Overly Scientific Analysis Misses the Point
Economists are trained to act scientifically, and the emphasis over the last 50 years has been to rely on regression analysis to establish proof for theories. In the 1970s and 1980s, it was difficult to find courses in economic history or business cycles, even at Ivy League institutions.
So while the qualitative side of the discipline has been wanting, the quantitative rigor is heavily reliant upon a technique that emphasizes linearity, the normal distribution, and parsing of data covering truncated stretches of time divided into equal units.
This has its benefits: One can be quite definitive about establishing a relationship between two or more variables. But it may not hold up over time. No serious institutional portfolio manager or trader would base a decision rule upon the limited data contained in most regressions in the anti-gold models, because they are intimately familiar with the tendency for relationships to change over time.
Ned Davis Research, an authoritative source of cataloging financial market relationships, monitors hundreds of correlations and readily acknowledges the best of these have a tendency to deteriorate, either suddenly or over time.
This firm’s researchers, who are tasked to make money rather than to win Nobel Prizes, would be unlikely to bet heavily on a correlation that cannot explain nearly half of a model’s outcome. Foolishly, those running the Fed would not hesitate to do so even though the wealth of America and the world is put at great risk.
The assumptions of linearity and normally distributed outcomes are perhaps the most worrisome and limiting of thought, particularly because in the discipline of finance it has been almost unquestionably shown that six sigma events are happening with all too much regularity to be assumed random. (Two sigmas denote that in 95 percent of outcomes results will be within a stated boundary. A six sigma event is extremely rare; normally it would occur just 3.4 times in a million instances).
The principals of Long Term Capital Management, who had PhDs in economics, assumed away the possibility of rare outcomes with ruinous results for their investors. Nassim Nicholas Taleb has gained notoriety for reminding the investment community of this uncomfortable point. Linearity is another problem.
As Einstein’s work in the area of physics revealed, the human preference to regard the world in terms of three dimensions restricts comprehension. The existence of another dimension explained how the universe and time actually folded back over on itself. Would it not be unreasonable to expect that the economy, the shape of which is determined through the interaction of humans with biomechanical brains, might not react to stimuli in equally divided increments of time always with proportionately the same force or effect?
If economists are unable to find that sequential patterns of small, equal bites of time are predictive or explanatory of credit meltdowns after there has been a large credit buildup, yet they are able to conclude that fixed exchange rates (particularly when based on gold) were significant factors in taking the Depression to painful depths, then maybe they should question the carte blanche usage of regression analysis.
[Editor’s note: This passage is reprinted from William W. Baker’s book, Endless Money: The Moral Hazards of Socialism, with the permission of John Wiley & Sons, Inc (©2010). You can get your own copy here.]