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


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)
JournalStructural Equation Modeling
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'Fitting Multilevel Vector Autoregressive Models in Stan, JAGS, and Mplus'. Together they form a unique fingerprint.

Cite this