Split-then-Combine Method for out-of-sample Combinations of Forecasts

Abstract

Relative forecast performance of forecast units may periodically evolve over time. Therefore, it is desirable to take into account their forecast periodicity when forming forecast combinations. When dealing with small samples and small number of models, using panels is an efficient way of pulling out the additional information provided by that periodicity in the data. We capture this periodic information with different weights at different periods that we then keep in the out-of-sample combination. As in the simple average, we do not estimate weights, but instead compute them from panels of forecasts taken as given data. Empirical and bootstrap exercises illustrate the superiority of this method over fixed weight schemes.