Another big difference between weather forecasting and economic forecasting is that with weather, it pretty much acts as it does before so within a given time scale it is possible to predict the path of a storm say based on what has happened before. Economic forecasting is less like this though there are occasions when things are more predictable. I would say that economic forecasting is a bit more like predicting earthquakes.
With regards Ludi's post: "useful", probably, "interesting" definitely :-)
In general, warning levels are a good idea, but inherent instability (house of cards like feedback mechanisms) is also important to appreciate. Simultaneously measuring inherent risk and exposure to these risks is important and perhaps economic forecasting and predictng a crash is just as difficult as say predicting a road accident. However with road accidents peoples (mis)perceptions perhaps come into it more. Yet there are a lot of things that try to stabilise the economic system which is perhaps much more like a precarious balancing act.
Best wishes,
Andy
http://www.geog.leeds.ac.uk/people/a.turner/index.html
-----Original Message-----
From: email list for Radical Statistics [mailto:[log in to unmask]] On Behalf Of BYRNE D.S.
Sent: 21 April 2016 10:08
To: [log in to unmask]
Subject: Re: RADSTATS Digest - 19 Apr 2016 to 20 Apr 2016 (#2016-58)
There is a fundamental difference between weather forecasting and the current form of economic forecasting. What matters for weather forecasting is predicting system change i.e. what will happen that is different. Weather systems are complex and the models are subject to chaos so very small difference in initial parameters can engender qualitatively different outcomes. It isn't the butterfly's wing flap that causes the hurricane - it is a difference of that magnitude in initial parameter specification that generates or does not generate a hurricane in the model's prediction. However, it is not the chaotic development that matters so much as the complex nature of the system being described as with climate regimes as opposed to short term weather. Conventional economic forecasting is based on linear models which deal in incremental change, not changes of kind. That is why they were so useless at predicting the crash. Econo physicists - a trade which now has its own journals - are using non-linear models generally based on difference rather than differential equations (although there are non-linear differential models) but even these have limits as to predictive validity. Qualitative knowledge of systems is the best guide so anyone with a qualitative knowledge of what was happening at the bottom end of the US housing system - liar loans arranged by brokers working on commission to refinance the houses of poor people without health insurance - see the brilliant Bird and Fortune video on this - could and did predict that derivatives based on these and massively multiplying the risk would go wrong. No mathematical modelling required.
David Byrne
________________________________________
From: email list for Radical Statistics [[log in to unmask]] on behalf of Ludi Simpson [[log in to unmask]]
Sent: 21 April 2016 09:55
To: [log in to unmask]
Subject: Re: RADSTATS Digest - 19 Apr 2016 to 20 Apr 2016 (#2016-58)
It would be useful to review the success of past forecasts - what's the distribution of errors around forecasts of GDP and GDP change, 1 year ahead and 10 years ahead? The same for unemployment and other key indicators used in public policy. Unlike weather, the economy is not forecast frequently enough for there to be a great deal of data to go on. But perhaps it's already been done, enough to know whether the uncertainty is sufficiently stable to use to judge new forecasts.
But a claim like 'Brexit with a bilateral agreement between UK and EU would reduce GDP by £4,300 per household' is a conditional forecast. The success of past forecasts would only show the uncertainty around things not determined by the Brexit condition. The assumptions made about what Brexit would involve are fixed by the forecaster and would take further unpicking: argument over their validity would add more uncertainty, perhaps a lot more.
There may have been a lot of work done on this already. If anyone knows it and could consider turning it into practical advice for evaluating claims, it would make a good RadStats article - or a chapter in the RadStats books.
From my experience in population forecasts, there are key needs where uncertainty matters a lot (school rolls; town planning; adult care), and there are quite a lot of data in the UK. But very few evaluations.
Ludi
------------------------------
Date: Wed, 20 Apr 2016 17:10:56 +0100
From: John Bibby <[log in to unmask]>
Subject: Fwd: Letter: "All forecasts are wrong. But some are more wrong than others" (Derek Jerram,"Forecasting is art, not science", Letters in "i", 2016april20, p.14.
Please see email below - all comments welcome! JOHN BIBBY
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---------- Forwarded message ----------
From: John Bibby <[log in to unmask]>
Date: 20 April 2016 at 17:09
All forecasts are wrong, but some are more wrong than others. Forecasting should not lead to a "precise" number or "point forecast" - like the recent Treasury £4300 in their research on leaving the EU. Instead, it should lead to a "50% confidence interval forecast", such as £4000-£5000 - the interval being calibrated so that the researcher is 50% confident that the forecast is correct. This has the advantage of falsifiability - if an accurate forecasting method is used, exactly 50% of forecasts will be true; the other 50% will be false. (Alternatively, forecasters may choose a wider interval if they require a higher level of confidence e.g. £3000-£6000 with 90% confidence.)
My forecast - most forecasters will not listen to this, because it gives us a way of distinguishing the fraudulent forecasters from the genuine ones.
JOHN BIBBY (01904-330334)
1 Straylands Grove
York YO31 1EB
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