When a policy institution is putting together a forecast, it will never be derived from just one model: it will come from various different sources. Then everything can be cross-checked for consistency. Statistical models are used to predict the very short term – what is called nowcasting – while structural models are used to construct scenarios further ahead, say one or two years. Crucially, from a forecast, you can put together a story. I see a forecast as a story-telling device.
This is an important point: constructing a forecast makes you cross-check your story for coherence. For example, if you say something about the exchange rate, is it consistent with what you are saying about inflation? What are you saying about unemployment and about GDP? We know because of the historical relationship between these variables that there must be a coherence. Constructing a forecast allows you to bring these things together.
Of course, the uncertainties are huge. And if, at some point in the medium-term future, you look back at your forecasts, you are quite likely to find that they were incredibly inaccurate.
But that doesn’t mean that it was wrong to even try to make the forecasts in the first place. The process is useful because it enforces the discipline of having to be internally coherent. Also, it allows an institution such as a central bank to ask “what if…?” questions. It provides a framework to test what the different possible outcomes would be of a range of policy moves. The forecast may not correctly predict the exact outcome: again, there are likely to be too many external variables over which the bank has no control and which will affect the result. But at least the central bank can make comparisons between available policy choices: “What if we did this as opposed to doing that?” Or “What if GDP were to fall by 2 percent? What would happen to banks’ balance sheets?”
None of this is to deny the central point that forecasts – even for the medium-term, let along the long-term – are likely to turn out to be inaccurate. But one key element of the whole process – and perhaps one which the public doesn’t appreciate – is that forecasts will themselves acknowledge their own uncertainty. With a forecasting model, you can make a probabilistic statement; for example, “the inflation rate is likely to be X in a year’s time, and the likelihood of its being within a certain margin either side of that figure is, say, 50 percent. In two years’ time, the inflation rate is likely to be Y and the likelihood of its being within the same margin is, say, 25 percent.” The further ahead the forecast, the greater the uncertainty.
It is crucial to acknowledge uncertainty, to try to quantify it and to make it part of the forecast itself. It is possibly this element of forecasting that the public doesn’t understand – which helps explain why forecasters get such a bad press. People aren’t educated in this kind of probabilistic way of looking at the world.
As forecasters try to look further and further into the future, their predictions become ever more uncertain. For example, an investment bank may predict that China will become the world’s largest economy in such-and-such a year. Well, maybe, maybe not. This is the sort of forecast that that makes a stab in the dark look like laser surgery.
Forecasts that try to predict with confidence how things will look even in the medium-term – say two years ahead – are likely to be wrong. Or rather, the margin of error is so large as to make the forecast almost meaningless. But that doesn’t stop their being useful as story-telling devices – enforcing internal consistency and providing useful answers to those “what if?...” questions.
Is there any type of forecasting that produces sound numbers where the errors are likely to be small? Yes. That’s “nowcasting” – using all the available data to produce an estimate of what has happened in the very recent past, the position now and the very-short term future.