The majority of the automatic model selection algorithms like
ets (https://robjhyndman.com/publications/automatic-forecasting/) are using information criteria (AIC, AICc or BIC) as opposed to Cross Validation for model selection.
This makes a lot of sense for automatic model selection algorithms as it's way faster and you don't need to design your CV protocol (i.e. CV splits and horizon). These ICs approximate the one-step forecast errors. But what happens if we're interested good forecast performance for longer forecast horizons e.g (monthly data with a 12-24 step horizon)?
Is CV going to significantly outperform Information Criteria for longer forecasting horizons? (to a point where it makes sense to invest the time to use CV instead)
I'm not aware of any papers/articles making this comparison, but I think it makes for interesting discussion!