by Yesterday John Kay had an interesting article about models and forecasting in Financial Times: A bane of this economist’s life is the belief that economics is clairvoyance. I should, according to this view, be offering prognostications of what gross domestic product growth will be this year and when the central bank will raise interest rates … What was the right answer on January 1 1989 to the question “will the Berlin Wall be pulled down in 1989?” A shrewd commentator would have said (though few did) something like “almost certainly the Wall will stand but you should understand the potentially destructive forces undermining the Soviet engine and the East German state”. That type of response combines probabilistic and narrative thinking. But people long for certainties, though they know they cannot have them. I have learnt that few really want answers when they ask me to predict GDP growth or advise whether interest rates will rise in the third quarter. It is usually easy to move the subject on to something more interesting than macroeconomic forecasting.
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Yesterday John Kay had an interesting article about models and forecasting in Financial Times:
A bane of this economist’s life is the belief that economics is clairvoyance. I should, according to this view, be offering prognostications of what gross domestic product growth will be this year and when the central bank will raise interest rates …
What was the right answer on January 1 1989 to the question “will the Berlin Wall be pulled down in 1989?” A shrewd commentator would have said (though few did) something like “almost certainly the Wall will stand but you should understand the potentially destructive forces undermining the Soviet engine and the East German state”. That type of response combines probabilistic and narrative thinking.
But people long for certainties, though they know they cannot have them. I have learnt that few really want answers when they ask me to predict GDP growth or advise whether interest rates will rise in the third quarter. It is usually easy to move the subject on to something more interesting than macroeconomic forecasting.
Kay’s remarks — and Tony Yates comments on them — made me think about an article that Oxford macroeconomist Simon Wren-Lewis wrote on models and forecasts a couple of years ago, saying that “macroeconomic forecasts are always bad,” but, on the other hand, since they are “probably no worse than intelligent guesses” and anyway are “not obviously harmful,” we have no reason to complain.
The thing is that Wren-Lewis is wrong. These forecasting models and the organizations and persons around them do cost society billions of pounds, euros and dollars every year. And if they do not produce anything better than “intelligent guesswork,” I’m afraid most taxpayers would say that they are certainly not harmless at all!
Mainstream neoclassical economists often maintain – usually referring to the methodological individualism of Milton Friedman – that it doesn’t matter if the assumptions of the models they use are realistic or not. What matters is if the predictions are right or not. But, if so, then the only conclusion we can make is – throw away the garbage! Because, oh dear, oh dear, how wrong they have been!
When Simon Potter a couple of years ago analyzed the predictions that the Federal Reserve Bank of New York did on the development of real GDP and unemployment for the years 2007-2010, it turned out that the predictions were wrong with respectively 5.9% and 4.4% – which is equivalent to 6 millions of unemployed. In other words — the “rigorous” and “precise” macroeconomic mathematical-statistical forecasting models were wrong. And the rest of us have to pay.
Potter is not the only one who lately has criticized the forecasting business. John Mingers comes to essentially the same conclusion when scrutinizing it from a somewhat more theoretical angle.
The empirical and theoretical evidence is clear. Predictions and forecasts are inherently difficult to make in a socio-economic domain where genuine uncertainty and unknown unknowns often rule the roost. The real processes that underly the time series that economists use to make their predictions and forecasts do not confirm with the assumptions made in the applied statistical and econometric models. Much less is a fortiori predictable than standardly — and uncritically — assumed. The forecasting models fail to a large extent because the kind of uncertainty that faces humans and societies actually makes the models strictly seen inapplicable. The future is inherently unknowable — and using statistics, econometrics, decision theory or game theory, does not in the least overcome this ontological fact. The economic future is not something that we normally can predict in advance. Better then to accept that as a rule “we simply do not know.”
So, to say that this counterproductive forecasting activity is harmless, simply isn’t true. Spending billions after billions of hard-earned money on an activity that is no better than “intelligent guesswork,” is doing harm to our economies.
In New York State, Section 899 of the Code of Criminal Procedure provides that persons “Pretending to Forecast the Future” shall be considered disorderly under subdivision 3, Section 901 of the Code and liable to a fine of $250 and/or six months in prison. Although the law does not apply to “ecclesiastical bodies acting in good faith and without fees,” I’m not sure where that leaves macroeconomic model-builders and other forecasters …
In an interesting discussion on the hopelessness of accurately modeling what will happen in the real world, Nobel laureate Kenneth Arrow – in Eminent Economists: Their Life Philosophies (CUP 1992) – pretty well sums up what the forecasting business is all about:
It is my view that most individuals underestimate the uncertainty of the world. This is almost as true of economists and other specialists as it is of the lay public. To me our knowledge of the way things work, in society or in nature, comes trailing clouds of vagueness … Experience during World War II as a weather forecaster added the news that the natural world as also unpredictable.
An incident illustrates both uncertainty and the unwillingness to entertain it. Some of my colleagues had the responsi-bility of preparing long-range weather forecasts, i.e., for the following month. The statisticians among us subjected these forecasts to verification and found they differed in no way from chance. The forecasters themselves were convinced and requested that the forecasts be discontinued. The reply read approximately like this: ‘The Commanding General is well aware that the forecasts are no good. However, he needs them for planning purposes.’