个体动态因素模型中的路径和方向发现:具有潜在变量的正则化混合统一结构方程模型》(A Regularized Hybrid Unified Structural Equation Modeling with Latent Variable.

IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Multivariate Behavioral Research Pub Date : 2024-09-01 Epub Date: 2024-07-26 DOI:10.1080/00273171.2024.2354232
Ai Ye, Kenneth A Bollen
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引用次数: 0

摘要

对具有测量误差的多变量时间序列数据建模的呼声越来越高。将潜在因子与向量自回归(VAR)模型相结合,就产生了动态因子模型(DFM),在该模型中,因子序列内部、因子与观测时间序列之间或两者之间都存在动态关系。然而,目前的 DFM 代表和估计存在一些局限性:(1) 动态部分包含有向或无向的同期关系,但不能同时包含这两种关系;(2) 在探索性 DFM 中选择最优模型是一个挑战;(3) 几乎没有研究过模型选择中的结构性错误规范的后果。本文通过混合 VAR 表示法和利用 LASSO 正则化选择动态隐含工具变量、两阶段最小二乘法(MIIV-2SLS)估计来推进 DFM。我们提出的方法通过稳健的估算突出了动态关系建模方向的灵活性。我们旨在为研究人员在以人为中心的动态评估中的模型选择和估计提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Path and Direction Discovery in Individual Dynamic Factor Models: A Regularized Hybrid Unified Structural Equation Modeling with Latent Variable.

There has been an increasing call to model multivariate time series data with measurement error. The combination of latent factors with a vector autoregressive (VAR) model leads to the dynamic factor model (DFM), in which dynamic relations are derived within factor series, among factors and observed time series, or both. However, a few limitations exist in the current DFM representatives and estimation: (1) the dynamic component contains either directed or undirected contemporaneous relations, but not both, (2) selecting the optimal model in exploratory DFM is a challenge, (3) the consequences of structural misspecifications from model selection is barely studied. Our paper serves to advance DFM with a hybrid VAR representations and the utilization of LASSO regularization to select dynamic implied instrumental variable, two-stage least squares (MIIV-2SLS) estimation. Our proposed method highlights the flexibility in modeling the directions of dynamic relations with a robust estimation. We aim to offer researchers guidance on model selection and estimation in person-centered dynamic assessments.

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来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
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