Taking rational expectations seriously? You’ve got to be joking!

from Lars Syll

If at some time my skeleton should come to be used by a teacher of osteology to illustrate his lectures, will his students seek to infer my capacities for thinking, feeling, and deciding from a study of my bones? If they do, and any report of their proceedings should reach the Elysian Fields, I shall be much distressed, for they will be using a model which entirely ignores the greater number of relevant variables, and all of the important ones. Yet this is what ‘rational expectations’ does to economics.

G. L. S. Shackle

Since I have already put forward a rather detailed theoretical-methodological critique of the rational expectations hypothesis in Rational expectations – a fallacious foundation for macroeconomics in a non-ergodic world (real-world economics review, issue 62, 2012), I will limit myself here to elaborate on a couple of the rather unwarranted allegations that defenders of rational expectations put forward in their attempts at rescuing the rational expectations hypothesis from the critique. 

In a laboratory experiment run by James Andreoni and Tymofiy Mylovanov, the researchers induced common probability priors, and then told all participants of the actions taken by the others. Their findings are very interesting, and says something rather profound on the value of the rational expectations hypothesis in standard neoclassical economic models:

We look at choices in round 1, when individuals should still maintain common priors, being indifferent about the true state. Nonetheless, we see that about 20% of the sample erroneously disagrees and favors one point of view. Moreover, while other errors tend to diminish as the experiment progresses, the fraction making this type of error is nearly constant. One may interpret disagreement in this case as evidence of erroneous or nonrational choices.

Next, we look at the final round where information about disagreement is made public and, under common knowledge of rationality, should be sufficient to eliminate disagreement. Here we find that individuals weigh their own information more than twice that of the five others in their group. When we look separately at those who err by disagreeing in round 1, we find that these people weigh their own information more than 10 times that of others, putting virtually no stock in public information. This indicates a different type of error, that is, a failure of some individuals to learn from each other. This error is quite large and for a nontrivial minority of the population.

Setting aside the subjects who make systematic errors, we find that individuals still put 50% more weight on their own information than they do on the information revealed through the actions of others, although this difference is not statistically significant.

So in this experiment there seems to be some irrational idiots who don’t understand that they are exactly that — idiots. When told that the earth is flat they still adhere to their own beliefs of a circular earth. It is as if people thought that the probability that all others are idiots — with irrational beliefs –is higher than the probability that the earth is circular.

Now compare these experimental results with rational expectations models, where the world evolves in accordance with fully predetermined models where uncertainty has been reduced to stochastic risk describable by some probabilistic distribution.

The tiny little problem that there is no hard empirical evidence that verifies these models doesn’t usually bother its protagonists too much. Rational expectations überpriest Thomas Sargent has the following to say on the epistemological status of the rational expectations hypothesis (emphasis added):

Partly because it focuses on outcomes and does not pretend to have behavioral content, the hypothesis of rational epectations has proved to be a powerful tool for making precise statements about complicarted dynamic economic systems.

Precise, yes, but relevant and realistic? I’ll be dipped!

And a few years later, when asked in an interview in Macroeconomic Dynamics — in 2005 — if he thought “that differences among people’s models are important aspects of macroeconomic policy debates”, Sargent replied (emphasis added):

The fact is you simply cannot talk about their differences within the typical rational expectations model. There is a communism of models. All agents within the model, the econometricians, and God share the same model.

One might perhaps find it odd to juxtapose God and people, but I think Leonard Rapping – himself a former rational expectationist – was on the right track when in 1984 interviewed by Arjo Klamer — The New Classical Macroeconomics — he said:

Frankly, I do not think that the rational expectations theorists are in the real world. Their approach is much to abstract.

Building models on rational expectations either means we are Gods or Idiots. Most of us know we are neither. So, God may share Sargent’s and Wren-Lewis’s models, but they certainly aren’t my models.

In their attempted rescue operations, rational expectationists try to give the picture that only heterodox economists like yours truly are critical of the rational expectations hypothesis. But, on this, they are, simply, eh, wrong. Let’s listen to Nobel laureate Edmund Phelps — hardly a heterodox economist — and what he has to say (emphasis added):

Question: In a new volume with Roman Frydman, “Rethinking Expectations: The Way Forward for Macroeconomics,” you say the vast majority of macroeconomic models over the last four decades derailed your “microfoundations” approach. Can you explain what that is and how it differs from the approach that became widely accepted by the profession?

Answer: In the expectations-based framework that I put forward around 1968, we didn’t pretend we had a correct and complete understanding of how firms or employees formed expectations about prices or wages elsewhere. We turned to what we thought was a plausible and convenient hypothesis. For example, if the prices of a company’s competitors were last reported to be higher than in the past, it might be supposed that the company will expect their prices to be higher this time, too, but not that much. This is called “adaptive expectations:” You adapt your expectations to new observations but don’t throw out the past. If inflation went up last month, it might be supposed that inflation will again be high but not that high.

Q: So how did adaptive expectations morph into rational expectations?

A: The “scientists” from Chicago and MIT came along to say, we have a well-established theory of how prices and wages work. Before, we used a rule of thumb to explain or predict expectations: Such a rule is picked out of the air. They said, let’s be scientific. In their mind, the scientific way is to suppose price and wage setters form their expectations with every bit as much understanding of markets as the expert economist seeking to model, or predict, their behavior. The rational expectations approach is to suppose that the people in the market form their expectations in the very same way that the economist studying their behavior forms her expectations: on the basis of her theoretical model.

Q: And what’s the consequence of this putsch?

A: Craziness for one thing. You’re not supposed to ask what to do if one economist has one model of the market and another economist a different model. The people in the market cannot follow both economists at the same time. One, if not both, of the economists must be wrong. Another thing: It’s an important feature of capitalist economies that they permit speculation by people who have idiosyncratic views and an important feature of a modern capitalist economy that innovators conceive their new products and methods with little knowledge of whether the new things will be adopted — thus innovations. Speculators and innovators have to roll their own expectations. They can’t ring up the local professor to learn how. The professors should be ringing up the speculators and aspiring innovators. In short, expectations are causal variables in the sense that they are the drivers. They are not effects to be explained in terms of some trumped-up causes.

Q: So rather than live with variability, write a formula in stone!

A: What led to rational expectations was a fear of the uncertainty and, worse, the lack of understanding of how modern economies work. The rational expectationists wanted to bottle all that up and replace it with deterministic models of prices, wages, even share prices, so that the math looked like the math in rocket science. The rocket’s course can be modeled while a living modern economy’s course cannot be modeled to such an extreme. It yields up a formula for expectations that looks scientific because it has all our incomplete and not altogether correct understanding of how economies work inside of it, but it cannot have the incorrect and incomplete understanding of economies that the speculators and would-be innovators have.

Q: One of the issues I have with rational expectations is the assumption that we have perfect information, that there is no cost in acquiring that information. Yet the economics profession, including Federal Reserve policy makers, appears to have been hijacked by Robert Lucas.

A: You’re right that people are grossly uninformed, which is a far cry from what the rational expectations models suppose. Why are they misinformed? I think they don’t pay much attention to the vast information out there because they wouldn’t know what to do what to do with it if they had it. The fundamental fallacy on which rational expectations models are based is that everyone knows how to process the information they receive according to the one and only right theory of the world. The problem is that we don’t have a “right” model that could be certified as such by the National Academy of Sciences. And as long as we operate in a modern economy, there can never be such a model.

Bloomberg

And this is what another non-heterodox economist, Willem Buiter, has to say about the state of the standard macroeconomic theory that builds on the twin assumptions of rational expectations and efficient markets:

In both the New Classical and New Keynesian approaches to monetary theory (and to aggregative macroeconomics in general), the strongest version of the efficient markets hypothesis (EMH) was maintained. This is the hypothesis that asset prices aggregate and fully reflect all relevant fundamental information, and thus provide the proper signals for resource allocation. Even during the seventies, eighties, nineties and noughties before 2007, the manifest failure of the EMH in many key asset markets was obvious to virtually all those whose cognitive abilities had not been warped by a modern Anglo-American Ph.D. education. But most of the profession continued to swallow the EMH hook, line and sinker, although there were influential advocates of reason throughout, including James Tobin, Robert Shiller, George Akerlof, Hyman Minsky, Joseph Stiglitz and behaviourist approaches to finance. The influence of the heterodox approaches from within macroeconomics and from other fields of economics on mainstream macroeconomics – the New Classical and New Keynesian approaches – was, however, strictly limited.

But let’s see how rational expectations fares as an empirical assumption. Empirical efforts at testing the correctnes of the hypothesis has resulted in a series of empirical studies that have more or less concluded that it is not consistent with the facts. In one of the more well-known and highly respected evaluation reviews made, Michael Lovell (1986) concluded:

it seems to me that the weight of empirical evidence is sufficiently strong to compel us to suspend belief in the hypothesis of rational expectations, pending the accumulation of additional empirical evidence.

And this is how Nikolay Gertchev summarizes studies on the empirical correctness of the hypothesis:

More recently, it even has been argued that the very conclusions of dynamic models assuming rational expectations are contrary to reality: “the dynamic implications of many of the specifications that assume rational expectations and optimizing behavior are often seriously at odds with the data” (Estrella and Fuhrer 2002, p. 1013). It is hence clear that if taken as an empirical behavioral assumption, the RE hypothesis is plainly false; if considered only as a theoretical tool, it is unfounded and selfcontradictory.

But how about the large mainstream literature on learning? Let me shortly adress the issue.

The rational expectations hypothesis presupposes – basically for reasons of consistency – that agents have complete knowledge of all of the relevant probability distribution functions. And when trying to incorporate learning in these models – trying to take the heat of some of the criticism launched against it up to date – it is always a very restricted kind of learning that is considered. A learning where truly unanticipated, surprising, new things never take place, but only rather mechanical updatings – increasing the precision of already existing information sets – of existing probability functions.

Nothing really new happens in these ergodic models, where the statistical representation of learning and information is nothing more than a caricature of what takes place in the real world target system. This follows from taking for granted that people’s decisions can be portrayed as based on an existing probability distribution, which by definition implies the knowledge of every possible event (otherwise it is in a strict mathematical-statistically sense not really a probability distribution) that can be thought of taking place.

But in the real world it is – as shown again and again by behavioural and experimental economics – common to mistake a conditional distribution for a probability distribution. Mistakes that are impossible to make in the kinds of economic analysis – built on the rational expectations hypothesis – that Levine is such an adamant propagator for. On average rational expectations agents are always correct. But truly new information will not only reduce the estimation error but actually change the entire estimation and hence possibly the decisions made. To be truly new, information has to be unexpected. If not, it would simply be inferred from the already existing information set.

In rational expectations models new information is typically presented as something only reducing the variance of the parameter estimated. But if new information means truly new information it actually could increase our uncertainty and variance (information set (A, B) => (A, B, C)).

Truly new information give birth to new probabilities, revised plans and decisions – something the rational expectations hypothesis cannot account for with its finite sampling representation of incomplete information.

In the world of rational expectations, learning is like being better and better at reciting the complete works of Shakespeare by heart – or at hitting bull’s eye when playing dart. It presupposes that we have a complete list of the possible states of the world and that by definition mistakes are non-systematic (which, strictly seen, follows from the assumption of “subjective” probability distributions being equal to the “objective” probability distribution). This is a rather uninteresting and trivial kind of learning. It is a closed world learning, synonymous to improving one’s adaptation to a world which is fundamentally unchanging. But in real, open world situations, learning is more often about adapting and trying to cope with genuinely new phenomena.

The rational expectations hypothesis presumes consistent behaviour, where expectations do not display any persistent errors. In the world of rational expectations we are always, on average, hitting the bull’s eye. In the more realistic, open systems view, there is always the possibility (danger) of making mistakes that may turn out to be systematic. It is because of this, presumably, that we put so much emphasis on learning in our modern knowledge societies.

So, where does all this leave us? I think John Kay sums it up pretty well:

Prof Sargent and colleagues appropriated the term “rational expectations” for their answer. Suppose the economic world evolves according to some predetermined model, in which uncertainties are “known unknowns” that can be described by probability distributions. Then economists could gradually deduce the properties of this model, and businesses and individuals would naturally form expectations in that light. If they did not, they would be missing obvious opportunities for advantage.

This approach, which postulates a universal explanation into which economists have privileged insight, was as influential as it was superficially attractive. But a scientific idea is not seminal because it influences the research agenda of PhD students. An important scientific advance yields conclusions that differ from those derived from other theories, and establishes that these divergent conclusions are supported by observation. Yet as Prof Sargent disarmingly observed, “such empirical tests were rejecting too many good models” in the programme he had established with fellow Nobel laureates Bob Lucas and Ed Prescott. In their world, the validity of a theory is demonstrated if, after the event, and often with torturing of data and ad hoc adjustments that are usually called “imperfections”, it can be reconciled with already known facts – “calibrated”. Since almost everything can be “explained” in this way, the theory is indeed universal; no other approach is necessary, or even admissible …

Rational expectations consequently fail for the same reason communism failed – the arrogance and ignorance of the monopolist.