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Crowdsourcing of Economic Forecast : Combination of Combinations of Individual Forecasts Using Bayesian Model Averaging

Economic forecasts are essential in our daily lives. Accordingly, we ask the following questions: (1) Can we have an improved prediction when we additionally combine combinations of forecasts made by various institutions? (2) If we can, then what method of additional combination will be preferred? We non-linearly combine multiple linear combinations of existing forecasts to form a new forecast (“combination of combinations”), and the weights are given by Bayesian model averaging. In the case of forecasting South Korea’s real GDP growth rate, this new forecast dominates any single forecast in terms of root-mean-square prediction errors. When compared with simple linear combinations of forecasts, our method works as a “hedge” against prediction risks, avoiding the worst combination while maintaining prediction errors similar to those of the best combinations
Year of publication:
[2021]
Authors: Rhee, Tae-hwan ; Ryu, Keunkwan
Publisher: [S.l.] : SSRN
Subject: Bayes-Statistik | Bayesian inference | Prognoseverfahren | Forecasting model | Wirtschaftsprognose | Economic forecast | Crowdsourcing | Frühindikator | Leading indicator | Theorie | Theory
Extent:
1 Online-Ressource (28 p)
Type of publication: Book / Working Paper
Language: English
Notes:
In: Seoul Journal of Economics, Vol. 34, No. 1, 2021
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments February 28, 2021 erstellt
Classification: C53 - Forecasting and Other Model Applications ; E37 - Forecasting and Simulation
Source:
ECONIS - Online Catalogue of the ZBW
Persistent link: https://ebvufind01.dmz1.zbw.eu/10013244933
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