Where are all the Alien Super-Civilizations?

Scientific American just published a very significant article titled Alien Super-civilizations Absent from 100,000 Nearby Galaxies. The article reports that "the most far-seeing search ever performed" looking the artifacts that would be produced by advanced Super-civilizations has come up empty handed. This might seem like an interesting piece of scientific trivia or even science fiction, but the reason for this "null result" has important implications for how we develop models of our own World System. It suggests that our preconceptions about how civilizations develop are "deeply flawed".

The new study by Pennsylvania State University astronomer Jason Wright used a new approach to searching for alien civilizations. Instead of conducting a traditional SETI survey looking for electronic messages from the stars, Wright looked for the thermodynamic consequences of galactic-scale colonization. Think about this point carefully (physicist Freeman Dyson did in the 1960's): a growing, advanced, energy-hungry technological culture would ultimately be limited by access to energy. In the current age of Peak Oil (or even after the 1973 Arab Oil Crisis), we shouldn't be surprised by this limit to growth, but our current neoclassical economic growth models contain no such limits.

A truly advanced civilization, when faced with limits to growth imposed by decreasing supplies of energy, would be capable of taking extreme measures such as harvesting energy from surrounding stars using Dyson Spheres. The important point here is that Dyson Spheres or any other advanced technology for harvesting energy would leave a mid-infrared glow of radiated heat waste. Astronomer Jason Wright, after examining 100,000 nearby galaxies, found no evidence for large-scale radiated heat waste that would signal the existence of Super-Civilizations.

The possible reasons for the null result are extremely important:

• Possibly, advanced technological civilizations always self-destruct. In the 13.8 billion years since the Big Bang, civilizations such as ours have come and gone, collapsing from energy limits, environmental damage, nuclear war, etc.
  
• Possibly, any sufficiently advanced, successful technological civilization would be indistinguishable from Nature (a position originally argued by science fiction author Karl Schroeder). Successful advanced civilizations would be more integrated with their natural environments, slower growing (even Steady-State systems), striving for technological efficiency and coming ever-closer to thermodynamic equilibrium.

Current neoclassical economic growth models predict exponential growth forever without limits to growth imposed by energy depletion. They predict the eventual construction of Dyson Spheres or some other technology to harvest energy from surrounding stars. They predict the existence of mid-infrared glow from radiated heat waste. These predictions have failed and it is time to consider neoclassical economic growth models as also having failed.

In future posts I will investigate alternative models that should now be given more serious consideration as replacements for neoclassical economic growth models.

The Steady State Economy: Realistic Model or Illusion?

The idea of a steady state economy has a long history. It was discussed by Adam Smith, John Stuart Mill, John Maynard Keynes, Nicholas Georgescu-Roegen, E.F. Schumacher,  Kenneth Boulding, and Herman Daly (all discussed here). Basically, the theory of economic growth describes the conditions under which an economy will grow (I've discussed the neoclassical growth model here) and steady-state economy theory describes the conditions under which the economy will stop growing (without collapsing). Both of these theories are flawed for reasons described by the theory of complex dissipative systems. I'll introduce some of these ideas in this post, but my major objective is to introduce the concept of steady state.

The fundamental insight from the steady state economy model is displayed in the graphic above taken from Herman Daly's (1977) Steady State Economics (San Francisco: Freeman) on page 35. Economic growth theory takes up the right-hand box in the figure. The economy, in Daly's view, produces stocks of artifacts (capital goods and stocks of knowledge) that not only deplete the Ecosystem (the left-hand box) but also generate pollution (even knowledge production does this, think of your University's power plant). Compared to the Ecosystem, which is open to Solar Energy and accepts heat pollution, the Economic system is closed. From my earlier discussion of economic growth theory (here), the economy is typically thought to be open to population growth and technological change. Daly includes both those stocks (the stock of population and the stock of knowledge) within the economic system.

If we split the drawing in half, the economy is open to input from the Ecosystem and the Ecosystem is open to pollution and depreciation from the economy as well as solar input.  The theory of open systems shows that only closed systems can reach a permanent steady state. An open system is always forced into another state by its inputs. So unless solar energy becomes an input to the Economy and, at the same time, regenerates the Ecosystem which is being depleted but the Economy, the combined system cannot reach a permanent steady state at a high level of economic development. Basically, the Second Law of Thermodynamics holds that all closed systems run down due to dissipation of energy (the theory of complex dissipative systems).

What is lurking under all this theory is the concept of openness. The subtleties are easier to understand by considering the general open system S(t) = A S(t-1) + B X(t-1) where t is time, S is the state of the system, A and B are matrices of coefficients and X is a matrix of inputs from the environment (the open part of the system). The characteristic behavior of the state-space representation is determined by properties of the A matrix, specifically its stability (all eigenvalues of A less than one). If the system is stable it will reach a steady state only when X becomes zero, that is, when the inputs are dissipated (think of X as the environment) or when X becomes a constant, that is, the environment reaches a steady state. If the system is unstable, it will growth forever, even without inputs--which is impossible. So, real world systems must ultimately become stable.

But, imagine a world which is radically open. There are all sorts of systems, some stable, some unstable (think of normal cells in the human body and cancer cells, the cancer cells are growing unstably). Each system is interacting with other systems. In this disorderly world, change over time is determined by the unstable systems. Unless the unstable systems are stabilized, they take over the stable systems. In the case of cancer cells, without killing the cancer cells (one type of steady state) they eventually kill the host system.

The view of the ecological system and the economic system advanced by the theory of steady state economics involves mildly open, stable systems. What if they are not? What if they are more like the human body? What if the Economy and the Ecological system are radically open? This will be a topic for a future post.

The theory of radically open systems possess serious problems for all attempts to specify the steady state or even the growth path of an economy. All the conclusions from economic growth theory, steady-state economic theory and simple open systems theory depend on being able to specify the state S of the system. In radically open systems, if the state is misspecified then some unmeasured, unstable force in the environment (e.g., a caner pathogen) can disrupt the system and make forecasting the future impossible. In a world of radically open systems, all systems are weakly state determined and strongly driven by the environment. For theorists and model builders, accustomed to specifying system states, specifying the entire environment is an impossible task. For us consumers of theories and models, we need an answer to the question of how open our real word systems are before we an have much faith in the conclusions of theoreticians and model builders.

The one question that the debate between economic growth theorists and steady-state economists has brought into the open is whether any of our economic systems are moving toward a steady state. And, if an economic system is moving toward a steady state (an empirical question) what might happen after that (a speculative question). The resolution of this question is important because it impacts the Ecosystem on which the Economy depends. I will present historical data from and test actual economics systems in future posts (something that is seldom done in ECON 101).

HISTORICAL NOTE

The concept of a Steady State Economy was dropped from ECON 101 after WWII as was much of what had been developed by Classical Economists in the Nineteenth Century (except David Ricardo).

For example, in the graph above is the output from a Steady-State Malthusian System model produced by the code below, if you would like to experiment yourself. Note, this model is a closed system.

Code

A Steady-state Malthusian Model. Models have a compact R-code in dse and can be run easily https://rdrr.io/snippets/. Cut-and-paste the following code into the Snippets editor window. When you run it, it should produce the shock decomposition diagram and the forecast in the graphic above.

#

#    MALTHUS

#

require(dse)

require(matlab)

f <- matrix( c(   .8, 0, 0.09478143,

               0, .8, 0.09238426,

                      0.000000000, 0.00000000,  1.000000000

),byrow=TRUE,nrow=3,ncol=3)

h <- eye(2,3)

k <- f[1:3,1:2,drop=FALSE]

TRM <- SS(F=f,H=h,K=k,

z0=c(0.09478143, 0.09238426, 1.00000000),

              output.names=c("N","QA"))

stability(TRM)

TRM

TRM.data <- simulate(TRM,sampleT=20,

,start=1,freq=1,noise=matrix(0,20,2))

TRM.model <- l(TRM,TRM.data)

#tfplot(TRM.model)

shockDecomposition(toSSChol(TRM))

tfplot(forecast(TRM.model,horizon=20))

Is Climate Change an Economic Problem?

In Chapter 3 of the Climate Casino, William Nordhaus argues that climate change is an economic rather than scientific problem (find my reviews of other chapters here). The argument is controversial. Heat waves, melting ice sheets, droughts, rising sea level, storms and record temperatures are typically thought of as a complex scientific problem. Prof. Nordhaus argues that the economic problem is more straightforward: the people that are dumping CO2 into the atmosphere don't have to pay for their emissions. If they did, they would be more careful about what they did with the by-products of burning fossil fuels. Economists call this an externality, that is, "...a by-product of economic activity that causes damages to innocent bystanders" (p 18). Because no one owns the atmosphere, not even government, there is no one to charge for its use and no market in which to purchase such environmental services. 

Economists like this argument; it shifts the debate from "who is to blame" to "who should pay" for damages caused by climate change. Unfortunately, polluters argue that no one is actually being harmed by CO2 emissions and that there is no observable link between CO2 emissions, climate change and economic damages. 

Prof. Nordhaus confronts these arguments by presenting the data and using models to predict the future. Since 1900, global CO2 emissions have been rising linearly (on a log scale) in his Figure 2 above (p. 21). Although there have been some interesting periods of acceleration (after WWII, for example), the general trend has been continuously upward.

Over the same period, there have been technological improvements that increased the efficiency with which fossil fuels are being burned. Technical efficiency has reduced the carbon intensity of, at least, the US economy as displayed in Figure 3 above (from page 22 of the Climate Casino, again presented on a log scale). This process is called decarbonization, but "...while the carbon intensity of production is declining, it is not declining fast enough to reduce total CO2 emissions, either for the world or for the United States" (p. 23). In other words, technological change will not get us out of this dilemma and neither will the substitution of renewable resources because they are more expensive than fossil fuels--returning us to the economic problem.

Another part of the economic problem is that the negative impacts of fossil fuel burning, even with increased technical efficiency in their use, will not be felt for many years in the future. To look out past 2013 when the Climate Casino was written into the future when environmental effects could become serious, we need a mathematical model that can make quantitative predictions. Prof. Nordhaus has such a model, called the DICE model, as in "roll the dice". In Figure 3 above (from p. 33 on the Climate Casino), emissions projections out to 2100 from the DICE model are compared to projections made by eleven EMF models from the Stanford University Energy Modeling Forum. The projections from the DICE model compare quite favorably with the average of the EMF models.

Prof. Nordhaus acknowledges that there is a great deal of "uncertainty" in these estimates. CO2 emissions in 2100 range from about 40 billion to over 130 billion tons per year. The differences in predictions have nothing to do with "chance" or "random events" but rather with the different structures and assumptions of the models. The models are totally deterministic. 

In the face of uncertainty about the future (and uncertainty about the models), Prof. Nordhaus makes the following suggestion:

Start with a best-guess scenario for output, population, emissions, and climate change. Take policies that will best deal with the costs and impacts in this best-guess case. Then consider the potential for low-probability but high-consequence outcomes in the Climate Casino. Take further steps to provide insurance against these dangerous outcomes. But definitely do not assume that the problems will just disappear. (p. 34-35)

This sounds reasonable, but the arguments in Chapter 3 of the Climate Casino do not seem well connected to the suggestion. Is Prof. Nordhaus saying that the DICE and EMF-22 models are necessary to making judgments about either the future or about policy actions that should be taken in the present? Are the DICE and EMF-22 models an accurate simplification of how the world economy works? How do we know that? Have the models been somehow validated? Is there a way we can check the projections being made by the models? If we are going to run policy experiments on the models, how do we know the world economy will respond in the same way? How would we even think about implementing policies that would have an impact on the world economy? Is there any way for us to evaluate these models as systems? What kind of confidence would we have to have in models to make predictions almost eighty years into the future? Is there any reason to believe that we can really predict that far into the future? And, how do the models prove that climate change is really an economic problem? As I will argue later, it seems more realistic to view it as a "system" problem, specifically a problem of the Capitalist world-system, a system that is great at exploiting environmental systems for profit. Is it possible to change this system? How have macro-societal systems changed in the past?

So many questions! They will all have to be topics for future posts, keeping in mind that this blog is about causal macrosystems and not about climate change. Unfortunately, it is in the area of climate change that the stakes are highest and to which, hopefully, researchers are bringing their best models!

EXERCISES

1. Download the DICE users manual (here). How well was the model validated? What evidence was offered to support using the model for policy experiments? How were the model parameters determined? Evaluate Ackerman's (2007: 1657 here) concern that "...the optimistic projections and modest optimal policies often attributes to models such as DICE may be artifacts of parameter choices, rather than robust forecasts about an uncertain future" For more topics to pursue in the DICE manual, see below.
2. Follow up on the references offered in the DICE manual, particularly the Ramsey model (the one that DICE is based on here) and versions of DICE that are not deterministic (here). Try to find a reference that validates the model on historical data.
3. Download the "Simple Excel" version of DICE (here and here). Try to understand the model and explore different runs. Make simple changes to the alternative assumptions. NOTE: the Excel model seems "under development" and I have not been able to get it running. Skip to Exercise 4 if you're having trouble.
4. Download the free educational version VENSIM PLE (here) and the version of DICE in the VENSIM language from Tom Fiddaman's System Dynamics Model Library, specifically here. Become familiar with the VENSIM language. Most of the causal macrosystems I will review have been implemented in VENSIM and it is free.

Using the DICE manual, evaluate and critique the following statements:

1. DICE is an optimization model. "...the use of optimization can be interpreted in two ways: First, from a positive point of view, optimization is a means of simulating the behavior of a system of competitive markets; and, second, from a normative point of view, it is a possible approach to comparing the impact of alternative paths or policies on economic welfare" (p. 6)
2. "In the DICE and RICE models, the world or individual regions are assumed to have well-defined preferences, represented by a social welfare function, which ranks different paths of consumption" (p. 6).
3. "Technological change takes two forms: economy-wide technological change and carbon-saving technological change. The level of total factor productivity ... is a logistic equation similar to that of population...TFP growth declines over time" (p. 9). This is an interesting change of specification. In the neoclassical economic growth model, TFPG is simply an exponential function, meaning it increases forever since there are no inherent limits to the growth of knowledge (see my earlier post here).
4. "...providing reliable estimates of the damages from climate change over the long run has proven extremely difficult" (p. 10 and following).
5. "The DICE-2013R model explicitly includes a backstop technology, which is a technology that can replace all fossil fuels" (p. 13).
6. "The model assumes that incremental extraction costs are zero and that carbon fuels are efficiently allocated over time by the market" (p. 14)
7. "As with the economics, the modeling philosophy for the geophysical relationships has been to use parsimonious specifications so that the theoretical model is transparent and so that the optimization model is empirically and computationally tractable" (p. 15).
8. "This discussion implies that we can interpret optimization models as a device for estimating the equilibrium of a market economy. As such, it does not necessarily have a normative interpretation. Rather, the maximization is an algorithm for finding the outcome of efficient competitive markets" (p. 22). How well does this interpretation fit the world system?
9. "The real interest rate is a critical variable for determining climate policy" (p. 25).
10. "Earlier versions of DICE and other IAMs tended to have a stagnationist bias, with the growth rate of total factor productivity declining rapidly in the coming decades. The current version assumes continued rapid total factor productivity growth over the next century, particularly for developing countries" (p. 36).
11. "The 2007 model over predicted output by about 6 percent, primarily because of failure to see the Great Recession. We have reduced output to put the new path on the current starting point" (p. 41).
12. "Since many computerized climate and integrated assessment models contain between 10,000 and 1 million SLOC...[source lines of code]..., there is the prospect of many bugs contained in our code" (p. 52).
13. "...there is a tendency to develop models that increase in parallel with the rapidly expanding frontier of computational abilities. This leads to increasingly large and complex models. We need also to ask, do we fully understand the implication of our assumptions? Is disaggregation really helping or hurting?" (p. 53)
14. "The properties of linear stochastic systems are moderately well-understood, but that is not the case of all non-linear stochastic systems" (p. 54).

Stacking the Deck at the Climate Casino

This year, Prof. William Nordhaus (Yale) published The Climate Casino: Risk, Uncertainty and Economics for a Warming World. The book continues Professor Nordhaus' forty year career investigating the economics of energy resources. The book was also recently reviewed by Paul Krugman (Princeton, here) and is interesting on many levels: the use of sophisticated macroeconomic models, attempts to forecast far out into the future, the use of probability and uncertainty (the models are purely deterministic), Prof. Nordhaus' willingness to make very specific policy recommendations (increase the price of CO2 and increase technological change in the energy sector), and finally his long running argument about the Limits to Growth.

The initial argument about the Limits to Growth grew out of Prof. Nordhaus' work on nonrenewable resources, particularly fossil fuels. The argument is captured in the causal diagram above.  A group of engineers at MIT were arguing that the depletion of nonrenewable resources would lead to economic collapse. Prof. Nordhaus countered that technological change would create substitutes that would prevent any future collapse from happening. Paul Krugman points out that Nordhaus was wrong about the specifics in his long-term forecasts (real oil prices are about twice as high as the models predicted while real coal and natural gas prices are much lower) but was right to have attempted the forecast (presumably, we should be learning from what went wrong).

In any event, the argument about nonrenewable resources has morphed into an argument about the impacts of CO2 emissions on climate change, that is, the impact of extracting fossil fuels and pumping the by-products into the atmosphere.

The new argument is summarized in the causal diagram at the left which in part, is taken from page 10 of the Climate Casino. The resource extraction issue is still lurking in the link between economic growth and CO2 emissions but technological change is now supposed to help us reduce emissions by decreasing the carbon intensity of the economy.

Nordhaus accepts the basic causal link that Economic Growth -> CO2 Emissions -> Climate Change -> Ecological Impacts. He is less certain about what will happen as a result of observable ecological impacts (sea level rise, super typhoons, water scarcity, habitat loss, coastal flooding, ocean acidification, reduction of agricultural yields, and other impacts). His major argument is that policy change is needed, but his concern is also with avoiding negative effects on economic growth. The policy measures he is most focused on are cap-and-trade systems, carbon taxes, and environmental regulation. Nordhaus is looking for a policy that will raise the price of carbon, can be universally applied and will not inhibit economic growth.

One causal link that is not presented in the summary diagram on page 10 of the Climate Casino is the link between Ecological Impacts and Economic Growth. If all the negative ecological impacts listed above actually happen, negative economic impacts (Limits to Growth) would seem to be highly likely. Possibly, this is just a matter of emphasis. Policy change is already happening and Prof. Nordhaus wants to shape the debate (stack the deck?). Aside from the political agenda, there are deeper issues that go back to the reasons Prof. Nordhaus' original long-range forecasts were off the mark.

The Climate Casino uses an embellished version of the same macroeconomic growth model used to make the original predictions (here) about nonrenewable resource allocation. If the initial forecasts were not very accurate, has anything fundamental been changed in the new version to produce better projections? Publication of the Climate Casino gives us the opportunity to look back at Prof. Nordhaus' model and his competitor's models (to include the Limits to Growth models). In future posts, I'll review chapters of the Climate Casino and look more closely at the underlying models and how they were constructed. The "deep dives" into model specifics are not particularly reassuring.

Can Biodiversity Coexist with Food Production?

Watch Balancing Costa Rica's Farming With Preservation with Nature on PBS. See more from PBS NewsHour.

To my knowledge, there are no macroeconomic models that include measures of biodiversity. From the video above, it would seem to be a major omission. It's also interesting that Costa Rica is one of the first countries to recognize that biodiversity can coexist with food production. The question for this blog is how to include such measures (how to value Nature) in macro models, a topic I'll take up in future posts.

Causal Analysis of the Neoclassical Economic Growth Model

The neoclassical economic growth model, in one form or another, encapsulates the conventional wisdom on long-run economic growth. The model captures many of the issues thought to be important: productivity, capital formation, population growth, and technology. In this post, I will present the equations for the Solow-Swan model, convert the model into a causal path diagram and explore the model's assumptions, predictions, insights and shortcomings.

The heart of the Solow-Swan model is Cobb-Douglas production function which models total production, Y, as a function of Total Factor Productivity Growth (TFPG) or technology, A, capital, K, and labor, L. The exponent alpha is simultaneously the elasticity of production, the capital-income ratio and the the shares of capital and labor in output. Capital investment (I) comes from savings, sY, and when compared to depreciation, (1-d), determines growth of the capital stock. The growth of the labor force is a function of population growth, N (the difference between births and deaths, b-d), and the percent of population employed, e. Technological change (TFPG), A, is exogenously given and grows at the rate g.

Although the functional form used in the Solow-Swan model has appealing properties (constant elasticity of production, constant returns to scale, constant shares returned to capital and labor, diminishing marginal returns, etc.), there is really little theoretical justification for the equations as written. Working with US data from 1927-1947, Paul Douglas needed some functional form to use in regression analysis and the multiplicative model was suggested by the mathematician Charles Cobb, without theoretical development.

Dropping the functional forms and mapping the causal connections among variables yields the directed graph above where total output, Y, is replaced by gross domestic product, Q. The causal model makes clear that as long as population and technological change are growing exponentially, gross domestic product will also grow exponentially, forever. Also, the idea that as long as technological change exceeds population growth, output-per-capita will increase is appealing to economists and addresses criticisms of the Malthusian model where population growth exceeds productive growth.

The assumption of uncontrolled exponential growth can be modified somewhat by assuming population growth is exogenous and specifying a regression model (Wonnacott and Wonnacott, 1970: 93-95):

However, technological change (TFPG, the regression constant) still is assumed to grow exponentially. For reasonable long-run projections it can be assumed that, at some point in the future, technological progress will decline and eventually stop. For example, in the DICE model:

 "Assumptions about future growth trends are as followed: The rate of growth of population was assumed to decrease slowly, stabilizing at 10.5 billion people in the 22nd century. The rate of growth of total factor productivity [the growth rate of A(t) in Eq. 3] was calculated to be 1.3% per annum in the period from 1960 to 1989. This rate was assumed to decline slowly over the coming decades. We calibrate the model by fitting the solution for the first three decades to the actual data for 1965, 1975, and 1985 and then optimizing for capital accumulation and GHG emissions in the future..." page 1317 in An Optimal Transition Path for Controlling Greenhouse Gases" William D. Nordhaus (1992).

There are many problems applying the neoclassical growth. For estimation, capital stock measures are not available or have questionable validity in many countries. Although the number of inputs (land, intermediate goods, etc.) are typically expanded when used for growth accounting, as a transformation function the model does not make sense (neither labor or capital are not transformed into output through production). For cross-country comparisons, the assumption that one production function would cover all countries (Somalia would have the same production function as would Canada) seems unrealistic. Since the model is nonlinear, it cannot be aggregated from the individual level to the nation state or from the nation state to world system. Finally, the assumption of decreasing technological change in the future is only made because exponential growth forever is unreasonable not because there is any evidence for such an assumption.

A lot more can be said about the neoclassical production function, particularly that causality might run in the opposite direction from that assumed in the Cobb-Douglas production function. The other points are best made when considering other macro models, which I will do in future posts.

Do We Need Better Econometric Models?

I have made a number of arguments in the last post (here) suggesting that we need better econometric models. One of the arguments was that econometric forecasting models did a poor job of predicting the Subprime Mortgage Crisis (the Financial Crisis of 2007-2008).

Yesterday the Federal Reserve Open Market Committee (FOMC) released transcripts of closed-door policy meetings in 2007 (here), at the start of the Subprime Mortgage Crisis. In the quote below from the Washington Post (here) we see that the models used by the FED staff economists were not predicting the upcoming crisis. The reasons why these models failed is a major concern of this blog and should be a major topic of discussion for the economics profession.

In December 2007, the month that the recession is now known to have begun, Fed officials were working from economic projections that would prove wildly inaccurate. They forecast sluggish but sustained growth in 2008 followed by a bounceback in 2009. Staff economist Dave Stockton acknowledged that his was a more optimistic view:

“Our forecast could admittedly be read as still painting a pretty benign
picture: Despite all the financial turmoil, the economy avoids recession and, even with steeply higher prices for food and energy and a lower exchange value of the dollar, we achieve some modest edging-off of inflation.”

Dave Stockton, who was the Chief Economist and Director of Research and Statistics at the Federal Reserve Board from 2000-2011, was basing his forecasts on those being produced by the FED econometric models. The burning question is why those models performed so poorly during the financial crisis and whether anything has been done about it after the crisis.

Typically, econometric models predict from one period to the next, whether that is months, quarters of years. The models are basically a collection of structural equations, some of which are dynamic, that is, have a time component. For example, the simple model Q(t) = A Q(t-1) + B X(t-1) + E would look similar to the dynamic equations where Q is some variable, t represents a time point, A represents a coefficient matrix for the endogenous variables, B represents a coefficient matrix for the exogenous variables (X) and E represents error.

The problem here is that when the model gets to time t, the predictions for Q contain E. If E is non-random (systematic positive shocks from the liar loans in the financial sector, for example), Q keeps accumulating errors and creates a bubble. It's ability to predict Q(t) is compromised by adding up errors from the developing Subprime Mortgage Bubble and makes everything look pretty rosy, as is evident from Mr. Stockton's comments.

The way in which econometric models are used, in this case simulating from one period to the next, is one of their major problems. The other problem is the theory (or lack of it) underlying the models. Both of these arguments will take many more posts to develop.