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VARX格兰杰分析:神经科学,生理学,社会学和计量经济学的模型
Lucas C Parra1, Aimar Silvan1, Maximilian Nentwich2
1Department of Biomedical Engineering, City College of New York, New York, NY, United States of America.
PloS one
|January 9, 2025
概括
我们介绍了一个带有eXogenous输入 (VARX) 的矢量自回归模型,以将外部影响与复杂系统的内部动态分开. 这种方法澄清了因果关系,并在各种科学领域提供了节的解释.
科学领域:
- 复杂系统分析 复杂系统分析
- 在神经科学,生理学,社会学和经济学的跨学科应用.
背景情况:
- 复杂的系统 (大脑,市场,社会) 的内部动态受到外部因素的影响.
- 区分延迟的外部效应与内部系统动态是一项挑战.
- "因果关系"的格兰杰因果关系框架缺乏与特定模型的明确联系.
研究的目的:
- 为了弥合向量自回归模型与异源输入 (VARX) 和格兰杰因果关系之间的理解差距.
- 提供一种强大的方法,使复杂系统中的外部影响与内部动态脱而出.
- 为各种科学领域应用VARX模型提供实用工具和演示.
主要方法:
- 使用带有eXogenous输入 (VARX) 的矢量自回归模型来建模内部和外部变量之间的延迟相互作用.
- 开发用于VARX模型实现的基本方程和用户友好的代码 (MATLAB,Python,R).
- 将VARX模型应用于来自神经科学,生理学,社会学和经济学的模拟和现实世界的数据.
主要成果:
- VARX模型有效地解开外部影响,避免虚假的相关性,并产生节的系统解释.
- 神经记录的分析表明,长时间的大脑反应可以归因于短时间的外源效应和内部活动.
- 生理学数据显示,VARX可以恢复已知的相互作用,比如影响瞳孔大小的眼睛运动和双向心率-呼吸合.
结论:
- VARX模型为分析复杂系统提供了统计学上严格且实际适用的方法.
- 这种方法通过将内部动态与外部驱动因素分开来增强对因果关系的理解.
- 提供的代码和演示有助于采用VARX模型进行可靠的科学研究.
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