the consequences of the forecast and associated policy being wrong. for both producers and users of forecasts, it is also worth looking back at forecast errors - not to berate the forecasters, but rather to see what we can learn from those errors about the way the economy works. evidence on accuracy it has long been understood that economic forecasts are not all that good. most elements of the round - up that i gave five years ago3 still seem apposite. first, forecasts are better than a coin toss - that is, an economic forecast can more often than not be expected to outperform a random process or some very simple extrapolative rule - though often not by all that much. this is not true, however, for some financial variables, where the economics profession ’ s forecasting embarrassment is greatest. there is some evidence that, in australia, forecasts improved in the past decade. the table shows that the average absolute error of one - year - ahead forecasts for both gdp growth and cpi inflation in the age survey from 1994 to 2003 declined to just over half what it had been in the preceding 10 years. of course, that period has been one of much reduced volatility in the economy, a fact that has been jonathan kearns provided invaluable assistance in preparation for this speech. because of data revisions, the unemployment rate for may 1999 is today recorded at 7. 0 per cent. but this was originally published as 7. 5 per cent. stevens ( 1999 ). noted before. 4 so maybe it was just easier to make forecasts in that period, and the real test will come when the economy enters rougher waters. a crude way of assessing this would be to see whether a comparison of the age forecasts with those from a naive forecast rule - that the future value is the same as the current one - revealed an improvement. the second column gives the forecast errors for such a naive rule. the theil statistic in the final column shows the ratio of the two errors. ‘ good ’ forecasts have a value less than unity - that is, the forecasters add value in the sense of lowering forecast errors compared with the naive rule. while the age panel ’ s performance improves a lot in the past decade, so does that of the naive forecast rule. the theil statistic suggests that the forecasters were adding some value in both periods, but with no major changes between the two. so we shouldn ’ t get too

going on for some time. the nber recession dating committee, for example, did not declare the march 2001 peak in the us economy until november that year. norman ( 2001 ). might be wrong has some value. we just need to keep in mind that numerical forecasts are not much more than opinion formed ( hopefully ) within a coherent and disciplined framework. they are not guarantees of performance, and should always be accompanied by a discussion of risks. that discussion is likely to be at least as useful as the point estimates themselves. use of models versus judgement let me turn now to some questions to do with the formation of forecasts. one of the perennial ones is the respective roles of formal models and subjective judgement. it seems to me obvious that we need both. any judgemental forecast embodies some notion of how the economy works, unless the numbers really are drawn from a dart board. most forecasters make some effort to ensure their forecasts for different variables are consistent with each other, and tell some sort of story that can be related to presumed behaviour. that is to say, they have a model of sorts, even if a fairly informal one. econometric models are a more formal way of representing the relationships in the historical data. it is usually helpful occasionally to confront the notions in our heads with the data to see if there is any validation for our prejudices. that said, formal models come, or should come, with various usage warnings. to begin with, there seems to be some evidence that simple models often are more useful than more complex ones, perhaps because they are more robust and so less likely to come unstuck due to structural change, etc. because their workings are more transparent, users may also be able to spot problems more easily when they start to break down. simplicity, of course, has to be traded off against the general principle that the economy has many complex interactions, which simple models can miss. but, in general, complex is not always better, especially for short - term forecasting. 7 second, some modelling techniques which are thought to be best practice for describing history may not be optimal for forecasting purposes. a case in point is the use of cointegration models, where the deviation from an estimated long - run equilibrium level can be a powerful factor affecting short - run forecasts of changes, as the model wants to move the dependent variable towards the supposed long - run equilibrium. but if there has been a level shift in the equilibrium relationship