Fan charts were pioneered by the Bank of England and Riksbank and provide a visually

appealing means to convey the uncertainty surrounding a forecast. This paper describes a

method for parameterising fan charts around GDP growth forecasts by which the degree of

uncertainty is based on past forecast errors, but the skew is derived from a probit modelbased

assessment of the probability of a future downturn. The probit-based fan charts

clearly out-perform the Bank of England and Riksbank approaches when applied to

forecasts made immediately preceding the Global Financial Crisis. These examples also

highlight weaknesses with the Bank of England and Riksbank approaches.

• The Riksbank approach implicitly assumes that forecast errors are normally

distributed, but over a long track record this is unlikely to be the case because

forecasters are generally poor at predicting downturns, which leads to bias and skew

in the pattern of forecast errors. Thus, the Riksbank fan chart is neither an accurate

representation of past forecast errors, nor is it a reflection of the risk assessment

underlying the forecast.

• The Bank of England approach relies heavily on the judgment of the members of

the Monetary Policy Committee to assess risks. However, even when they have

correctly foreseen the nature of future risks, the quantitative translation of these

risks into the fan chart skew has been too timid. Perhaps one reason for this is that

the fan chart prediction intervals based on historical forecast errors already appear

quite wide so that inflating them by adding skew may appear embarrassing (at least

ex ante).

The approach advocated in this paper addresses these weaknesses by recognising that

forecast errors are not symmetrical: firstly, this leads to more compressed prediction

intervals in the upper part of the fan chart (representing the possibility of under-prediction);

and secondly, using the large forecast errors from past downturns to calibrate downward

skew clearly supports a more bold approach when there is a risk of a downturn. A weakness

of the probit model-based approach is that it will not predict atypical downturns. For

example, in the current conjuncture it would not pick up risks associated with a ‘no deal’

Brexit or a global trade war. However, a downturn triggered by atypical events may be

more severe if risk factors describing a typical business-financial cycle are also high.