No 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.JEL Codes: C11, C22, C53

This paper considers the Split-Then-Combine (STC) approach (Arroyo and de Juan, 2014) to combine forecasts inside the simplex space, the sample space of positive weights adding up to one. As it turns out, the simplicial statistic given by the center of the simplex compares favorably against the fixed-weight, average forecast. Besides, we also develop a Combine-After-Selection (CAS) method to get rid of redundant forecasters. We apply these two approaches to make out-of-sample one-step ahead combinations and subcombinations of forecasts for several economic variables. This methodology is particularly useful when the sample size is smaller than the number of forecasts, a case where other methods (e.g., Least Squares (LS) or Principal Component Analysis (PCA)) are not applicable.