A0786
Title: Forecasting macroeconomic risks in the UK
Authors: David Aikman - Kings College London (United Kingdom)
Rhys Bidder - Kings College London (United Kingdom)
Simon Lloyd - Bank of England (United Kingdom) [presenting]
Giulia Mantoan - Bank of England (United Kingdom)
Simone Maso - Kings College London (United Kingdom)
Aditya Mori - Kings College London (United Kingdom)
Matthew Tong - Bank of England (United Kingdom)
Abstract: Statistical metrics of UK macroeconomic risks are constructed, building quantile-regression density forecasts for inflation and GDP growth. The models account for the UK's position as a small-open economy and capture time variation in tail risks, owing to variation in economic and financial conditions, in addition to changes in central forecasts. To highlight how the statistical models of macroeconomic risk can provide valuable real-time signals about the balance of risks, a battery of tests is used to compare the predictive distributions to those captured in the Bank of England's fan charts. The fitted densities for growth are at least as well calibrated as the fan charts, outperforming the fans in terms of relative accuracy. Although the estimates for inflation perform similarly to the fans over the last two decades overall, they do better capture economic narratives when inflation deviates from the target. These tools can contribute to a broader suite for quantifying macroeconomic risks in the UK, with regular evaluation of density forecasts necessary to ensure that the toolkit remains fit for purpose as the constellation of shocks hitting the UK economy evolves.
