The state of modern macroeconomics – indescribable misery

23 Apr, 2013 at 12:59 | Posted in Economics | 3 Comments

Neoclassical economic theory today is in the story-telling business whereby economic theorists create make-believe analogue models of the target system – usually conceived as the real economic system. This modeling activity is considered useful and essential. Since fully-fledged experiments on a societal scale as a rule are prohibitively expensive, ethically indefensible or unmanageable, economic theorists have to substitute experimenting with something else. To understand and explain relations between different entities in the real economy the predominant strategy is to build models and make things happen in these “analogue-economy models” rather than engineering things happening in real economies.

Formalistic deductive “Glasperlenspiel” can be very impressive and seductive. But in the realm of science it ought to be considered of little or no value to simply make claims about the model and lose sight of reality.

Neoclassical economics has since long given up on the real world and contents itself with proving things about thought up worlds. Empirical evidence only plays a minor role in economic theory, where models largely function as a substitute for empirical evidence. But “facts kick”, as Gunnar Myrdal used to say. Hopefully humbled by the manifest failure of its theoretical pretences, the one-sided, almost religious, insistence on axiomatic-deductivist modeling as the only scientific activity worthy of pursuing in economics will give way to methodological pluralism based on ontological considerations rather than formalistic tractability.

The financial crisis of 2007-08 hit most laymen and economists with surprise. What was it that went wrong with our macroeconomic models, since they obviously did not foresee the collapse or even make it conceivable?

In modern neoclassical macroeconomics – Dynamic Stochastic General Equilibrium (DSGE), New Synthesis, New Classical and “New Keynesian” – variables are treated as if drawn from a known “data-generating process” that unfolds over time and on which we therefore allegedly have access to heaps of historical time-series.

Modern macroeconomics obviously did not anticipate the enormity of the problems that unregulated “efficient” financial markets created. Why? Because it builds on the myth of us knowing the “data-generating process” and that we can describe the variables of our evolving economies as drawn from an urn containing stochastic probability functions with known means and variances.

In the end this is what it all boils down to. We all know that many activities, relations, processes and events are genuinely uncertaint. The data do not unequivocally single out one decision as the only “rational” one. Neither the economist, nor the deciding individual, can fully pre-specify how people will decide when facing uncertainties and ambiguities that are ontological facts of the way the world works.

Some macroeconomists, however, still want to be able to use their hammer. So they decide to pretend that the world looks like a nail, and pretend that uncertainty can be reduced to risk. So they construct their mathematical models on that assumption.

I think that macroeconomists ought to be more critical of the present state of macroeconomics than they are. If macroeconomic models – no matter of what ilk –  build on microfoundational assumptions of representative actors, rational expectations, market clearing and equilibrium, and we know that real people and markets cannot be expected to obey these assumptions, the warrants for supposing that conclusions or hypothesis of causally relevant mechanisms or regularities can be bridged, are obviously non-justifiable. Incompatibility between actual behaviour and the behaviour in macroeconomic models building on representative actors and rational expectations-microfoundations is not a symptom of “irrationality”. It rather shows the futility of trying to represent real-world target systems with models flagrantly at odds with reality. 

A gadget is just a gadget – and brilliantly silly models do not help us working with the fundamental issues of modern economies. So let’s take a critical  look at modern macroeconomics and try to pinpoint what makes its state today such an indescribable misery.
Real business cycles theory

Real business cycles theory (RBC) basically says that economic cycles are caused by technologyinduced changes in productivity. It says that employment goes up or down because people choose to work more when productivity is high and less when it’s low. This is of course nothing but pure nonsense. In yours truly’s History of Economic Theories (4th ed, 2007, p. 405) it was concluded that

the problem is that it has turned out to be very difficult to empirically verify the theory’s view on economic fluctuations as being effects of rational actors’ optimal intertemporal choices … Empirical studies have not been able to corroborate the assumption of the sensitivity of labour supply to changes in intertemporal relative prices. Most studies rather points to expected changes in real wages having only rather little influence on the supply of labour.

And this is what Lawrence Summersin Some Skeptical Observations on Real Business Cycle Theory – had to say about RBC:

The increasing ascendancy of real business cycle theories of various stripes, with their common view that the economy is best modeled as a floating Walrasian equilibrium, buffeted by productivity shocks, is indicative of the depths of the divisions separating academic macroeconomists …

If these theories are correct, they imply that the macroeconomics developed in the wake of the Keynesian Revolution is well confined to the ashbin of history. And they suggest that most of the work of contemporary macroeconomists is worth little more than that of those pursuing astrological science …

The appearance of Ed Prescott’ s stimulating paper, “Theory Ahead of Business Cycle Measurement,” affords an opportunity to assess the current state of real business cycle theory and to consider its prospects as a foundation for macroeconomic analysis …

My view is that business cycle models of the type urged on us by Prescott have nothing to do with the business cycle phenomena observed in The United States or other capitalist economies …

Presoctt’s growth model is not an inconceivable representation of reality. But to claim that its prameters are securely tied down by growth and micro observations seems to me a gross overstatement. The image of a big loose tent flapping in the wind comes to mind …

In Prescott’s model, the central driving force behind cyclical fluctuations is technological shocks. The propagation mechansim is intertemporal substitution in employment. As I have argued so far, there is no independent evidence from any source for either of these phenomena …

Imagine an analyst confronting the market for ketchup. Suppose she or he decided to ignore data on the price of ketchup. This would considerably increase the analyst’s freedom in accounting for fluctuations in the quantity of ketchup purchased … It is difficult to believe that any explanation of fluctuations in ketchup sales that did not confront price data would be taken seriously, at least by hard-headed economists.

Yet Pescott offers an exercise in price-free economics … Others have confronted models like Prescott’s to data on prices with what I think can fairly be labeled dismal results. There is simply no evidence to support any of the price effects predicted by the model …

Improvement in the track record of macroeconomics will require the development of theories that can explain why exchange sometimes work and other times breaks down. Nothing could be more counterproductive in this regard than a lengthy professional detour into the analysis of stochastic Robinson Crusoes.

Thomas Sargent was awarded The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel for 2011 for his “empirical research on cause and effect in the macroeconomy”. In an interview with Sargent in The Region , one could read the following defense of “modern macro”:

Sargent: I know that I’m the one who is supposed to be answering questions, but perhaps you can tell me what popular criticisms of modern macro you have in mind.
Rolnick: OK, here goes. Examples of such criticisms are that modern macroeconomics makes too much use of sophisticated mathematics to model people and markets; that it incorrectly relies on the assumption that asset markets are efficient in the sense that asset prices aggregate information of all individuals; that the faith in good outcomes always emerging from competitive markets is misplaced; that the assumption of “rational expectations” is wrongheaded because it attributes too much knowledge and forecasting ability to people; that the modern macro mainstay “real business cycle model” is deficient because it ignores so many frictions and imperfections and is useless as a guide to policy for dealing with financial crises; that modern macroeconomics has either assumed away or shortchanged the analysis of unemployment; that the recent financial crisis took modern macro by surprise; and that macroeconomics should be based less on formal decision theory and more on the findings of “behavioral economics.” Shouldn’t these be taken seriously?
Sargent: Sorry, Art, but aside from the foolish and intellectually lazy remark about mathematics, all of the criticisms that you have listed reflect either woeful ignorance or intentional disregard for what much of modern macroeconomics is about and what it has accomplished. That said, it is true that modern macroeconomics uses mathematics and statistics to understand behavior in situations where there is uncertainty about how the future will unfold from the past. But a rule of thumb is that the more dynamic, uncertain and ambiguous is the economic environment that you seek to model, the more you are going to have to roll up your sleeves, and learn and use some math. That’s life.

Are these the words of an empirical macroeconomist? To me it sounds like the same old axiomatic-deductivist mumbo jumbo that parades as economic science of today.

There are many kinds of useless economics held in high regard within mainstream economics establishment today . Few – if any – are less deserved than the macroeconomic theory – mostly connected with Nobel laureates Finn Kydland, Robert Lucas, Edward Prescott and Thomas Sargent – called calibration.


In an interview by Seppo Honkapohja and Lee Evans (Macroeconomic Dynamics 2005, vol. 9) Thomas Sargent says:

Evans and Honkapohja: What were the profession’s most important responses to the Lucas Critique?

Sargent: There were two. The first and most optimistic response was complete rational expectations econometrics. A rational expectations equilibrium is a likelihood function. Maximize it.

Evans and Honkapohja: Why optimistic?

Sargent: You have to believe in your model to use the likelihood function. it provides a coherent way to estimate objects of interest (preferences, technologies, information sets, measurement processes) within the context of a trusted model.

Evans and Honkapohja: What was the second response?

Sargent: Various types of calibration. Calibration is less optimistic about what your theory can accomplish because you would only use it if you din’t fully trust your entire model, meaning that you think your model is partly misspecified or incompetely specified, or if you trusted someone else’s model and data set more than your own. My recollection is that Bob Lucas and Ed Prescott were initially very enthusiastic about rational expetations econometrics. After all, it simply involved imposing on ourselves the same high standards we had criticized the Keynesians for failing to live up to. But after about five years of doing likelihood ratio tests on rational expectations models, I recall Bob Lucas and Ed Prescott both telling me that those tests were rejecting too many good models. The idea of calibration is to ignore some of the probabilistic implications of your model but to retain others. Somehow, calibration was intended as a balanced response to professing that your model, although not correct, is still worthy as a vehicle for quantitative policy analysis….

Evans and Honkapohja: Do you think calibration in macroeconomics was an advance?

Sargent: In many ways, yes. I view it as a constructive response to Bob’ remark that “your likelihood ratio tests are rejecting too many good models”. In those days… there was a danger that skeptics and opponents would misread those likelihood ratio tests as rejections of an entire class of models, which of course they were not…. The unstated cse for calibration was that it was a way to continue the process of acquiring experience in matching rational expectations models to data by lowering our standards relative to maximum likelihood, and emphasizing those features of the data that our models could capture. Instead of trumpeting their failures in terms of dismal likelihood ratio statistics, celebrate the featuers that they could capture and focus attention on the next unexplained feature that ought to be explained. One can argue that this was a sensible response… a sequential plan of attack: let’s first devote resources to learning how to create a range of compelling equilibrium models to incorporate interesting mechanisms. We’ll be careful about the estimation in later years when we have mastered the modelling technology…

But is the Lucas-Kydland-Prescott-Sargent calibration really an advance?

Let’s see what two eminent econometricians have to say. In Journal of Economic Perspective (1996, vol. 10) Lars Peter Hansen and James J. Hickman writes:

It is only under very special circumstances that a micro parameter such as the intertemporal elasticity of substitution or even a marginal propensity to consume out of income can be ‘plugged into’ a representative consumer model to produce an empirically concordant aggregate model … What credibility should we attach to numbers produced from their ‘computational experiments’, and why should we use their ‘calibrated models’ as a basis for serious quantitative policy evaluation? … There is no filing cabinet full of robust micro estimats ready to use in calibrating dynamic stochastic equilibrium models … The justification for what is called ‘calibration’ is vague and confusing.

This is the view of econometric methodologist Kevin Hoover :

The calibration methodology, to date, lacks any discipline as stern as that imposed by econometric methods.

Error-probabilistic statistician Aris Spanos – in Error and Inference (Mayo & Spanos, 2010, p. 240) – is no less critical:

Given that “calibration” purposefully foresakes error probabilities and provides no way to assess the reliability of inference, how does one assess the adequacy of the calibrated model? …

The idea that it should suffice that a theory “is not obscenely at variance with the data” (Sargent, 1976, p. 233) is to disregard the work that statistical inference can perform in favor of some discretional subjective appraisal … it hardly recommends itself as an empirical methodology that lives up to the standards of scientific objectivity

And this is the verdict of Nobel laureate Paul Krugman :

The point is that if you have a conceptual model of some aspect of the world, which you know is at best an approximation, it’s OK to see what that model would say if you tried to make it numerically realistic in some dimensions.

But doing this gives you very little help in deciding whether you are more or less on the right analytical track. I was going to say no help, but it is true that a calibration exercise is informative when it fails: if there’s no way to squeeze the relevant data into your model, or the calibrated model makes predictions that you know on other grounds are ludicrous, something was gained. But no way is calibration a substitute for actual econometrics that tests your view about how the world works.

In physics it may possibly not be straining credulity too much to model processes as ergodic – where time and history do not really matter – but in social and historical sciences it is obviously ridiculous. If societies and economies were ergodic worlds, why do econometricians fervently discuss things such as structural breaks and regime shifts? That they do is an indication of the unrealisticness of treating open systems as analyzable with ergodic concepts.

The future is not reducible to a known set of prospects. It is not like sitting at the roulette table and calculating what the future outcomes of spinning the wheel will be. Reading Sargent and other calibrationists one comes to think of Robert Clower’s apt remark that

much economics is so far removed from anything that remotely resembles the real world that it’s often difficult for economists to take their own subject seriously.

Instead of assuming calibration and rational expectations to be right, one ought to confront the hypothesis with the available evidence. It is not enough to construct models. Anyone can construct models. To be seriously interesting, models have to come with an aim. They have to have an intended use. If the intention of calibration and rational expecteations is to help us explain real economies, it has to be evaluated from that perspective. A model or hypothesis without a specific applicability is not really deserving our interest.

To say, as Edward Prescott that

one can only test if some theory, whether it incorporates rational expectations or, for that matter, irrational expectations, is or is not consistent with observations

is not enough. Without strong evidence all kinds of absurd claims and nonsense may pretend to be science. We have to demand more of a justification than this rather watered-down version of “anything goes” when it comes to rationality postulates. If one proposes rational expectatons one also has to support its underlying assumptions. None is given, which makes it rather puzzling how rational expectations has become the standard modeling assumption made in much of modern macroeconomics. Perhaps the reason is, as Paul Krugman has it, that economists often mistake

beauty, clad in impressive looking mathematics, for truth.

But I think Prescott’s view is also the reason why calibration economists are not particularly interested in empirical examinations of how real choices and decisions are made in real economies. In the hands of Lucas, Prescott and Sargent, rational expectations has been transformed from an – in principle – testable hypothesis to an irrefutable proposition. Irrefutable propositions may be comfortable – like religious convictions or ideological dogmas – but it is not science.
“New Keynesianism”

Not that long ago, Paul Krugman had a post up on his blog discussing “New Keynesian” macroeconomics and the definition of necoclassical economics:

So, what is neoclassical economics? … I think we mean in practice economics based on maximization-with-equilibrium. We imagine an economy consisting of rational, self-interested players, and suppose that economic outcomes reflect a situation in which each player is doing the best he, she, or it can given the actions of all the other players …

Some economists really really believe that life is like this — and they have a significant impact on our discourse. But the rest of us are well aware that this is nothing but a metaphor; nonetheless, most of what I and many others do is sorta-kinda neoclassical because it takes the maximization-and-equilibrium world as a starting point or baseline, which is then modified — but not too much — in the direction of realism.

This is, not to put too fine a point on it, very much true of Keynesian economics as practiced … New Keynesian models are intertemporal maximization modified with sticky prices and a few other deviations …

Why do things this way? Simplicity and clarity. In the real world, people are fairly rational and more or less self-interested; the qualifiers are complicated to model, so it makes sense to see what you can learn by dropping them. And dynamics are hard, whereas looking at the presumed end state of a dynamic process — an equilibrium — may tell you much of what you want to know.

Being myself sorta-kinda Keynesian I find this analysis utterly unconvincing. Let me try to elaborate on why.

Macroeconomic models may be an informative tool for research. But if practitioners of “New Keynesian” macroeconomics do not investigate and make an effort of providing a justification for the credibility of the assumptions on which they erect their building, it will not fulfill its tasks. There is a gap between its aspirations and its accomplishments, and without more supportive evidence to substantiate its claims, critics will continue to consider its ultimate argument as a mixture of rather unhelpful metaphors and metaphysics. Maintaining that economics is a science in the “true knowledge” business, I remain a skeptic of the pretences and aspirations of “New Keynesian” macroeconomics. So far, I cannot really see that it has yielded very much in terms of realistic and relevant economic knowledge.

The marginal return on its ever higher technical sophistication in no way makes up for the lack of serious underlabouring of its deeper philosophical and methodological foundations. The rather one-sided emphasis of usefulness and its concomitant instrumentalist justification cannot hide that “New Keynesians” cannot give supportive evidence for their considering it fruitful to analyze macroeconomic structures and events as the aggregated result of optimizing representative actors. After having analyzed some of its ontological and epistemological foundations, I cannot but conclude that “New Keynesian” macroeconomics on the whole has not delivered anything else than “as if” unreal and irrelevant models.

If we are going to be able to show that the mechanisms or causes that we isolate and handle in our microfounded macromodels are stable in the sense that they do not change when we “export” them to our “target systems”, they do only hold under ceteris paribus conditions and are a fortiori of limited value to our understanding, explanations or predictions of real economic systems. Or as the always eminently quotable Keynes wrote in Treatise on Probability(1921):

The kind of fundamental assumption about the character of material laws, on which scientists appear commonly to act, seems to me to be [that] the system of the material universe must consist of bodies … such that each of them exercises its own separate, independent, and invariable effect, a change of the total state being compounded of a number of separate changes each of which is solely due to a separate portion of the preceding state … Yet there might well be quite different laws for wholes of different degrees of complexity, and laws of connection between complexes which could not be stated in terms of laws connecting individual parts … If different wholes were subject to different laws qua wholes and not simply on account of and in proportion to the differences of their parts, knowledge of a part could not lead, it would seem, even to presumptive or probable knowledge as to its association with other parts … These considerations do not show us a way by which we can justify induction … /427 No one supposes that a good induction can be arrived at merely by counting cases. The business of strengthening the argument chiefly consists in determining whether the alleged association is stable, when accompanying conditions are varied … /468 In my judgment, the practical usefulness of those modes of inference … on which the boasted knowledge of modern science depends, can only exist … if the universe of phenomena does in fact present those peculiar characteristics of atomism and limited variety which appears more and more clearly as the ultimate result to which material science is tending.

Science should help us penetrate to “the true process of causation lying behind current events” and disclose “the causal forces behind the apparent facts” [Keynes 1971-89 vol XVII:427]. We should look out for causal relations. But models can never be more than a starting point in that endeavour. There is always the possibility that there are other variables – of vital importance and although perhaps unobservable and non-additive not necessarily epistemologically inaccessible – that were not considered for the model.

This is a more fundamental and radical problem than the celebrated “Lucas critique” have suggested. This is not the question if deep parameters, absent on the macro-level, exist in “tastes” and “technology” on the micro-level. It goes deeper. Real world social systems are not governed by stable causal mechanisms or capacities. It is the criticism that Keynes first launched against the “atomistic fallacy” already in the 1920s:

The atomic hypothesis which has worked so splendidly in Physics breaks down in Psychics. We are faced at every turn with the problems of Organic Unity, of Discreteness, of Discontinuity – the whole is not equal to the sum of the parts, comparisons of quantity fails us, small changes produce large effects, the assumptions of a uniform and homogeneous continuum are not satisfied. Thus the results of Mathematical Psychics turn out to be derivative, not fundamental, indexes, not measurements, first approximations at the best; and fallible indexes, dubious approximations at that, with much doubt added as to what, if anything, they are indexes or approximations of.

The kinds of laws and relations that economics has established, are laws and relations about entities in models that presuppose causal mechanisms being atomistic and additive. When causal mechanisms operate in real world social target systems they only do it in ever-changing and unstable combinations where the whole is more than a mechanical sum of parts. If economic regularities obtain they do it (as a rule) only because we engineered them for that purpose. Outside man-made “nomological machines” they are rare, or even non-existant. Unfortunately that also makes most of the achievements of econometrics – as most of contemporary endeavours of economic theoretical modeling – rather useless.

Keynes basically argued that it was inadmissible to project history on the future. Consequently an economic policy cannot presuppose that what has worked before, will continue to do so in the future. That macroeconomic models could get hold of correlations between different “variables” was not enough. If they could not get at the causal structure that generated the data, they were not really “identified”. Dynamic stochastic general euilibrium (DSGE) macroeconomists – including “New Keynesians” – has drawn the conclusion that the problem with unstable relations is to construct models with clear microfoundations where forward-looking optimizing individuals and robust, deep, behavioural parameters are seen to be stable even to changes in economic policies. This, however, is a dead end.

So here we are getting close to the heart of darkness in “New Keynesian” macroeconomics. Where “New Keynesian” economists think that they can rigorously deduce the aggregate effects of (representative) actors with their reductionist microfoundational methodology, they have to put a blind eye on the emergent properties that characterize all open social systems – including the economic system. The interaction between animal spirits, trust, confidence, institutions etc., cannot be deduced or reduced to a question answerable on the idividual level. Macroeconomic structures and phenomena have to be analyzed also on their own terms. And although one may easily agree with Krugman’s emphasis on simple models, the simplifications used may have to be simplifications adequate for macroeconomics and not those adequate for microeconomics.

“New Keynesian” macromodels describe imaginary worlds using a combination of formal sign systems such as mathematics and ordinary language. The descriptions made are extremely thin and to a large degree disconnected to the specific contexts of the targeted system than one (usually) wants to (partially) represent. This is not by chance. These closed formalistic-mathematical theories and models are constructed for the purpose of being able to deliver purportedly rigorous deductions that may somehow by be exportable to the target system. By analyzing a few causal factors in their “macroeconomic laboratories” they hope they can perform “thought experiments” and observe how these factors operate on their own and without impediments or confounders.

Unfortunately, this is not so. The reason for this is that economic causes never act in a socio-economic vacuum. Causes have to be set in a contextual structure to be able to operate. This structure has to take some form or other, but instead of incorporating structures that are true to the target system, the settings made in these macroeconomic models are rather based on formalistic mathematical tractability. In the models they appear as unrealistic assumptions, usually playing a decisive role in getting the deductive machinery deliver “precise” and “rigorous” results. This, of course, makes exporting to real world target systems problematic, since these models – as part of a deductivist covering-law tradition in economics – are thought to deliver general and far-reaching conclusions that are externally valid. But how can we be sure the lessons learned in these theories and models have external validity, when based on highly specific unrealistic assumptions? As a rule, the more specific and concrete the structures, the less generalizable the results. Admitting that we in principle can move from (partial) falsehoods in theories and models to truth in real world target systems does not take us very far, unless a thorough explication of the relation between theory, model and the real world target system is made. If models assume representative actors, rational expectations, market clearing and equilibrium, and we know that real people and markets cannot be expected to obey these assumptions, the warrants for supposing that conclusions or hypothesis of causally relevant mechanisms or regularities can be bridged, are obviously non-justifiable. To have a deductive warrant for things happening in a closed model is no guarantee for them being preserved when applied to an open real world target system.

In microeconomics we know that aggregation really presupposes homothetic an identical preferences, something that almost never exist in real economies. The results given by these assumptions are therefore not robust and do not capture the underlying mechanisms at work in any real economy. And models that are critically based on particular and odd assumptions – and are neither robust nor congruent to real world economies – are of questionable value.

Even if economies naturally presuppose individuals, it does not follow that we can infer or explain macroeconomic phenomena solely from knowledge of these individuals. Macroeconomics is to a large extent emergent and cannot be reduced to a simple summation of micro-phenomena. Moreover, as we have already argued, even these microfoundations aren’t immutable. The “deep parameters” of “New Keynesian” DSGE models– “tastes” and “technology” – are not really the bedrock of constancy that they believe (pretend) them to be.

So I cannot concur with Krugman – and other sorta-kinda “New Keynesians” – when they try to reduce Keynesian economics to “intertemporal maximization modified with sticky prices and a few other deviations”. As John Quiggin so aptly writes:

If there is one thing that distinguished Keynes’ economic analysis from that of his predecessors, it was his rejection of the idea of a unique full employment equilibrium to which a market economy will automatically return when it experiences a shock. Keynes argued that an economy could shift from a full-employment equilibrium to a persistent slump as the result of the interaction between objective macroeconomic variables and the subjective ‘animal spirits’ of investors and other decision-makers. It is this perspective that has been lost in the absorption of New Keynesian macro into the DSGE framework.

The purported strength of new-classical and new-Keynesian macroeconomics is that they have firm anchorage in preference-based microeconomics, and especially the decisions taken by inter-temporal utility maximizing “forward-loooking” individuals.

To some of us, however, this has come at too high a price. The almost quasi-religious insistence that macroeconomics has to have microfoundations – without ever presenting neither ontological nor epistemological justifications for this claim – has put a blind eye to the weakness of the whole enterprise of trying to depict a complex economy based on an all-embracing representative actor equipped with superhuman knowledge, forecasting abilities and forward-looking rational expectations. It is as if – after having swallowed the sour grapes of the Sonnenschein-Mantel-Debreu-theorem – these economists want to resurrect the omniscient walrasian auctioneer in the form of all-knowing representative actors equipped with rational expectations and assumed to somehow know the true structure of our model of the world (how that could even be conceivable is beyond my imagination, given that the ongoing debate on microfoundations, if anything, shows that not even we, the economists, can come to agreement on a common model).
How could it go so wrong?

Following the greatest economic depression since the 1930s, the grand old man of modern economic growth theory, Nobel laureate Robert Solow, on July 20, 2010, gave a prepared statement on “Building a Science of Economics for the Real World” for a hearing in the U. S. Congress. According to Solow modern macroeconomics has not only failed at solving present economic and financial problems, but is “bound” to fail. Building dynamically stochastic general equilibrium models (DSGE) on “assuming the economy populated by a representative agent” – consisting of “one single combination worker-owner-consumer-everything-else who plans ahead carefully and lives forever” – do not pass “the smell test: does this really make sense?” One cannot but concur in Solow’s surmise that a thoughtful person “faced with the thought that economic policy was being pursued on this basis, might reasonably wonder what planet he or she is on.”

Already in 2008 Solow had – in “The State of Macroeconomics” (Journal of Economic Perspectives 2008:243-249) – told us of what he thought of microfounded modern macroeconomics:

[When modern macroeconomists] speak of macroeconomics as being firmly grounded in economic theory, we know what they mean … They mean a macroeconomics that is deduced from a model in which a single immortal consumer-worker-owner maximizes a perfectly conventional time-additive utility function over an infinite horizon, under perfect foresight or rational expectations, and in an institutional and technological environment that favors universal price-taking behavior …

No one would be driven to accept this story because of its obvious “rightness”. After all, a modern economy is populated by consumers, workers, pensioners, owners, managers, investors, entrepreneurs, bankers, and others, with different and sometimes conflicting desires, information, expectations, capacities, beliefs, and rules of behavior … To ignore all this in principle does not seem to qualify as mere abstraction – that is setting aside inessential details. It seems more like the arbitrary suppression of clues merely because they are inconvenient for cherished preconceptions …

Friends have reminded me that much effort of ‘modern macro’ goes into the incorporation of important deviations from the Panglossian assumptions … [But] a story loses legitimacy and credibility when it is spliced to a simple, extreme, and on the face of it, irrelevant special case. This is the core of my objection: adding some realistic frictions does not make it any more plausible than an observed economy is acting out the desires of a single, consistent, forward-looking intelligence …

It seems to me, therefore, that the claim that ‘modern macro’ somehow has the special virtue of following the principles of economic theory is tendentious and misleading … The other possible defense of modern macro is that, however special it may seem, it is justified empirically. This strikes me as a delusion …

So I am left with a puzzle, or even a challenge. What accounts for the ability of ‘modern macro’ to win hearts and minds among bright and enterprising academic economists? … There has always been a purist streak in economics that wants everything to follow neatly from greed, rationality, and equilibrium, with no ifs, ands, or buts … The theory is neat, learnable, not terribly difficult, but just technical enough to feel like ‘science’. Moreover it is practically guaranteed to give laissez-faire-type advice, which happens to fit nicely with the general turn to the political right that began in the 1970s and may or may not be coming to an end.

In case you’re still not convinced – here’s another masterpiece that essentially says it all:

So how did macroeconomics arrive at its current state?

The original impulse to look for better or more explicit micro foundations was probably reasonable. What emerged was not a good idea. The preferred model has a single representative consumer optimizing over infinite time with perfect foresight or rational expectations, in an environment that realizes the resulting plans more or less flawlessly through perfectly competitive forward-looking markets for goods and labor, and perfectly flexible prices and wages.

How could anyone expect a sensible short-to-medium-run macroeconomics to come out of that set-up? My impression is that this approach (which seems now to be the mainstream, and certainly dominates the journals, if not the workaday world of macroeconomics) has had no empirical success; but that is not the point here. I start from the presumption that we want macroeconomics to account for the occasional aggregative pathologies that beset modern capitalist economies, like recessions, intervals of stagnation, inflation, “stagflation,” not to mention negative pathologies like unusually good times. A model that rules out pathologies by definition is unlikely to help. It is always possible to claim that those “pathologies” are delusions, and the economy is merely adjusting optimally to some exogenous shock. But why should reasonable people accept this? …

What is needed for a better macroeconomics? [S]ome of the gross implausibilities … need to be eliminated. The clearest candidate is the representative agent. Heterogeneity is the essence of a modern economy. In real life we worry about the relations between managers and shareowners, between banks and their borrowers, between workers and employers, between venture capitalists and entrepreneurs, you name it. We worry about those interfaces because they can and do go wrong, with likely macroeconomic consequences. We know for a fact that heterogeneous agents have different and sometimes conflicting goals, different information, different capacities to process it, different expectations, different beliefs about how the economy works. Representative-agent models exclude all this landscape, though it needs to be abstracted and included in macro-models.

I also doubt that universal rational expectations provide a useful framework for macroeconomics …

Now here is a peculiar thing. When I was in advanced middle age, I suddenly woke up to the fact that my colleagues in macroeconomics, the ones I most admired, thought that the fundamental problem of macro theory was to understand how nominal events could have real consequences. This is just a way of stating some puzzle or puzzles about the sources for sticky wages and prices. This struck me as peculiar in two ways.

First of all, when I was even younger, nobody thought this was a puzzle. You only had to look around you to stumble on a hundred different reasons why various prices and factor prices should be much less than perfectly flexible. I once wrote, archly I admit, that the world has its reasons for not being Walrasian. Of course I soon realized that what macroeconomists wanted was a formal account of price stickiness that would fit comfortably into rational, optimizing models. OK, that is a harmless enough activity, especially if it is not taken too seriously. But price and wage stickiness themselves are not a major intellectual puzzle unless you insist on making them one.

Robert Solow, “Dumb and dumber in macroeconomics”

Of course there are alternatives. For those of us who have not forgotten the history of our discipline, and not bought the sweet-water nursery tale of Lucas et consortes that Keynes was not “serious thinking,” we can easily see that there exists a macroeconomic tradition inspired by Keynes (that has absolutely nothing to do with any New Synthesis or “New Keynesianism” to do).

Its ultimate building-block is the perception of genuine uncertainty and that people often “simply do not know.” Real actors can’t know everything and their acts and decisions are not simply possible to sum or aggregate without the economist risking to succumb to “the fallacy of composition”.

Instead of basing macroeconomics on unreal and unwarranted generalizations of microeconomic behaviour and relations, it is far better to accept the ontological fact that the future to a large extent is uncertain, and rather conduct macroeconomics on this fact of reality.

The real macroeconomic challenge is to accept uncertainty and still try to explain why economic transactions take place – instead of simply conjuring the problem away by assuming uncertainty to be reducible to stochastic risk. That is scientific cheating. And it has been going on for too long now.

The sooner we are intellectually honest and ready to admit that the modern macroeconomic microfoundationalist programme has come to way’s end – the sooner we can redirect our macroeconomic aspirations and knowledge in more fruitful endeavours.


  1. an excellent survey of the dysfunctional deformity that is modern macro

  2. Very nice post. On the last part, I sympathize with the view that fundamental uncertainty (à la Knight, Davidson) needs to be taken into account. But does it really present an insurmountable problem? Are there alternative models that you think could be developed for useful policy advice? Again, good post

  3. It seems to me that one big problem is that the math used by economists is about what I learned as a math major in college. In my econ class, it was easy to see the feedback problems in the simplest models we were taught, and I never understood how those were to be managed in equilibrium math. Is anyone trying to figure out how to use fractal math?

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