Editor’s note: Today, we’re sharing an essay from our friends at Agora Financial. In it, Jim Rickards shares how different economic models lead to different conclusions about the economy…
There are plenty of models and lots of ways of looking at the same data to draw conclusions about the economy and make forecasts. Sometimes the model differences are more superficial than not. Other times the model differences are profound.
But for a few who actually explain their models (including a typical Fed mathfest around their dynamic stochastic general equilibrium, or DSGE, models), most differences don’t matter because the analysts are too busy shouting their conclusions and not patient enough to offer a deeper explanation to genuinely interested parties.
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In any case, followers latch onto their favourites and re-shout the conclusions and life goes on with no resolution of which models work and which don’t.
The Fed DSGE models (also used by the IMF and others) are straightforward. They begin with the assumption that dynamic economic systems are equilibrium-based. There’s no actual evidence for this, but it’s a convenient economic assumption.
The model also assumes that events in a sequence are stochastic. This means “random,” but not in a path-dependent way. Random for this purpose is more like a coin toss, card draw or roll of the dice where an individual outcome is not predictable, but a long-term series of outcomes is highly predictable i.e., where 1,000 coin tosses will come close to 500 heads and 500 tails every time.
Once one assumes DSGE models are correct (I don’t), it follows that the overall economy is generally in balance and the long-term path of key variables is predictable, despite short-term variance.
Then there is relatively little for central banks to do except avoid changes in expectations, mostly about inflation or deflation.
The result is that expectations and real variables (interest rates, unemployment, growth, etc.) are optimised and in balance. The only time the Fed needs to intervene is when expectations or real variables fall out of balance and some nudge, usually via interest rates, is needed from the Fed to restore those factors to balance.
The Fed found itself in a highly unbalanced economy in late 2007 and most of 2008 regarding deflationary expectations (much higher) and the path-dependence of bank failures.
The Fed’s solution, first under Ben Bernanke and then Janet Yellen, was to first cut rates to zero (ZIRP). Then, blow up the Fed balance sheet through so-called QE, QE2 and QE3, bail out failing banks in the U.S. and Europe and reassure the general public that Bernanke, Yellen and the ECB’s Mario Draghi would do “whatever it takes” to keep the financial system going.
At that point, the Fed and ECB assumed the simple passage of time and continual reassurance would heal the wounds and return the economy to normalcy.
This never happened. It’s true that a worse recession in 2008 was avoided, but there is no evidence at all that the expansion from 2009–2018 was more than a slow, modest claw-back of growth.
This nine-year expansion averaged real annual growth of 2.19 percent, significantly lower than the 3.22 percent average real annual growth for all expansions since 1980.
The most recent quarter of 4.1 percent annual real growth was the fifth such quarter since 2009. All four prior 4 percent-plus quarterly expansions quickly compressed to growth below 1.0 percent or actual losses within six months.
The reason actual Fed expectations have not been served is that their DSGE models bear little to no relationship to the real economy. The real economy is not an equilibrium system. It is a system of booms and busts, surges and sharp contractions, and misplaced reliance on government.
Assumptions about consumer and investor expectations are routinely disappointing, which leads to rapid reversals that are also disappointing. Each of these psychological reversals (disappointment, reversal, disappointment, reversal, etc.) is an example of an “emergent property,” a hallmark of complex dynamic systems.
A complex dynamic system, part of a larger branch of science called complexity theory, is much more in sync with how the real economy operates.
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A complex dynamic system is characterised by diversity (millions of perspectives), connectedness (diverse views have message traffic links), interaction (system participants conduct billions of transactions) and adaptive behavior (system losers change or close losing positions).
A city dweller who looks outside on a winter morning to see if locals are wearing sweaters (not too cold) or down parkas (below freezing) and adapts her dress accordingly is a willing participant in a complex dynamic system of which she may be only slightly aware.
Most experts in complexity theory and complex dynamic systems are only familiar with capital markets in superficial ways. They are more likely to be experts in physics, biology, seismology, meteorology and other branches of science where complexity is deeply rooted.
Complexity was discovered as a hard science in 1948 by Warren Weaver of the Rockefeller Foundation and was put on a more firm statistical footing by climatologist Edward N. Lorenz in 1963.
Since the early 1960s and the advent of remarkably swift and inexpensive computing power, complexity theory has flourished in hard sciences, soft sciences and capital markets.
Complexity theory is an almost perfect description of actual behavior in capital markets (go here to learn how to make complexity theory and the world’s most powerful predictive tools work for you in the markets).
The capital markets are four-for-four on the main criteria — diversity, connectedness, interaction and adaptive behavior.
Billions of investors wake up every day and adopt a posture of bull or bear, leveraged or unleveraged, aggressive or timid and so on. They connect through a myriad of networks over every channel, from Bloomberg to text.
They interact to the extent of trillions of dollars in stocks, bonds, currencies, commodities and more. Finally, they adapt their behaviour at warp speed or else expect to be removed from the trading floor, possibly feet first.
These conformance characteristics alone prove nothing, but they lay a strong basis for further analysis. The critical connection between complexity and capital markets is not just superficial behavior, although that’s an important starting point, but is based on the fact that humans themselves are models of complex behavior independent of the role of some in capital markets.
This makes human behavior in capital markets an exponentially complex adaptive arena. Without leaping to conclusions, there is an eminently sound foundation for the conclusion that capital markets are the most complex human arena for behaviour alongside love and war.
Once your analysis has come this far, many hallmark behaviors from capital markets become easier to understand. Bull and bear markets are seen for what they are — psychological enthusiasm and psychological depression taking turns as the thème du jour.
Panics are also seen in the proper context — radical changes in investor attitudes toward real cash (“Where’s mine?”) versus pseudocash, which are stocks, bonds, real estate and other investments that are taken to be “cash,” up until the day they’re not.
These and many other instant behavioural changes are considered to be “emergent properties” of complex systems — a favoured term for events no one sees coming until they land on your doorstep or brokerage account statement.
Name-tagging does not make forecasting easier but it does put forecasting in a proper context. The humble analyst, hopefully including your correspondent, would rather be approximately right than completely wrong.
Other paradigms abound, but DSGE (the Fed) and complexity theory (me and a few others) have staked out the high ground for which theory best explains behaviour in capital markets. Many analysts favor DSGE because it’s mainstream, and taught from the first week of Intro to Economics.
The accuracy of predictive analytics favors complexity theory (again, go here to learn how you can harness the power of complexity theory and the most powerful analytic tools in the world to make money in today’s market).
In time, powerful predictive analytics falling from the application of complexity to capital markets will trump the sheer number of DSGE adherents. Yet there’s no reason to expect this to happen quickly.
Copernicus, with Kepler and Brahe hot on his heels, laid out the mathematical and empirical foundations of a heliocentric solar system by the mid-16th century.
Yet the theological and scientific debate inside the Catholic Church raged on during the 17th century and was not finally put to rest in Copernicus’ favor until the early 19th century. Even the best ideas have high mountains to climb when they’re new.
Meanwhile, my readers will receive the fruits of complexity theory applied in predictive analytic form to capital markets. I’m happy if everyone receives them but not displeased if the audience is smaller… at least for the short run.
P.S. After 15 years of secrecy, I’ve decided to “declassify” information from an abandoned CIA project.
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But due to its controversial nature, it must come offline by midnight October 7 (noon in Hong Kong and Singapore).