quite a lot of the analogy between the dynamics of an economy or financial
market and the weather. It's one of the basic themes
of this blog, and the focus of my
forthcoming book FORECAST. I don't pretend to be the first one to think of
this at all. I know that the head of the Bank of England Mervyn King has talked
about this analogy in the past, as have many others.
But the idea now
seems to be gathering more popularity. Steve Keen even writes
here specifically about the task of economic forecasting, and the entirely
different approaches used on weather science, where forecasting is now quite
successful, and in economics, where it is not:
modelling tools can extrapolate forward existing trends fairly well – if those
trends continue. But they are as hopeless at forecasting a changing economic
world as weather forecasts would be, if weather forecasters assumed that,
because yesterday’s temperature was 29 degrees Celsius and today’s was 30,
tomorrow’s will be 31 – and in a year it will be 395 degrees.
Of course, weather forecasters don’t do
that. When the Bureau of Meteorology forecasts that the maximum temperature in Sydney on January
16 to January 19 will be respectively 29, 30, 35 and 25 degrees, it
is reporting the results of a family of computer models that generate a forecast
of future weather patterns that is, by and large, accurate over the time horizon
the models attempt to predict – which is about a week.
Weather forecasts have
also improved dramatically over the last 40 years – so much so that even an
enormous event like Hurricane Sandy was predicted
accurately almost a week in advance,
which gave people plenty of time to prepare for the devastation when it
Almost five days prior
to landfall, the National Hurricane Center pegged the prediction for Hurricane
Sandy, correctly placing southern New Jersey near the centre of its track
forecast. This long lead time was
critical for preparation efforts from the Mid-Atlantic to the Northeast and no
doubt saved lives.
Hurricane forecasting has come a long way in the last
few decades. In 1970, the average error in track forecasts three days into the
future was 518 miles. That error shrunk to 345 miles in 1990. From 2007-2011, it
dropped to 138 miles. Yet for Sandy, it was a remarkably low 71 miles, according
to preliminary numbers from the National Hurricane Center.
Within 48 hours, the forecast came into even sharper
focus, with a forecast error of just 48 miles, compared to an average error of
96 miles over the last five years.
Meteorological model predictions are regularly
attenuated by experienced meteorologists, who nudge numbers that experience
tells them are probably wrong. But they start with a model of the weather than
is fundamentally accurate, because it is founded on the proposition that the
weather is unstable.
Conventional economic models, on the other hand,
assume that the economy is stable, and will return to an 'equilibrium growth
path' after it has been dislodged from it by some 'exogenous shock'. So most
so-called predictions are instead just assumptions that the economy will
converge back to its long-term growth average very rapidly (if your economist is
a Freshwater type) or somewhat slowly (if he’s a Saltwater croc).
Weather forecasters used to be as bad
as this, because they too used statistical models that assumed the weather was
in or near equilibrium, and their forecasts were basically linearly
extrapolations of current trends.
How did weather
forecasters get better? By recognizing, of course, the inherent role of positive
feed backs and instabilities in the atmosphere, and by developing methods to
explore and follow the growth of such instabilities mathematically. That meant
modelling in detail the actual fine scale workings of the atmosphere and using
computers to follow the interactions of those details. The same will almost
certainly be true in economics. Forecasting will require both lots of data and
also much more detailed models of the interactions among people, firms and
financial institutions of all kinds, taking the real structure of networks into
account, using real data to build models of behaviour and so on. All this means
giving up tidy analytical solutions, of course, and even computer models that
insist the economy must exist in a nice tidy equilibrium. Science begins by
taking reality seriously