In an uncertain world, the limitations of forecasting are becoming increasingly obvious, but there are some useful lessons to be learnt.
1 Worse than fallible
It’s not just that economic forecasts are often wrong, they tend to let you down when you need them most. Nobel prize winning economist, Paul Samuelson said: the share market has predicted nine out of the last five recessions. By comparison, economic forecasters rarely predict any. They are typically caught out by shocks.
2 But decisions need forecasts
Economists are well aware of the shortcomings of forecasts. But they have no option but to keep making them. People also keep asking for them- because they can provide a way of justifying decisions, which are often actually made for other reasons. If things go wrong, you can always blame the expert.
3 Deceptive air of certainty
In an uncertain world, forecasts and the story behind them give us comfort. But if we place too much faith in them they may lead us astray. Such overconfidence gives us an illusion of control. Forecasters often ignore the advice that “it’s better to be vaguely right rather than exactly wrong”.
Confident and dogmatic forecasters often grab headlines. Unfortunately, their strong but simple narratives are often way off the mark. The devil is usually in the detail.
4. Charlatans rule
Confident and dogmatic forecasters often grab the headlines. Unfortunately, their strong but simple narratives are often way off the mark. The devil is often in the detail, so we should be wary of forecasts based on a single factor. It’s also good to try to attach a probability to your projections. Admitting uncertainty and being open about your degree of confidence in your prediction is not a sign of weakness, but helpful realism.
5 Try to be objective
We’re all biased, and forecasts usually reflect that. Forecasters need to understand this, and aim for objectivity. It’s easy to focus too much on the recent and most shocking news. Yet forecasters have a natural human tendency to indulge in elegant, after-the-fact rationalisations. Hindsight is indeed a wonderful thing.
6 The more the merrier
By making more forecasts, you have more chances to” ‘win” by getting some right. More seriously, it also gives a forecaster more chances to learn. Rather than sticking with a forecast that is off track, it is important to take on board new information.
7 Unlike traders, forecasters get marks for style
Financial market traders are judged by the profits they make. As a result, they’re happy to get it right for the wrong reasons. But relying on luck is dangerous. In contrast, forecasting is not just about accuracy. Aside from providing provocative or comforting stories, forecasts are often about survival rather than getting it right. And accuracy is often relative – just as when faced with a charging bull, your survival depends not on outrunning the bull but your neighbour.
8 It’s more about why than what
Meaningful forecasts come with useful explanations. They should provide the user with a framework of thought and wisdom, rather than just knowledge and information. A common error is to assume that because two things are moving together that the two are related or even that one is causing the other.
9 Stories can help, but also hinder
One way of dealing with countless future possibilities is to construct scenarios. Rather than “betting the ranch” on a single forecast, scenarios are stories built around the key drivers of the outlook in terms of their probability and impact. The stories give users benchmarks for answering the question ”what kind of a world are we in?”. This can help people to think through potentially disruptive events beforehand, because they are liable to make bad decisions when they’re under stress.
10 Magnitudes aren’t always necessary
Often, we don’t need precise forecasts. Sometimes working out the direction we should go is enough. That said, it also helps to get the timing right. This is particularly true of questions that have binary outcomes. For example, wars and elections are won or lost: we are far more concerned with who wins an election than their margin of victory. Such events are not easy to plug into economic models, and require different methods, with an emphasis on probabilities.