Today is risk gone away day?

All the politicians have had their say about Nuklorea, the shootings died down. So, yields are up as investors leave safer bonds and go to equities. Still not sure I have this right.

Why is the bond market safer? More aggregated, hence more resilient. In hydraulic mode, bonds have lower bandpass, in QM mode, bonds have greater economies of scale, higher transaction sizes at lower transaction rates. In measure theory, bonds measure longer term trends which should be more stable.

Each of those segments on the dynamic yield curve is a Hick's partial equilibrium. The sequence of Hicks's equilibriums along the curve should be the best, constant precision match to the hydraulic model.

OK, let's talk economic norms

The great utility of money is that it can be treated like a compressible fluid in an approximation.  That is a great simplification in identifying value over time, follow the money.  That is also a great simplification for economists, it allows algebra to approximate the economy,  it enables double entry accounting.

The economy makes the same approximation when it achieves economies of scale.  The economy says to itself, "as long as there are no major perturbations, then I can reduce the variability of products and still use approximate double entry book-keeping."    So, as long as demand is stable around the 16 Oz soda bottle, the soda industry gets economies of scale.  But when the new equilibrium becomes the 12 OZ bottle, then the soda industry has to make good on a lot of deferred reversible flow, because the soda industry needs to make the transition smooth.  The producer wants the consumer to smoothly adjust his inventory of household soda.

The underlying real norm, as I note many times, is a positive definite, directed flow, a queuing model.  This is not generally a reversible flow, the economy deals with expanding entropy; inventories cannot generally be negative nor are products generally returned.  So, the algebra only holds to the approximation that the economy is willing to suffer.  But there is still structure in the directed flow, a different math, a math the economist can use after the fact.

What happens when we get unexpected shortages of essential inputs?  Faced with the idea of negative inventory looming, we shorten the production line, thus making sure that inventories return to positive territory, making sure the approximation holds within error. Economist need to research that tipping point, the point at which a high probability of negative inventory causes a Recalc.  That point should be called the Jensen bond energy.  There is a specific volatility in inventory that peaks our attention, it exceeds a set bound and we know the approximation is not holding.

Notes on the yield curve

What I look for is underneath the yield curve, the unquantized version under infinite dimensionality; hydraulic macro. I look at it as a normal frequency spectrum. What do I see?

The spectrum really peaks toward zero as it moves toward the long term, shaped like a production spectrum. At ideal equilibrium, the curve is Bell shaped, peaking around ten years.

Quantizing that curve put most of the long term into one 'bit'. The amount of the long term curve that trails over, across the Y axis, through zero, that portion is very small in good times, the curve is either peaked out near 7 years or we have rapid gains from sudden discovery and we are peaking close to 5. When the curve is flatter, more of it spills into negative territory.

This is standard, square integrable, 'signal processing' view.

My mathematical model of the economy

I mention it often, it is based on the idea of precision.
The more bits of precision the production system has, the more accurate it can deposit final lot_size * transaction_rate at the short end, the consumer end. The economy can count smaller variations in demand for a more diverse array of products. Bits of precision indicates the rank of the distribution network, the length of its production chain.

This is the essence of the entropy model. Really just a finite queuing model.

My model has a hydraulic macro equivalent, the production spectrum. The model has a standard form for inventory growth along the production network, the generalized yield curve. In hydraulic macro this can be treated like a frequency spectrum, measuring the 'impulse' response or the sectorial or aggregate production response.

The difference between the regressed hydraulic model and the precision model is quantization. We would expect mass changes at higher level of production, both positive and negative; hence this model would view the paradox of thrift as mostly changes happening with economies of scale. Economies of scale imply quantization, intermediate steps in production are moved inside the firm, hence fewer market interfaces between stages of production, hence less accurate and a higher quant at the finest level of final demand.

Errors in the system occur where the level of inventory variation with respect to inventory averages exceeds a constant double bound, the constant SNR value.

My model is based upon a biological assumption, the economic brain operates with the same basic measuring capability across all environments, the uncertainty constant. The existence of this constant makes us comfortable in production chains, it makes specialization work, it makes us habitual. When things are too certain, we began a search for yield; when things too uncertain we began to contract in consumption.
The units of the uncertainty constant are: The number of items we can track before we are comfortable that one will be tracked badly. We would see this constant in measures of the optimum team size. We should have a neurological measure of entropy maximization by reduced pattern neuronal firing, that is a direct measure of entropy encoding by inhibitor neurons.

QM Theory and economic change

Source: Chart Book: The Legacy of the Great Recession - CBPP

I am starting with this chart reposted in Mark Thoma's blog.  The first question is why is growth constant at trend?

 

We always measure the same growth trend, regardless of underlying technology. This graph is a comparison of a very lightweight good (money and debt) to the heavy weight goods. It will always look the same in terms of trend, unchanging.We can do nothing else, our precision is a fixed biological constant. So we will always organize our distribution networks such that each node in the distribution has the same fixed variation in inventory levels.The economy operates with a fixed Signal to Noise ratio.
 

Then the question becomes, How does this chart relate to a common theme of mine, the relationship between information technology and transportation technology.

When information technology explodes, the Y axis of the chart causes the sudden downturn as the economy is disrupted by the sudden appearance of $20 bills on the sidewalk.When transportation technology explodes, the X axis is disrupted by the sudden ability to move heavy goods to better locations.

The first effect causes actual GDP to drop below potential, and the second causes actual GDP to rise above potential.

Since 1820, all depressions follow the same path:  Information technology explodes on the scene then transportation technology adapts.

But the X axis is time!
Yes, the transactions rates slow down during the downturn.  We are seeing a strain on the transportation grid.  John Taylor at Stanford gets this part.

More yield curve

My proxy for an aggregate bankers yield curve

Consider log(Yi) dYi. which grows faster on the steep slope. It is this in each of, less than four or five terms, inversed from traditional form. The aggregate attempts to equalize, that value among the finite terms. When the value is measured to a constant accuracy, the channel is treated in real time. It appears in reality as a minimum entropy physical network with primary function of delivering paychecks, and its maximum bandwidth (paychecks per quarter) is limited.

But also the curve has to be within one SNR of a minimum variance spectrum to avoid cycles. Cycle? For example, periodically overbuilding a retirement community, which could happen when the AARP and their Social Security checks meet-up.

So dual constraints. The first wants the economy to economize on the number of stages in the network, its rank. The second wants to minimize inventory variance at each node.

How would I interpret this as government policy?
Congress makes very large, and rare generational promises, but operates thinly in the short term.

Booms slow and crashes fast

We crash much more efficiently than we boom. Kling suspects the cause may be related to money as he explains in the Peso problem.

But money adapts fast, that is the whole utility of money, and credit. It is a lightweight good on purpose, so it can adapt to expected changes in real goods production. Hence, the question needs more depth.

But not much more. Crashes are sudden because the economy moves to a known equilibrium, a version of production known from recent history. So the firms and the households know how to deflate. Booms are caused by utilizing new technology, but the technology is new and has no historical reference.

Klings fundamental insight seems correct, asymmetry is fundamental to any economic theory.

When did the crash happen?

It really started in 2004, as Jim Hamilton points out. His post identifies what I call financial illusion, the illusion among economists that crashes do not occur until they are noticed by the financial community.
HT Mark Thoma.

The latest from Okun and the Fed

Mary Daly and Bart Hobijn report from the Fed:

The final panel of Figure 2 points to the factor that turns out to be the main driver of the recent departure from Okun’s law—average labor productivity, measured as GDP per nonfarm hour worked. The deviation in average labor productivity relative to the GDP gap is far outside the range plotted over time and is consistent with the rapid productivity growth recorded in 2009. The surge in labor productivity allowed employers to keep output steady while shedding workers and reducing hours of work in the economy. As such, it allowed unemployment to rise much more than expected given the change in GDP, breaking the normal pattern between the two measures observed over the past 60 years.

Perhaps we suffer a positive technology shock which causes a Kling Recalculation as firms retain that part of the operation that utilizes the new technology? We would expect firms to react as 11% of their sales go on-line each year.