  We assess the relationship between model size and complexity in the
time-varying parameter VAR framework via thorough predictive exercises for the
Euro Area, the United Kingdom and the United States. It turns out that
sophisticated dynamics through drifting coefficients are important in small
data sets, while simpler models tend to perform better in sizeable data sets.
To combine the best of both worlds, novel shrinkage priors help to mitigate the
curse of dimensionality, resulting in competitive forecasts for all scenarios
considered. Furthermore, we discuss dynamic model selection to improve upon the
best performing individual model for each point in time.
