The economics profession needs new models
The list of policy and predictive failures by mainstream economists is longer than the typical nine-year-old’s Christmas wish list.
They failed to foresee the 2007 mortgage bubble and the subsequent 2008 financial crisis.
They cheered ‘green shoots’ in 2009 when that was a complete illusion. They welcomed the ‘recovery summer’ in 2010, which was nothing but more of the same punk growth.
They spent six years pursuing QE and seven years with zero interest rates, and had nothing to show for it except an inflated balance sheet, inflated asset values and $4 trillion of lost wealth through below-trend growth.
The latest failure was proclaiming ‘synchronised global growth’ in 2017 when there was nothing of the sort.
Major economies are now slipping into recession…
Mainstream economist says mainstream economists have it wrong
Prominent economist Mohamed A. El-Erian says it’s finally time for economists to learn from failure and improve their analysis or face marginalisation in the policy debate.
He says economists should avoid the ‘herding’ instinct and be more critical of Fed communications strategies.
He also says trade analysts should be less accepting of simple ‘free trade’ dogma, and work harder to see the perspectives of the victims of free trade who have lost good-paying jobs and benefits.
Finally, he says that economists should adopt an interdisciplinary approach that integrates behavioural psychology, complexity and branches of applied mathematics that are outside the traditional (and flawed) equilibrium models.
These are recommendations I have been pointing to for years.
El-Erian’s suggestions are solid and long overdue.
Just don’t hold your breath waiting for economists to follow his advice.
They seem determined to persist in flawed methodologies until they are rendered completely irrelevant in the public debate on economics.
The professionals get it wrong half the time
Don’t get me wrong. No one expects perfection or anything close to it.
A forecaster who turns out to be accurate 70% of the time is way ahead of the crowd.
In fact, if you can be accurate just 55% of the time, you’re in a position to make money, since you’ll be right more than you’re wrong.
If you size your bets properly and cut losses, a 55% batting average will produce above-average returns.
Even monkeys can join in the game. If you’re forecasting random binary outcomes (stocks up or down, rates high or low, etc.), a trained monkey will have a 50% batting average.
The reason is that the monkey knows nothing and just points to a random result.
Random pointing with random outcomes over a sustained period will be ‘right’ half the time and ‘wrong’ half the time, for a 50% forecasting record. You won’t make any money with that, but you won’t lose any either. It’s a push.
So, if 70% accuracy is uncanny, 55% accuracy is okay and 50% accuracy is achieved by trained monkeys, how do actual professional forecasters do? The answer is less than 50%.
In short, professional forecasters are worse than trained monkeys at predicting markets.
The bottom line is that if you have defective and obsolete models, you will produce incorrect analysis and bad policy every time.
There’s no better example of this than the Federal Reserve.
Not all things are equal
The Fed uses equilibrium models to understand an economy that is not an equilibrium system; it’s a complex dynamic system.
The Fed uses the Phillips curve to understand the relationship between unemployment and inflation when 50 years of data say there is no fixed relationship.
The Fed uses what’s called value-at-risk modelling based on normally distributed events when the evidence is clear that the degree distribution of risk events is a power curve, not a normal or bell curve.
As a result of these defective models, the Fed printed US$3.5 trillion of new money beginning in 2008 to ‘stimulate’ the economy, only to produce the weakest recovery in history.
Need proof? Every year, the Federal Reserve forecasts economic growth on a one-year forward basis.
And it’s been wrong every year for the better part of a decade. When I say ‘wrong’, I mean by orders of magnitude.
If the Fed forecast 3.5% growth and actual growth was 3.3%, I would consider that to be awesome.
But the Fed would forecast 3.5% growth and it would come in at 2.2%. That’s not even close, considering that growth is confined to plus or minus 4% in the vast majority of years.
The 2009-2019 recovery has already been the weakest recovery in US history, despite a few good quarters here and there. And there’s little reason to expect it to pick up from here. In fact, growth is slowing.
Right now, my own models are saying that Powell’s verbal ease is too little too late.
Damage to US growth prospects has already been done by the Fed’s tightening since December 2015 and the Fed’s QT policy, which started in October 2017.
Let’s not be too hard on the Fed.
The IMF forecasts were just as bad. And the ‘the wisdom of crowds’ can also be dramatically wrong. It does not have very high predictive value.
It’s just as faulty as the professional forecasts from the Fed and IMF.
How to outthink the crowd
The wisdom of crowds is a highly misunderstood concept. It works well when the problem is simple and the answer is static, but unknown.
The classic case is guessing how many jellybeans are in a large jar.
In that situation, the average of 1,000 guesses actually will be better than a single ‘expert’ opinion.
That works because the number of jellybeans never changes. There’s nothing dynamic about the problem.
But, when the answer is truly unknown and the problem is complex and dynamic, such as capital markets forecasting, then the wisdom of crowds is subject to all of the same biases, herding, risk aversion and other human quirks known through behavioural psychology.
This is important because when academics say, ‘You can’t beat the market’, my answer is the market indicators are usually wrong.
When talking heads say, ‘You can’t beat the wisdom of crowds’, I just smile and explain what the wisdom of crowds actually does and does not mean.
By the way, this is one reason why markets missed Brexit and Trump.
The professional forecasters simply misinterpreted what polls and betting odds were actually saying.
None of this means that polls, betting odds and futures contracts have no value. They do.
But the value lies in understanding what they’re actually indicating, and not resting on a naive and superficial understanding of the wisdom of crowds.
Does this mean that forecasting is impossible or that the experts are uninformed? Not at all.
Highly accurate forecasting is possible.
The problem with the ‘experts’ is not that they’re dopes (they’re not), or that they’re not trying hard (they are).
The problem is that they use the wrong models.
The smartest person in the world working as hard as possible will always be wrong if they use the wrong model.
That’s why the IMF, Fed and the wisdom of crowds bat below .500. They’re using the wrong models.
Hopefully that changes. The change is long overdue.
All the best,