Seeing the future

We crave certainty. We want to feel secure now and we want to feel that we have a pretty good idea about our likely future. We look at forecasts and we want them to be right. We want to trust them. And when forecasts – of tomorrow’s weather, next year’s inflation rate, the outcome of an election - turn out to be wrong, the public feels let down. Forecasters are ridiculed. I think this is unfair – certainly when looking at the economy. People may want something certain, but to be realistic, they’re not going to get it. It’s impossible.

So does that mean that we should give up? If we can’t have confidence in a prediction, then does that mean it’s useless? No.
There are two types of forecasts: purely statistical and more structural.

Statistical forecasts come from models which use data without imposing assumptions about the way that the economy works - about causal relationships. A statistical model works simply by identifying patterns in the historical data. Structural models, by contrast, impose assumptions – for example that individuals are rational. Structural models are often easier to interpret because they predict the behaviour of economic agents – consumers and firms – under different scenarios. Unfortunately, while structural models can 'make sense' of the data, they are not very good at predicting what will actually happen in the future.
No models - statistical or structural - do well when forecasting over anything but a relatively short horizon, but statistical models generally do outperform structural models within the horizon – less than one year – that is predictable.

When policy institutions and investment banks produce forecasts, they use models of the economy of both types. It is rare, however, that a structural model is used for forecasting in its pure form: judgement is used as well in order to put together a consistent story about the economy on the basis of (judgemental) assumptions about external variables.
For example, we may not be very good at forecasting the exchange rate, but we can make an assumption about what it will be; then we can make projections for other variables such as inflation, interest rates and so on.

The uncertainties

Already, there are two uncertainties: first about what the external variable – in this case the exchange rate – is going to be; and second about the relationship between different variables. We know that these relationships are very unstable. Models are very rough approximations.