An approach to increasing forecast‐combination accuracy through VAR error modeling

We consider a situation in which the forecaster has available M individual forecasts of a univariate target variable. We propose a 3‐step procedure designed to exploit the interrelationships among the M forecast‐error series (estimated from a large time‐varying parameter VAR model of the errors, using past observations) with the aim of obtaining more accurate predictions of future forecast errors. The refined future forecast‐error predictions are then used to obtain M new individual forecasts that are adapted to the information from the estimated VAR. The adapted M individual forecasts are ultimately combined and any potential accuracy gains from the adapted combination forecasts analyzed. We evaluate our approach in an out‐of‐sample forecasting analysis, using a well‐established 7‐country data set on output growth. Our 3‐step procedure yields substantial accuracy gains (in terms of loss reductions of up to 18%) for the simple average and three time‐varying‐parameter combination forecasts.