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A re‐evaluation of the quasi‐bayes approach to the linear combination of forecasts
Author(s) -
Faria A. E.,
Souza R. C.
Publication year - 1995
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980140605
Subject(s) - bayes' theorem , computer science , bayesian probability , variety (cybernetics) , econometrics , machine learning , artificial intelligence , operations research , economics , mathematics
The subjective forecasts used in decision analysis should, in principle, synthesize all available evidence about the subject in analysis. In this manner, when part of the evidence consists of a variety of forecasting models or expert opinions, Decision Theory requires the decision maker to formulate a combination of these predictors. This work takes into account the Bayesian methodologies out performance and quasi‐Bayes , as well as the classical model of optimal combination , all applied to the linear combination of petroleum price forecasts, generated by experts from Petrobrás—the Brazilian oil company—for several international markets. It presents a theoretical description of the methodologies followed by a comparative analysis between performances of the best experts' forecasts and combinations. The performances and features of these combinations are also compared.