Combining model-based near-term GDP forecasts and judgmental forecasts: a real-time exercise for the G7 countries

We investigate the effects of combining model‐based near‐term GDP forecasts and judgmental (quarterly) forecasts by professional analysts (Consensus forecasts) in a real‐time setting for the G7 countries over the years 1999–2013. Model‐based forecasts are produced by a dynamic factor model (DFM). We consider as combination schemes the weighted average and the linear combination. Combining with subjective information delivers sizable gains in forecasting ability of statistical models for all countries except Japan, even when subjective forecasts are somewhat dated. Accuracy gains are much more pronounced in the volatile period after 2008 due to a marked improvement in predictive power of Consensus forecasts relative to the DFM. A possible explanation is that mechanical models may be more vulnerable to extreme observations in estimation samples. Consensus forecasts are superior at the moment of publication for most countries since 2008. For some countries forecast combinations can improve upon Consensus forecasts in between the quarterly release dates of the Consensus survey.

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Recommended citation: Jansen, W.J.J. and J.M. de Winter (2018), Combining model-based near-term GDP forecasts and judgmental forecasts: A real-time exercise for the G7 countries, Oxford Bulletin of Economics and Statistics 80(6), 1213-1242.