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.