Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/233668 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 40 [Issue:] 4 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2021 [Pages:] 686-699
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
We consider a situation in which the forecaster has available M individual forecasts of a univariate target variable. We propose a 3-step procedure designed to exploit the interrelationships among the M forecast-error series (estimated from a large time-varying parameter VAR model of the errors, using past observations) with the aim of obtaining more accurate predictions of future forecast errors. The refined future forecast-error predictions are then used to obtain M new individual forecasts that are adapted to the information from the estimated VAR. The adapted M individual forecasts are ultimately combined and any potential accuracy gains from the adapted combination forecasts analyzed. We evaluate our approach in an out-of-sample forecasting analysis, using a well-established 7-country data set on output growth. Our 3-step procedure yields substantial accuracy gains (in terms of loss reductions of up to 18%) for the simple average and three time-varying-parameter combination forecasts.
Subjects: 
Bayesian VAR estimation
dynamic model averaging
forecast combinations
forgetting factors
large time‐varying parameter VARs
state‐space model
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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