Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/234097 
Year of Publication: 
2021
Series/Report no.: 
ECB Working Paper No. 2543
Publisher: 
European Central Bank (ECB), Frankfurt a. M.
Abstract: 
This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey forecasts by optimally exploiting their properties. To do that, it compares the forecasting performance of optimal pooling and tilting techniques, including survey forecasts for predicting euro area in ation and GDP growth at medium-term forecast horizons using both univariate and multivariate forecasting metrics. Results show that the Survey of Professional Forecasters (SPF) provides good point forecast performance, but also that SPF forecasts perform poorly in terms of densities for all variables and horizons. Accordingly, when the model combination or the individual models are tilted to SPF's first moments, point accuracy and calibration improve, whereas they worsen when SPF's second moments are included. We conclude that judgement incorporated in survey forecasts can considerably increase model forecasts accuracy, however, the way and the extent to which it is incorporated matters.
Subjects: 
Real Time
Optimal Pooling
Judgement
Entropic tilting
Survey of ProfessionalForecasters
JEL: 
C11
C32
C53
E27
E37
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-4543-1
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.