在个体动态因子模型中发现路径和方向:一个规范化的混合统一结构方程模型与潜在变量
1Lehrstuhl für Psychologische Methodenlehre & Diagnostik, Department Psychologie, Ludwig-Maximilians-Universität München, Munich, Germany.
Multivariate behavioral research
|July 26, 2024
概括
这项研究推进了具有测量误差的多变量时间序列的动态因子模型 (DFM). 它引入了一种新的混合VAR方法,用于可靠的估计和模型选择,改进动态关系建模.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 心理测量 心理测量 心理测量
背景情况:
- 多变量时间序列数据通常包含测量错误,需要先进的建模技术.
- 动态因子模型 (DFM) 将潜在因子与矢量自回归模型 (VAR) 结合起来,以捕捉复杂的时间依赖.
- 现有的DFM在建模指导和非指导的同时关系以及最佳模型选择方面存在局限性.
研究的目的:
- 为了解决当前动态因子模型 (DFM) 对具有测量误差的时间序列数据的局限性.
- 提出一个新的DFM框架,包括混合VAR表示和LASSO规范化.
- 为以人为中心的动态评估提供对模型选择和估计的指导.
主要方法:
- 为DFM开发混合VAR表示,以捕捉各种动态关系.
- 使用LASSO规范化来选择动态暗示仪表变量.
- 应用一个两阶段最小平方 (MIIV-2SLS) 估计策略来进行可靠的参数估计.
主要成果:
- 拟议的方法在模拟多变量时间序列内的动态关系的方向方面提供了更大的灵活性.
- 拉索调整有助于选择合适的仪器变量,提高模型稳定性.
- MIIV-2SLS估计提供了一个强大的方法,用于测量误差的DFM.
结论:
- 先进的DFM框架为模拟具有测量误差的多变量时间序列提供了更全面的方法.
- 拟议的方法提高了准确捕捉复杂的动态相互依存的能力.
- 这项研究为动态评估和相关领域的研究人员提供了有价值的工具和指导.
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