Combining economic forecasts through information measures

Abstract

The increasing number of prospective sources and methods provides a wide variety of forecasts for a given economic variable. Therefore, the theory suggests the convenience of combining the individual results to obtain a single aggregated prediction. The traditional methods for combining forecasts are based on the relative past performance of the forecasts to be combined. However, the number of forecasters is increasing considerably in the last years so it is not possible to have enough information about their past forecast task. This article focuses on the information theory as a framework to combine experts’ forecasts when information is limited. More specifically, we use the principle of entropy maximization to obtain a combined forecast from Shannon's measure (1948) and we also propose its extension to the quadratic uncertainty measure (Pérez, 1985). The empirical behaviour of both procedures is tested over a pool of forecasts referring to Spanish economic growth.