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

Yanling Li, Julie Wood, Linying Ji, Sy Miin Chow, Zita Oravecz

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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 and comparisons of options to fit Bayesian mlVAR models using Stan, JAGS and Mplus, supplemented with a Monte Carlo simulation study. An empirical example is used to demonstrate the utility of mlVAR models in studying intra- and inter-individual variations in affective dynamics.

Original languageEnglish (US)
Pages (from-to)452-475
Number of pages24
JournalStructural Equation Modeling
Issue number3
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


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