The sample period is relatively short so our conclusions need to be cautious. And forecast accuracy is not the same as policy acumen. Nonetheless, Chart 3 suggests that, in a complex forecasting task, there is little to choose between experts and 37  non-experts. Tetlock’s second key point, which qualifies his first, is that not everyone can be a super-forecaster. A randomly-chosen panel of non-experts cannot by itself be expected to outperform the experts. Chart 3 demonstrates that. Super-forecasting is not simply a law of large numbers. Tetlock shows that non-expert super-forecasters have particular characteristics, including inquisitiveness and independence of mind. Only those with these characteristics consistently outperform the experts. We do not have a clean test of this hypothesis for our inflation forecasts. But just imagine the Bank had been able to identify in advance one “super-forecaster” among the pool of outside forecasters. How much would that have improved its forecasts over the sample period? The gap in two year-ahead forecast errors between the MPC and the best external forecaster in the panel of outside forecasters is around ¼ percentage point. 20/20 hindsight is a wonderful thing, but this gives some measure of the scope for forecast improvement. 38  If we take Tetlock’s points together, they suggest that there is real value in seeking the views of non-experts to help inform difficult decisions. But there are limits to these benefits. It is unrealistic to expect everyone to become a super-forecaster.
For example, “popular narratives” can emerge which shape collective expectations among the public – optimism or pessimism, exuberance or depression – and which can then drive aggregate economic fluctuations. 19  Recent research has demonstrated just how powerful those emergent popular narratives can be, economically and financially. As one example, the work of Nick Bloom and co-authors has constructed various measures of businesses’ degree of uncertainty around key issues, based on the words used in media reporting. 20  These narrative-based measures of uncertainty have been found to be important for  explaining the investment behaviour of companies over time. David Tuckett and co-authors have constructed measures of the popular narratives used by participants in 21  financial markets. They find that these narratives can play an important role in explaining asset price  dynamics, over and above the impact of macro-economic fundamentals. In a similar spirit, the work of Michael Bailey and co-authors has found, using Facebook data, an important role for shared social narratives in explaining behaviour in the housing market. 22  At a macroeconomic level, the work of George Akerlof and Robert Shiller has looked at the popular narratives which emerge during periods of boom and bust. 23  Using words extracted from newspapers, they  find the prevailing popular narratives about the economy have played a significant role in accounting for the heights of the peaks and depths of the troughs during macro-economic booms and busts. Public expectations, embedded in the stories they tell, are a key macro-economic driver.