学就是基因组学.
背景情况:
- 50多年前引入的格兰杰因果关系,被广泛用于时间序列分析.
- 它用于推断因果关系的有效性仍然是持续辩论的主题.
- 从历史上看,计算的局限性限制了格兰杰因果关系在双变向量自回归过程中.
研究的目的:
- 审查围绕格兰杰因果关系的早期发展和辩论.
- 讨论解决传统格兰杰因果关系方法局限性的最新进展.
- 突出适用于复杂和多样化的时间序列数据的新模型.
主要方法:
- 审查历史格兰杰因果关系框架及其局限性.
- 探索时间序列分析的最新方法论进步.
- 讨论适用于高维,非线性和非高斯数据的模型.
主要成果:
- 最近的进展已经克服了早期格兰杰因果关系模型的局限性.
- 新的方法使得高维,非线性和非高斯时间序列的分析成为可能.
- 该框架现在支持部分采样和混合频率时间序列分析.
结论:
- 格兰杰因果关系已经远远超出了它最初的两种应用范围.
- 现代方法提高了格兰杰因果关系的稳定性和适用性,用于因果推理.
- 审查的进展扩大了格兰杰因果关系在各种科学领域的实用性.
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