 Combining economic forecasts through information measuresBlanca Moreno and Ana López-Menéndez () Applied Economics Letters, 2007, vol. 14, issue 12, 899-903 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 (Perez, 1985). The empirical behaviour of both procedures is tested over a pool of forecasts referring to Spanish economic growth. Date: 2007 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:apeclt:v:14:y:2007:i:12:p:899-903 Ordering information: This journal article can be ordered from DOI: 10.1080/13504850600689964 Access Statistics for this article Applied Economics Letters is currently edited by Anita Phillips More articles in Applied Economics Letters from Taylor & Francis Journals |
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