In panel data analysis, I have used multi-level models with random/mixed effects to deal with auto-correlation issues (i.e., observations are clustered within individuals over time) with other parameters added to adjust for some specification of time and shocks of interest. ARMA/ARIMA seem designed to address similar issues.
The resources I've found online discuss either times series (ARMA/ARIMA) or mixed effect models but beyond being build on regression, I don't understand the relationship between the two. Might one want to use ARMA/ARIMA from within a multilevel model? Is there a sense in which the two are equivalent or redundant?
Answers or pointers to resources that discuss this would be great.