## Sir David Hendry on the inadequacies of DSGE models

from **Lars Syll**

In most aspects of their lives humans must plan forwards. They take decisions today that affect their future in complex interactions with the decisions of others. When taking such decisions, the available information is only ever a subset of the universe of past and present information, as no individual or group of individuals can be aware of all the relevant information. Hence, views or expectations about the future, relevant for their decisions, use a partial information set, formally expressed as a conditional expectation given the available information.

Moreover, all such views are predicated on there being no un-anticipated future changes in the environment pertinent to the decision. This is formally captured in the concept of ‘stationarity’. Without stationarity, good outcomes based on conditional expectations could not be achieved consistently. Fortunately, there are periods of stability when insights into the way that past events unfolded can assist in planning for the future.

The world, however, is far from completely stationary. Unanticipated events occur, and they cannot be dealt with using standard data-transformation techniques such as differencing, or by taking linear combinations, or ratios. In particular, ‘extrinsic unpredictability’ – unpredicted shifts of the distributions of economic variables at unanticipated times – is common. As we shall illustrate, extrinsic unpredictability has dramatic consequences for the standard macroeconomic forecasting models used by governments around the world – models known as ‘dynamic stochastic general equilibrium’ models – or DSGE models …

Many of the theoretical equations in DSGE models take a form in which a variable today, say incomes (denoted as yt) depends inter alia on its ‘expected future value’… For example, yt may be the log-difference between a de-trended level and its steady-state value. Implicitly, such a formulation assumes some form of stationarity is achieved by de-trending.

Unfortunately, in most economies, the underlying distributions can shift unexpectedly. This vitiates any assumption of stationarity. The consequences for DSGEs are profound. As we explain below, the mathematical basis of a DSGE model fails when distributions shift … This would be like a fire station automatically burning down at every outbreak of a fire. Economic agents are affected by, and notice such shifts. They consequently change their plans, and perhaps the way they form their expectations. When they do so, they violate the key assumptions on which DSGEs are built.

A great article, confirming much of Keynes’s critique of econometrics and underlining that to understand real world ”non-routine” decisions and unforeseeable changes in behaviour, stationary probability distributions are of no avail. In a world full of genuine uncertainty – where real historical time rules the roost – the probabilities that ruled the past are not those that will rule the future.

When we cannot accept that the observations, along the time-series available to us, are independent … we have, in strict logic, no more than one observation, all of the separate items having to be taken together. For the analysis of that the probability calculus is useless; it does not apply … I am bold enough to conclude, from these considerations that the usefulness of ‘statistical’ or ‘stochastic’ methods in economics is a good deal less than is now conventionally supposed … We should always ask ourselves, before we apply them, whether they are appropriate to the problem in hand. Very often they are not … The probability calculus is no excuse for forgetfulness.

Time is what prevents everything from happening at once. To simply assume that economic processes are stationary is not a sensible way for dealing with the kind of genuine uncertainty that permeates open systems such as economies.

Econometrics is basically a deductive method. Given the assumptions (such as manipulability, transitivity, Reichenbach probability principles, separability, additivity, linearity etc) it delivers deductive inferences. The problem, of course, is that we will never completely know when the assumptions are right. Real target systems are seldom epistemically isomorphic to axiomatic-deductive models/systems, and even if they were, we still have to argue for the external validity of the conclusions reached from within these epistemically convenient models/systems. Causal evidence generated by statistical/econometric procedures may be valid in “closed” models, but what we usually are interested in, is causal evidence in the real target system we happen to live in.

Advocates of econometrics want to have deductively automated answers to fundamental causal questions. But to apply “thin” methods we have to have “thick” background knowledge of what’s going on in the real world, and not in idealized models. Conclusions can only be as certain as their premises – and that also applies to the quest for causality and forecasting predictability in econometrics.

The problem is not formally stated because DSGE models can be applied to many parts of the whole system. Unless the rest of it is properly included there will always be different results coming out of the different combination of the parts that were in. This attitude might make it appear that the only kind of model that is fully representative is very complex, but this is not the true situation provided only the most basic and significant parts are included and the rest scrapped. Then the result must be quite close to reality and give good forecasting power.

Nice to see economists catching up with futurists and systems analysts ! Yet so many have already made similar critiques of DSGE and other economic models ,including my own for two decades ( see my Mapping the Global Transition to the Solar Age:From Economism to Earth Systems Science , Foreword by NASA Chief Scientist Dennis Bushnell, downloadable from http://www.ethicalmarkets.com ). When can we expect the economics profession to clarify these shortcomings to policy makers ? Also see our page on Beyond GDP and http://www.beyond.gdp.eu

Predictably confused

Comment on ‘Sir David Hendry on the inadequacies of DSGE models’

“The future is unpredictable.” (Feynman, 1992, p. 147) Four words! Compare this to what economists have uttered about this issue without getting one iota further. But wait, are physicists not famous for their accurate predictions? Could it be that economists have gotten something badly wrong?

Let us have a closer look. If you show a physicist an apple tree and ask him when every single apple will fall, then he will tell you that this kind of event is not predictable. But he can tell you something else. If an apple has started to fall then he can tell you exactly its location and velocity after t seconds. The physicist ‘predicts’ the coordinates with high precision and everybody can test it. (OK, you did not want to know this to benign with, and exactly these layman’s expectations regularly cause the irritations with science.)

Likewise: imagine somebody throws blindly three coins into a large sandbox. Clearly, the three coins form a random triangle and no one can predict its form and size. Yet, the mathematician can ‘predict’ with certainty that the sum of angles is 180 degrees (if the sandbox is euclidean).

While the future is ‘unpredictable’ certain aspects may be ‘predictable’ with high precision. Therefore, we can agree with Keynes that “the price of copper and the rate of interest twenty years hence” is unpredictable without accepting his famous all-round capitulation “We simply do not know.” (Keynes, 1937, p. 214)

Example: an elementary consumption economy can be described by this deductively derived formula

This formula holds in every single period from past to future (2014, eq. (12)). In other words, we have a testable economic law. Test it twenty years hence and you will find out that it is true. Where, then, does the difficulty with prediction come in? The crucial point is that the variables that underlay the four rhos are unpredictable random variables.

Perhaps it sounds a bit paradoxical: there are deterministic economic laws which hold ‘on the whole’ while the components vary at random or are even uncertain in Keynes’s sense. So the concept of uncertainty can coexists with the concept of economic law. Hence, Keynesian uncertainty should not stop us from looking out for deterministic and testable economic laws. Attention, they are with absolute certainty not to be found where misguided Orthodoxy has looked for in the past!

The otherwise redundant DSGE debate shows that the representative economist is still a bit confused about the different aspects of prediction.

Egmont Kakarot-Handtke

References

Feynman, R. P. (1992). The Character of Physical Law. London: Penguin.

Kakarot-Handtke, E. (2014). The Synthesis of Economic Law, Evolution, and History. SSRN Working Paper Series, 2500696: 1–22. URL

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2500696

Keynes, J. M. (1937). The General Theory of Employment. Quarterly Journal of Economics, 51(2): 209–223. URL http://www.jstor.org/stable/1882087