Evaluating UK point and density forecasts from an estimated DSGE model: the role of off-model information over the financial crisis

Working Paper No. 538
By Nicholas Fawcett, Lena Körber, Riccardo M Masolo and Matt Waldron 

This paper investigates the real-time forecast performance of the Bank of England’s main DSGE model, COMPASS, before, during and after the financial crisis with reference to statistical and judgemental benchmarks. A general finding is that COMPASS’s relative forecast performance improves as the forecast horizon is extended (as does that of the Statistical Suite of forecasting models). The performance of forecasts from all three sources deteriorates substantially following the financial crisis. The deterioration is particularly marked for the DSGE model’s GDP forecasts. One possible explanation for that, and a key difference between DSGE models and judgemental forecasts, is that judgemental forecasts are implicitly conditioned on a broader information set, including faster-moving indicators that may be particularly informative when the state of the economy is evolving rapidly, as in periods of financial distress. Consistent with that interpretation, GDP forecasts from a version of the DSGE model augmented to include a survey measure of short-term GDP growth expectations are competitive with the judgemental forecasts at all horizons in the post-crisis period. More generally, a key theme of the paper is that both the type of off-model information and the method used to apply it are key determinants of DSGE model forecast accuracy.

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To accompany the publication of Staff Working Paper 538, evaluating the performance of forecast models during the financial crisis, we have released the archive of datasets used during estimation of the COMPASS model.  This contains ‘real-time’ data for each of the forecast rounds considered, from February 2000 to February 2013; thus the data are those that would have been available at the time the forecasts were constructed. We are providing this archive to help researchers conduct their own empirical research using real-time data, by collating all the data used in one place.

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For more details of the modelling work undertaken with these data, please see the working paper.

We regret that we cannot respond to queries about the construction of individual series in the data archive.