publication . Preprint . Research . Article . Book . 2010

Forecast Combinations

Marco Aiolfi; Carlos Capistrán; Allan Timmermann;
Open Access
  • Published: 01 Jun 2010
Abstract
Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the degree of correlation between forecast errors and the relative size of the individual models’ forecast error variances). Although the reasons for the success of simple combin...
Subjects
arXiv: Physics::Atmospheric and Oceanic Physics
ACM Computing Classification System: ComputerApplications_MISCELLANEOUSComputingMilieux_GENERAL
free text keywords: diversification gains; forecast combinations; model misspecification; pooling and trimming; shrinkage methods, C53, E, Factor Based Forecasts, Non-linear Forecasts, Structural Breaks, Survey Forecasts, Univariate Forecasts, Prognoseverfahren, Wirtschaftsprognose, Modellierung, Faktorenanalyse, Factor Based Forecasts, Non-linear Forecasts, Structural Breaks, Survey Forecasts, Univariate Forecasts., jel:C22, jel:C53, jel:E, ddc:330
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publication . Preprint . Research . Article . Book . 2010

Forecast Combinations

Marco Aiolfi; Carlos Capistrán; Allan Timmermann;