I'm doing multistep forecasts of univariate time series and a wide range of exogenous leading indicator variables are available. Therefore I'm looking for ways to optimally select and/or combine forecasts. Hanson (2010, "Multi-Step Forecast Model Selection") proposes a leave-h-out cross validation criterion which leaves out the observations t-h+1 up to t-h-1 to compute the forecast error of the observation at time t.
The method looks very appealing since leave-one-out CV tends to overfit my data in multistep forecasts. However, I have barely seen any work based on the method. Does anyone have any experience with leave-h-out cross validation? What is your opinion on the method? Which procedure do you prefer to evaluate multistep forecasts?
Any comments are appreciated. Thank you.