One of the things I really like about writing blogs is that it puts my views to the test. After I have written them of course, through comments and other bloggers. But also as I write them.
Take my earlier post on forecasting. When I began writing it I thought the conventional wisdom was that model based forecasts plus judgement did slightly better than intelligent guesswork. That view was based in part on a 1989 survey by Ken Wallis, which was about the time I stopped helping to produce forecasts. If that was true, then the justification for using model based forecasting in policy making institutions was simple: even quite small improvements in accuracy had benefits which easily exceeded the extra costs of using a model to forecast.
However, when ‘putting pen to paper’ I obviously needed to check that this was still the received wisdom. Reading a number of more recent papers suggested to me that it was not. I’m not quite sure if that is because the empirical evidence has changed, or just because studies have had a different focus, but it made me think about whether this was really the reason that policy makers tended to use model based forecasts anyway. And I decided it was probably not.
In a subsequent post I explained why policymakers will always tend to use macroeconomic models, because they need to do policy analysis, and models are much better at this than unconditional forecasting. Policy analysis is just one example of conditional forecasting: if X changes, how will Y change. To see why this helps to explain why they also tend to use these models to do unconditional forecasting (what will Y be), let’s imagine that they did not. Suppose instead they just used intelligent guesswork.
Take output for example. Output tends to go up each year, but this trend like behaviour is spasmodic: sometimes growth is above trend, sometimes below. However output tends to gradually revert to this trend growth line, which is why we get booms and recessions: if the level of output is above the trend line this year, it is more likely to be above than below next year. Using this information can give you a pretty good forecast for output. Suppose someone at the central bank shows that this forecast is as good as those produced by the bank’s model, and so the bank reassigns its forecasters and uses this intelligent guess instead.
This intelligent guesswork gives the bank a very limited story about why its forecast is what it is. Suppose now oil prices rise. Someone asks the central bank what impact will higher oil prices have on their forecast? The central bank says none. The questioner is puzzled. Surely, they respond, higher oil prices increase firms’ costs leading to lower output. Indeed, replies the central bank. In fact we have a model that tells us how big that effect might be. But we do not use that model to forecast, so our forecast has not changed. The questioner persists. So what oil price were you assuming when you made your forecast, they ask? We made no assumption about oil prices, comes the reply. We just looked at past output.
You can see the problem. By using an intelligent guess to forecast, the bank appears to be ignoring information, and it seems to be telling inconsistent stories. Central banks that are accountable do not want to get put in this position. From their point of view, it would be much easier if they used their main policy analysis model, plus judgement, to also make unconditional forecasts. They can always let the intelligent guesswork inform their judgement. If these forecasts are not worse than intelligent guesswork, then the cost to them of using the model to produce forecasts - a few extra economists - are trivial.