Combining Bayesian VARs with survey density forecasts: does it pay off?

Marta Banbura, Federica Brenna, Joan Paredes and Francesco Ravazzolo

No 2543, Working Paper Series from European Central Bank

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 inflation 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. JEL Classification: C11, C32, C53, E27, E37

Keywords: Entropic tilting; Judgement; Optimal Pooling; Real Time; Survey of Professional Forecasters (search for similar items in EconPapers)
Date: 2021-05
New Economics Papers: this item is included in nep-ets, nep-for and nep-mac
Note: 810771
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20212543

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