Empirically, far from isolating a microeconomic core, real-business-cycle models, as with other representative-agent models, use macroeconomic aggregates for their testing and estimation. Thus, to the degree that such models are successful in explaining empirical phenomena, they point to the ontological centrality of macroeconomic and not to microeconomic entities … At the empirical level, even the new classical representative-agent models are fundamentally macroeconomic in content …
The nature of microeconomics and macroeconomics — as they are currently practiced — undermines the prospects for a reduction of macroeconomics to microeconomics. Both microeconomics and macroeconomics must refer to irreducible macroeconomic entities.
Kevin Hoover has been writing on microfoundations for now more than 25 years, and is beyond any doubts the one economist/econometrician/methodologist who has thought most on the issue. It’s always interesting to compare his qualified and methodologically founded assessment on the representative-agent-rational-expectations microfoundationalist program with the more or less apologetic views of freshwater economists like Robert Lucas:
Given what we know about representative-agent models, there is not the slightest reason for us to think that the conditions under which they should work are fulfilled. The claim that representative-agent models provide microfundations succeeds only when we steadfastly avoid the fact that representative-agent models are just as aggregative as old-fashioned Keynesian macroeconometric models. They do not solve the problem of aggregation; rather they assume that it can be ignored. While they appear to use the mathematics of microeconomis, the subjects to which they apply that microeconomics are aggregates that do not belong to any agent. There is no agent who maximizes a utility function that represents the whole economy subject to a budget constraint that takes GDP as its limiting quantity. This is the simulacrum of microeconomics, not the genuine article …
[W]e should conclude that what happens to the microeconomy is relevant to the macroeconomy but that macroeconomics has its own modes of analysis … [I]t is almost certain that macroeconomics cannot be euthanized or eliminated. It shall remain necessary for the serious economist to switch back and forth between microeconomics and a relatively autonomous macroeconomics depending upon the problem in hand.
Instead of just methodologically sleepwalking into their models, modern followers of the Lucasian microfoundational program ought to do some reflection and at least try to come up with a sound methodological justification for their position. Just looking the other way won’t do. Writes Hoover:
The representative-agent program elevates the claims of microeconomics in some version or other to the utmost importance, while at the same time not acknowledging that the very microeconomic theory it privileges undermines, in the guise of the Sonnenschein-Debreu-Mantel theorem, the likelihood that the utility function of the representative agent will be any direct analogue of a plausible utility function for an individual agent … The new classicals treat [the difficulties posed by aggregation] as a non-issue, showing no apprciation of the theoretical work on aggregation and apparently unaware that earlier uses of the representative-agent model had achieved consistency wiyh theory only at the price of empirical relevance.
Where ‘New Keynesian’ and New Classical economists think that they can rigorously deduce the aggregate effects of (representative) actors with their reductionist microfoundational methodology, they — as argued in my On the use and misuse of theories and models in economics — have to put a blind eye on the emergent properties that characterize all open social and economic systems. The interaction between animal spirits, trust, confidence, institutions, etc., cannot be deduced or reduced to a question answerable on the individual level. Macroeconomic structures and phenomena have to be analyzed also on their own terms.
I have spent a considerable part of my life building economic models, and examining the models that other economists have built. I believe that I am making reasonably good use of my talents in an attempt to understand the social world.I have no fellow-feeling with those economic theorists who, off the record at seminars and conferences, admit that they are only playing a game with other theorists. If their models are not intended seriously, I want to say (and do say when I feel sufficiently combative), why do they expect me to spend my time listening to their expositions? Count me out of the game.
Those who want to build macroeconomics on microfoundations usually maintain that the only robust policies and institutions are those based on rational expectations and representative actors. As yours truly has tried to show in On the use and misuse of theories and models in economics there is really no support for this conviction at all. On the contrary. If we want to have anything of interest to say on real economies, financial crisis and the decisions and choices real people make, it is high time to place macroeconomic models building on representative actors and rational expectations-microfoundations where they belong – in the dustbin.
For if this microfounded macroeconomics has nothing to say about the real world and the economic problems out there, why should we care about it? The final court of appeal for macroeconomic models is the real world, and as long as no convincing justification is put forward for how the inferential bridging de facto is made, macroeconomic modelbuilding is little more than hand waving that give us rather little warrant for making inductive inferences from models to real world target systems. If substantive questions about the real world are being posed, it is the formalistic-mathematical representations utilized to analyze them that have to match reality, not the other way around.
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 rational expectations and treating uncertainty as if it was possible to reduce it to stochastic risk. That is scientific cheating. And it has been going on for too long now. If that’s the kind of game you want to play — count me out!
Unlimited tolerance must lead to the disappearance of tolerance. If we extend unlimited tolerance even to those who are intolerant, if we are not prepared to defend a tolerant society against the onslaught of the intolerant, then the tolerant will be destroyed, and tolerance with them … We should therefore claim, in the name of tolerance, the right not to tolerate the intolerant.
Karl Popper The Open Society and Its Enemies (1945)
Though I speak with the tongues of angels,
If I have not love…
My words would resound with but a tinkling cymbal.
And though I have the gift of prophesy…
And understand all mysteries…
and all knowledge…
And though I have all faith
So that I could remove mountains,
If I have not love…
I am nothing.
The verdict of history will be harsh.
Stylized facts are close kin of ceteris paribus laws. They are ‘broad generalizations true in essence, though perhaps not in detail’. They play a major role in economics, constituting explananda that economic models are required to explain. Models of economic growth, for example, are supposed to explain the (stylized) fact that the profit rate is constant. The unvarnished fact of course is that profit rates are not constant. All sorts of non-economic factors — e.g., war, pestilence, drought, political chicanery — interfere. Manifestly, stylized facts are not (what philosophers would call) facts, for the simple reason that they do not actually obtain. It might seem then that economics takes itself to be required to explain why known falsehoods are true. (Voodoo economics, indeed!) This can’t be correct. Rather, economics is committed to the view that the claims it recognizes as stylized facts are in the right neighborhood, and that their being in the right neighborhood is something economic models should account for. The models may show them to be good approximations in all cases, or where deviations from the economically ideal are small, or where economic factors dominate non-economic ones. Or they might afford some other account of their often being nearly right. The models may diverge as to what is actually true, or as to where, to what degree, and why the stylized facts are as good as they are. But to fail to acknowledge the stylized facts would be to lose valuable economic information (for example, the fact that if we control for the effects of such non-economic interference as war, disease, and the president for life absconding with the national treasury, the profit rate is constant.) Stylized facts figure in other social sciences as well. I suspect that under a less alarming description, they occur in the natural sciences too. The standard characterization of the pendulum, for example, strikes me as a stylized fact of physics. The motion of the pendulum which physics is supposed to explain is a motion that no actual pendulum exhibits. What such cases point to is this: The fact that a strictly false description is in the right neighborhood sometimes advances understanding of a domain.
Catherine Elgin thinks we should accept model claims when we consider them to be ‘true enough,’ and Uskali Mäki has argued in a similar vain, maintaining that it could be warranted — based on diverse pragmatic considerations — to accept model claims that are negligibly false.
When criticizing the basic (DSGE) workhorse model for its inability to explain involuntary unemployment, its defenders maintain that later elaborations — especially newer search models — manage to do just that. However, one of the more conspicuous problems with those “solutions,” is that they — as e.g. Pissarides’ ”Loss of Skill during Unemployment and the Persistence of Unemployment Shocks” QJE (1992) — are as a rule constructed without seriously trying to warrant that the model immanent assumptions and results are applicable in the real world. External validity is more or less a non-existent problematique sacrificed on the altar of model derivations. This is not by chance. For how could one even imagine to empirically test assumptions such as Pissarides’ ”model 1″ assumptions of reality being adequately represented by ”two overlapping generations of fixed size”, ”wages determined by Nash bargaining”, ”actors maximizing expected utility”,”endogenous job openings”, ”jobmatching describable by a probability distribution,” without coming to the conclusion that this is — in terms of realism and relevance — far from ‘negligibly false’ or ‘true enough’?
Suck on that — and tell me if those typical mainstream — neoclassical — modeling assumptions in any possibly relevant way — with or without due pragmatic considerations — can be considered anything else but imagined model worlds assumptions that has nothing at all to do with the real world we happen to live in!
In econometrics one often gets the feeling that many of its practitioners think of it as a kind of automatic inferential machine: input data and out comes casual knowledge. This is like pulling a rabbit from a hat. Great — but first you have to put the rabbit in the hat. And this is where assumptions come in to the picture.
As social scientists — and economists — we have to confront the all-important question of how to handle uncertainty and randomness. Should we equate randomness with probability? If we do, we have to accept that to speak of randomness we also have to presuppose the existence of nomological probability machines, since probabilities cannot be spoken of – and actually, to be strict, do not at all exist – without specifying such system-contexts.
Accepting a domain of probability theory and a sample space of “infinite populations” — which is legion in modern econometrics — also implies that judgments are made on the basis of observations that are actually never made! Infinitely repeated trials or samplings never take place in the real world. So that cannot be a sound inductive basis for a science with aspirations of explaining real-world socio-economic processes, structures or events. It’s not tenable.
In his book Statistical Models and Causal Inference: A Dialogue with the Social Sciences David Freedman touches on this fundamental problem, arising when you try to apply statistical models outside overly simple nomological machines like coin tossing and roulette wheels:
Lurking behind the typical regression model will be found a host of such assumptions; without them, legitimate inferences cannot be drawn from the model. There are statistical procedures for testing some of these assumptions. However, the tests often lack the power to detect substantial failures. Furthermore, model testing may become circular; breakdowns in assumptions are detected, and the model is redefined to accommodate. In short, hiding the problems can become a major goal of model building.
Using models to make predictions of the future, or the results of interventions, would be a valuable corrective. Testing the model on a variety of data sets – rather than fitting refinements over and over again to the same data set – might be a good second-best … Built into the equation is a model for non-discriminatory behavior: the coefficient d vanishes. If the company discriminates, that part of the model cannot be validated at all.
Regression models are widely used by social scientists to make causal inferences; such models are now almost a routine way of demonstrating counterfactuals. However, the “demonstrations” generally turn out to depend on a series of untested, even unarticulated, technical assumptions. Under the circumstances, reliance on model outputs may be quite unjustified. Making the ideas of validation somewhat more precise is a serious problem in the philosophy of science. That models should correspond to reality is, after all, a useful but not totally straightforward idea – with some history to it. Developing appropriate models is a serious problem in statistics; testing the connection to the phenomena is even more serious …
In our days, serious arguments have been made from data. Beautiful, delicate theorems have been proved, although the connection with data analysis often remains to be established. And an enormous amount of fiction has been produced, masquerading as rigorous science.
Making outlandish statistical assumptions does not provide a solid ground for doing relevant social science.