Fitting Multilevel Vector Autoregressive Models in Stan, JAGS, and Mplus.

ABSTRACT: The influx of intensive longitudinal data creates a pressing need for complex modeling tools that help enrich our understanding of how individuals change over time. Multilevel vector autoregressive (mlVAR) models allow for simultaneous evaluations of reciprocal linkages between dynamic processes and individual differences, and have gained increased recognition in recent years. High-dimensional and other complex variations of mlVAR models, though often computationally intractable in the frequentist framework, can be readily handled using Markov chain Monte Carlo techniques in a Bayesian framework. However, researchers in social science fields may be unfamiliar with ways to capitalize on recent developments in Bayesian software programs. In this paper, we provide step-by-step illustrations a

SUBMITTER: Li Y 

PROVIDER: S-EPMC9122119 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature