We derive conditions for decomposition and collapsibility of graphical interaction models for multivariate time series. These properties enable us to perform stepwise model selection under certain restrictions. For illustration, we apply the results to a multivariate time series describing the...

We investigate the possibility of exploiting partial correlation graphs for identifying interpretable latent variables underlying a multivariate time series. It is shown how the collapsibility and separation properties of partial correlation graphs can be used to understand the relation between...