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• W2942127451 abstract "Forecast combination methods reduce the information in a vector of forecasts to a single combined forecast by using a set of combination weights. Although there are several methods, a typical strategy is the use of the simple arithmetic mean to obtain the combined forecast. A priori, the use of this mean could be justified when all the forecasters have had the same performance in the past or when they do not have enough information. In this paper, we explore the possibility of using entropy econometrics as a procedure for combining forecasts that allows to discriminate between bad and good forecasters, even in the situation of little information. With this purpose, the data-weighted prior (DWP) estimator proposed by Golan (2001) is used for forecaster selection and simultaneous parameter estimation in linear statistical models. In particular, we examine the ability of the DWP estimator to effectively select relevant forecasts among all forecasts. We test the accuracy of the proposed model with a simulation exercise and compare its ex ante forecasting performance with other methods used to combine forecasts. The obtained results suggest that the proposed method dominates other combining methods, such as equal-weight averages or ordinal least squares methods, among others." @default.
• W2942127451 created "2019-05-03" @default.
• W2942127451 creator A5029050774 @default.
• W2942127451 creator A5032086913 @default.
• W2942127451 creator A5056705360 @default.
• W2942127451 date "2019-04-23" @default.
• W2942127451 modified "2023-10-09" @default.
• W2942127451 title "A Data-Weighted Prior Estimator for Forecast Combination" @default.
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• W2942127451 doi "https://doi.org/10.3390/e21040429" @default.
• W2942127451 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7514918" @default.
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• W2942127451 hasPublicationYear "2019" @default.
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