Ryan Bourne, an economics professor at the esteemed Cato Institute, announced in The Times that he believes “Britain is paying a heavy price for faulty economic forecasts”
Indeed, much the same could be said about many forecasts on offer in many different walks of life – few understand how such forecasts are produced but, when computer models are involved, they gather a widespread credibility which is acted upon – only later, and often too late, many are found to have been plain wrong
So why employ forecasts, especially economic forecasts?
- When formulating any business plan, forecasts are needed to help try and ensure that overall aims are not just stretching but realistic
- Forecasts are made of what the value of important business variables may be in the future – say market size, oil price, sales revenue, skilled labour and capacity needs, or raw material costs 5 to 10 years out
- Such forecasts are also used to hedge bets and forward buy or recruit more apprentices now for skills needed later
- But forecasts can be fraught with errors – the further the horizon, the bigger the error margins, especially when there’s several explanatory variables involved
- That said, some events can be predicted confidently – their causes are clearly understood or empirical evidence allows little argument – for example:
- Night follows day
- High tide will be 6 ½ hours after low tide
- Booms follow busts
- Markets usually revert to the mean
- But no such confidence can be attached to economic forecasts – too many explanatory variables are involved and there’s no empirical evidence for expected results
- Despite this, ‘experts’ continue to announce/ publish their long term forecasts for GDP, stock market indices, oil prices – and the weather – even when their short term forecasts have been found to be wildly out
- One can now be told there’s a 57% chance of light rain at 1000 for one specific hour, not this coming saturday but the next – and the same super-model will even insist there’s 0% chance of rain where I live even when sheets of it are lashing the window at that very moment
- Why the need for such certainty?
- Why persist with such unnecessary apparent accuracy?
- Most customers have been more than ‘twice-bitten’ by them and so act accordingly
- However, undaunted, the ‘experts’ keep building bigger and better (and more expensive) forecasting models, assuring us that they will be ‘even more accurate’
- Perhaps they should pay more attention to what some heavyweights with inside experience have had to say on this matter viz:
- Kenneth Rogoff, ex chief economist of the IMF – when employed to forecast currency movements and asked what that involved, he said: “Understanding better than most that I didn’t know what was going to happen“
- Laurence J Peter, the Canadian educator and famous for his Peter Principle, said: “Experts know tomorrow why things they predicted yesterday didn’t happen today“
- Denis Healey, ex UK Chancellor of the Exchequer and acerbic wit, said: “Economic forecasting involves extrapolation from a partially known past, through an unknown present, to an unknowable future“
- The fact is that most forecasts are based on what happened in the past
- But even when there’s clear mathematical correlations found in the data, a deduced forecast can be wrong:
- What’s your forecast for the next number in the series 1, 2, 4, ?
- Either 7 or 8 would be correct
- And the famous author and investor, Nicholas Taleb (‘The Black Swan‘) asked: “What would a turkey expect as Xmas neared based on its past – water and food had been provided every day, even more than usual recently – but one day, alas, the past turns out to have been very misleading“
- So back to Professor Bourne
- He says: “The scale of forecast errors points to fundamental flaws in our economic modelling” – in particular:
- The OBR – Office for Budget Responsibility – “missed excess demand pressure in the economy“
- The IMF and OECD required the government make tax cuts having predicted a 2023 UK recession, which did not happen – and “under-estimated UK growth in every year since 2016“
- Bourne concludes: “Predicting the path of a £2.5 trillion economy underpinned by billions of individual decisions and trades is inherently difficult – a more robust approach to making policy would lean on economic intuition than elaborate computer models, tangible data over speculative projections and budgeting rules less sensitive to such fickle guestimates“
- Hopefully, future UK Chancellors and corporate planners will agree with him
- By all means consider any and all forecasts on offer but, when decisions have to be taken and uncertainty rules, best to rely mostly on the combined thoughts and experience of the few people responsible and closely involved
- Big mistakes will still arise but, we humbly forecast, less than otherwise