While economics has long been known as the ‘dismal science’, this does not seem to apply to the science of forecasting. With some exceptions – such as the Mayans, or Nouriel Roubini – forecasters, it seems, are just way too cheery and confident.
As forecasting experts Spyros Makridakis and Nassim Taleb pointed out in an article in the International Journal of Forecasting, ‘Empirical evidence has shown that the ability of people to correctly assess uncertainty is even worse than that of accurately predicting future outcomes. Such evidence has shown that humans are overconfident of positive expectations, while ignoring or downgrading negative information.’
A good example of the overly-sunny nature of economic forecasts was the recent crisis. Not only did few economists predict it (Roubini and Taleb were the most famous exceptions), they were also too positive about the recovery, which has proved rather slower than expected.
In April 2007, for example, the IMF said that: ‘Notwithstanding the recent bout of financial volatility, the world economy still looks well set for continued robust growth in 2007 and 2008.’ A year later, in the aftermath of the credit crunch, they were predicting a ‘mild recession’ in the US to be followed by a ‘modest recovery’ in 2009. Instead, US gross domestic product shrank by 3.5 percent in 2009.
Their forecasts for the European recovery were also too positive. In 2011 they were foreseeing a rosy 2.1 percent growth in 2012, rather than the flat-lining which actually occurred. The IMF is far from unique – other organisations such as the OECD failed to spot the dangers, as did surveys of individual economists. Over the last thirty years, according to the New York Times, the average probability forecasters put on the economy lapsing into recession ‘has never risen above 50 percent—until the economy was already in a recession’. So why is it that forecasters are such champions of positive thinking – are they just sunny by nature, or is something else going on?
Part of the reason is that optimism is always popular, especially in areas such as business, because it makes everyone feel good. As psychologist Daniel Kahneman wrote in his book Thinking Fast and Slow, ‘Most of us view the world as more benign than it really is, our own attributes as more favorable than they truly are, and the goals we adopt as more achievable than they are likely to be.’
Not only are we optimistic about the future, but we think we can predict it as well: ‘We also tend to exaggerate our ability to forecast the future, which fosters overconfidence.’ This confidence is particularly valued in times of crisis, since ‘Extreme uncertainty is paralyzing under dangerous circumstances, and the admission that one is merely guessing is especially unacceptable when the stakes are high.’ Because of its role in decision-making, ‘the optimistic bias may well be the most significant cognitive bias.’
Forecasters, it seems, are mirroring a basic human trait in their optimistic stance. Of course, one might expect to find a healthy level of optimism in high-flying leaders and entrepreneurs, who are so busy living on the edge that they have little time to make a rational analysis of how all their decisions have panned out; or to ask how much of their success is due to luck or the efforts of other people rather than their own brilliance. But professional forecasters have the luxury of being able to compare their past predictions with historical data. Surely the desire for accuracy should serve to correct any bias over time?
Unfortunately, this does not seem to be the case. Consider for example the results from the Survey of Professional Forecasters, which is the oldest quarterly survey of macroeconomic forecasts in the United States with data going back to 1968. This survey asks economists to give their predictions of key variables such as GDP, by assigning probabilities to various outcomes – for example the chance of growth being between one and two percent. This allows the compilation of probabilistic forecasts, such as a central forecast along with 90 percent confidence intervals.
As with other such surveys, the forecasters missed the recent crisis, predicting positive growth for the years 2007 to 2009. Perhaps more concerning is that, over a period of more than 40 years, they have consistently overestimated the accuracy of their predictions. If forecasters were good judges of their own abilities, then the true GDP would fall outside the 90 percent confidence intervals only 10 percent of the time. Instead they miss over a quarter of the time. To reflect reality, the 90 percent confidence intervals should be widened to plus or minus a little more than three percent.
Given that forecasters have been repeating the same exercise every quarter for a number of decades, one might think that they would have grown more realistic about the uncertainties involved. At the same time, though, it is easy to understand why they prefer to sound confident. Predicting GDP growth of one percent sounds plausibly savant-like, but predicting growth of somewhere between minus two and four percent is less convincing, and might lead policy makers to go back to other methods of prognostication, such as tea leaves or the reading of animal entrails.
While a bias towards optimism may be adaptive in areas such as business or politics, where an aura of confidence and positivity are likely to attract things like funding and attention, they can also be dangerous if they mean that we do not pick up signals warning of impending disasters; or fail to take into account the full range of possibilities. No one likes to sound uncertain or pessimistic, least of all forecasters, but sometimes a healthy scepticism is appropriate. As PG Wodehouse wrote in Jeeves and the Unbidden Guest: ‘I rather fancy it’s Shakespeare who says that it’s always just when a fellow is feeling particularly braced with things in general that Fate sneaks up behind him with the bit of lead piping.’