The macroeconomics wars

It is becoming increasingly apparent that if the best defense of your theory of macroeconomics relies on rational actor models, your theory should not be taken seriously. I think that is the relevation (at least that I have had, I think others have already realized this) that will continue to be the most underappreciated within the profession. There is a better chance, I think, that the partisans will be marginalized. But even with folks like Delong, I see an unwillingness to more closely examine their underlying assumptions when it comes to their favorite policies (in Delong’s case that could be free trade, for example)

Charlie,

The very next sentence of Cochrane’s makes it pretty clear, to me at least: “Therefore, we have to examine the “ifs.” And those ifs are, as usual, obviously not true.” He’s saying that the reasoning in the argument is valid, so any dispute regarding its soundness must thereby focus on its premises.

6: Then he should have written something along the lines of ‘this theorem leads those who accept these premises to this conclusion …’. That would steer clear of any accusation that some (desired) conclusion had been smuggled in.

@ John Quiggin,

If you keep reading (the link, anyway), he says things like:
“A monetary argument for fiscal stimulus..”
“if money demand increases dramatically…”
“aggregate demand has fallen.. deflationary pressure…”
Sure, hastily written condensed posts aren’t the best place to conduct discussion. But if you look at any more extended or professional pieces (such as his working paper on fiscal and monetary policy), it’s clear that Cochrane is more sophisticated than you give him credit for.

(It saddens me that phlogiston and the luminiferous aether have become the poster children for pseudoscience, merely because they had excellent and poetic names)

Ooof! Correct link. (“Didja check the links? Didja?”)

I take this as an ethical issue: that in the final reading, the freshwater economists ignored two core ethical practices of science: that theoretical argument must give way to experiment, and that, and that scientific consensus on theory is to be built on the evaluation of experimental work.

This critique doesn’t merely cast doubt on freshwater economics; it rules out the very possibility of any form of social science, as a scientific enterprise. The reason is the world is too complicated, and there is not ever enough data to identify a model.

Let’s pick an example less controversial than economics — in particular, basketball. So, suppose we want a theory of basketball, which we want to use to predict the outcome of NBA games. We’ve got plenty of statistics on each player, so it should be possible, right? Suppose we make the not-unrealistic assumption that how well a player plays depends not just on his statistics, but also upon his interactions with his teammates and the specific behavior of the opposing team. Suddenly you have hugely more variables in your model than the number of players, and the amount of data you need to identify a model with good statistical confidence blows up — you will need more data than any player will ever generate in his whole career.

So we have no choice but to make use of qualitative insight to prune the space of models, in order to make it small enough that statistics can tell you anything useful. You cannot commit to building only on experimental data because there can never possibly be enough.

You can see the two faces of this problem in macroeconomics. The real business cycle theorists tried to build up models from microfoundations, and ended up with models with so many tunable parameters that they lacked the data to rule out anything out. This meant that their models couldn’t rule out the possibility that the Great Depression happened because scientific knowledge magically fell by a third. Qualitatively, this is absurd, but we don’t have enough quantitative data to rule it out statistically. The Keynesians are no better shape, though: they have no single story, but rather have a family of models all of which contradict each other. So they can design models to take advantage of qualitative insights (e.g., there was no mass outbreak of laziness in 1929), but there’s no way to falsify their general approach, because you can always add another tweak.

@12: They weren’t pseudoscience when they were science. But adhering to them after the evidence refuted them very much is.

@16: The problem is not whether you prune by qualitative insight, but whether you *refuse* to prune empirically, or accept empirical pruning as epistemologically superior to qualitative meditations. If the data underdetermine the models, speculation is fine. But once more data come in and refute some part of the previously-acceptable model space, you have to move out of that part or you’re no longer doing science.

@15: Public choice theory is *also* based on modeling individual actors as rational interest-maximizers. You can’t use it as an instrument to save freshwater econ when it’s the fruit of the same poisonous seed. And even if government and corporations are both flawed tools, corporations are *explicitly plutocratic* so the reasons to prefer the former as a starting point are obvious and strong. (Unless you are very rich.)

It saddens me that phlogiston and the luminiferous aether have become the poster children for pseudoscience merely because they had excellent and poetic names)

It saddens me because they weren’t “pseudoscience”. Both were products of modern empiricist scientific culture—they were as scientific as anything can be. Both don’t exist and were central to subsequently disproven theories, but that’s not the same thing as “pseudoscience”. Phrenology is a better candidate as the poster child for pseudoscience.

As a student of the history and philosophy of science, one of my biggest annoyances is this cultural tendency, in both scientific and popular culture, to by default accord contemporary scientific theories rigor and rationality and all past, disproven theories slipshod irrationality. When you study historical science and natural philosophy closely—not as a narrative gloss, but the actual intellectual and empirical work—pretty much all these ideas which seem to us now as outlandish and even absurd were perfectly reasonable in context, given what was known at the time. It’s only a relatively few dominant theories/assertions, such as some of Aristotle’s errors in anatomy (and subsequently repeated in Galen), which are egregious even within their proper context. People typically ridicule Ptolemaic astronomy on the basis that heliocentricism is a self-evidently more elegant and explanatory theory—most don’t realize that many geocentricists up to and including Ptolemy were well aware of this but quite reasonably couldn’t accept the unthinkably vast size of the cosmos indicated by the lack of observable parallax in the “fixed stars”.

I agree that economics as a scientific discipline has a great many problems, some of them systemic; but this trend of all and sundry smugly declaring it a “pseudoscience” is irritating because the vast majority couldn’t reliably differentiate a science from a pseudoscience if their lives depended upon it.

All the social sciences have problems, and they’re basically the same problems. The subject matter is almost impenetrably complex. These sciences are young. Because of both these things, these sciences are easily dominated by politically ideologically-driven ideas. They tend to have so-called “physics envy” and embrace a well-intended but simplistic need to formalize with mathematics and highly abstracted theory.

Economics has these illnesses far more severely than most (or all) and it certainly needs a renewed emphasis on empiricism. But it’s not wrong to have mathematically formalized and to have searched for powerful abstract theory. It’s just gone too far, too soon with this. And it needs to find a way to insulate itself from political trends—but that’s true of all the social sciences and is particularly difficult in the case of economics.

‘All the social sciences have problems, and they’re basically the same problems. The subject matter is almost impenetrably complex. These sciences are young. Because of both these things, these sciences are easily dominated by politically ideologically-driven ideas. They tend to have so-called “physics envy” and embrace a well-intended but simplistic need to formalize with mathematics and highly abstracted theory.’

This is true, but it is false that the problems can be derived from the fact that these sciences are ‘young’. Philosophy, as is well known, goes back to the Ancient Greeks, who also discussed psychology and political science. Neuroscience goes back to the Edwin Smith Papyrus . Sociology began with Ibn Khaldun (or, arguably, with Plato). Medicine of course has its roots in Ancient Babylon (actually, almost all cultures have some form of medicine). Economics goes back to Ancient Egypt. Experimental psychology goes back to the Islamic Golden Age. .

While it is true that these early predecessors of today’s social sciences were ‘primitive’ by our standards precisely the same can be said about the state of the ‘hard’ sciences at the time. Indeed, I would argue that the state of political science and philosophy in the 14th century was considerably more advanced than that of, say, astrophysics.

So what happened?

I think the answer to the question what went wrong with the social sciences is precisely the same answer to the question, what went right with the hard sciences? In both cases the answer is the same: the ‘Scientific Revolution’. Here I think the definitive statement of the problem is by Alan Costall:

‘Descartes set up, almost single-handedly, a whole range of dualisms that continue to trouble the human sciences: the physical vs. mental, body vs. mind, animal vs. human, self vs. other, mechanical vs. rational, passive vs. active, natural vs. normative-to mention just a few. Certainly, Descartes had been impressed by the scope of the new scheme of physical science and its remarkable extension to the distant heavens, on the one hand, and to the intimacy of our own bodies on the other. (He had been particularly struck by William Harvey’s account of the circulatory system.) As Descartes saw it, the new physics was nothing less than a comprehensive science of nature. Consequently, anything failing to figure within that science must exist beyond the realm of the natural (Wilson, 1980, pp. 41-42).

But, of course, Descartes was not acting alone. Galileo and Kepler, among many others, also seem to have engaged in a similar “ontological fix” to save the universal claims for the new physics (Burtt, 1967; Whitehead, 1926; Young, 1966; cf. Chapman, 1966): the new science explains everything-and everything it fails to explain is not really real.5

Within this scheme, psychology’s own subject-the “subject”- became radically subjectivized as that which eludes science. As Alexandre Koyr put it:

[Modern science] broke down the barriers that separated the heavens and the earth. . .[But] it did this by substituting for our world of quality and sense perception, the world in which we live, and love, and die, another world-the world of quantity, of reified geometry, a world in which, though there is a place for everything, there is no place for man. (1965, p. 24)

This is not just a matter of rather distant history. One can still find physicists who insist that their task is “to build a world which is foreign to consciousness and in which consciousness is obliterated” (G. Bergman, cited in Rosen, 2000, p. 82), but we need to think through the implications of this exclusion of us. First, it is not clear how there could ever be any kind of science because, after all, scientists are people, and science is a human enterprise. If scientists, at least, do not belong to the natural order of things, how is science getting done!

The dualistic scheme of traditional science also “set up” psychology to be a rather strange kind of enterprise, the science of the “unscientific”-the science of that which eludes science. When physical science had promoted its methodology (of atomism, mechanism, and quantification) to an exclusive ontology, psychology (so conceived) was a pretty obvious mistake just waiting to happen- an essentially derivative science modeled on physics, yet having as its subject the very realm that physics rendered utterly obscure.’

One might substitute ‘economics’, ‘sociology’ or any of the other ‘human sciences’ for ‘psychology’. The problem is, therefore, that the way the sciences are set up in the ‘West’ a scientific psychology, or sociology, or economics, is simply a contradiction in terms: a scientific study of that which can’t be studied scientifically. To make the social sciences genuinely scientific will mean not only destroying ‘scientism’ and ‘physics envy’ but also undoing the negative effects of the Scientific Revolution, which will have an impact on the practice of physics and chemistry too.

http://www.redorbit.com/news/science/91657/from_darwin_to_watson_and_cognitivism_and_back_again_the/

“… both economists have set up and run …”

should be

“… both economists and psychologists have set up and run… “

I think it’s worth noting that Barro concludes his 1974 paper with this construction:

This paper has focused on the question of whether an increase in government debt constitutes an increase in perceived household wealth. The effect of finite lives was examined within the context of an overlapping generations model of the economy. It was shown that households would act as though they were infinitely lived, and, hence, that there would be no marginal net-wealth effect of government bonds, so long as there existed an operative chain of intergenerational transfers which connected current to future generations.

Surely it would have been more circumspect either to say:

It was shown that households in an overlapping generations model would act as though …

or:

An observation of real households acting as though they were infinitely long lived would be consistent with the overlapping generations model presented here …

It’s possible that I missed something crucial in my brief (and not very well informed) reading (I’m fairly confident, though, that the ‘utility functions’ which appear on p. 1100 and following are not derived from any observation of the author). It’s also possible that Barro’s intended audience would (a) never confuse a description of a model with an empirical claim, and (b) possess perfect immunity from any normative effect of his conclusion. Personally, I can’t help thinking that this sort of product seems well suited for the shelves of your typical ‘family office’.

I agree with Raven that this is an ethical issue and I am surprised that nobody has connected Krugman’s demand that the saline economists repent with Rowan Williams’s alarm that there is no sign that anyone has learnt anything from the crash, least of all Keynes’s emphasis on uncertainty. Some humility and more ethics might be essential to stop an immediately repeat of the dismal cycle (I say more about this here).

Again, this is true of all science. It is why parsimony is invoked. One thing that I have noticed on economics blogs is how economists do not seem to invoke parsimony. Instead, this is the kind of thing I hear.

That’s because they know what they are talking about. If you listen to sociologists, you will hear the same thing, only more loudly and larded with rude comments about the folly of economists who are too quick to ignore important effects.

Parsimony is not a magic wand. It is a claim about the structure of the causal relationships within a system, which can be either true or false, and must be justified with evidence and argument. It happens that many (but by no means all) physical systems have the property that you can rule out most possible causes, and be left with a model with few enough parameters that you can actually identify a model with a reasonable amount of data.

However, the principle of parsimony does not get rid of enough causes in most social systems. Returning to the example of basketball, we can justifiably appeal to parsimony to rule out considering hemlines or football scores as relevant variables in a theory of basketball. However, we cannot appeal to it to rule out considering the effects of a player’s teammates on the quality of his or her play. Even if you limit yourself to considering one-on-one interactions, the number of interactions grows quadratically, and you quickly end up with too many parameters for the available data to identify a model.

Basically, theory in social science is under-determined by the data, and there is no prospect of that fact ever changing. This means that social scientists need lots and lots of yellow books to make even modest claims — you need fancy math to wring conclusions out of weak assumptions.

Most economists are in serious need of some sociology courses. …

You are perhaps too optimistic on this point. The hottest area in sociology these days is social network analysis, and random graph models of social networks make the rational choice framework look realistic and psychologically insightful.

(I don’t want to rag on either economics or sociology, actually. In both of these cases, facts were thrown out to get something mathematically tractable, and that’s actually a good thing. Math lets you tell a story with the confidence that the story doesn’t have any plot holes in it. But: it remains a narrative.)

Moi: “Again, this is true of all science. It is why parsimony is invoked. One thing that I have noticed on economics blogs is how economists do not seem to invoke parsimony. Instead, this is the kind of thing I hear.

Neel Krishnaswaim: “So they can design models to take advantage of qualitative insights (e.g., there was no mass outbreak of laziness in 1929), but there’s no way to falsify their general approach, because you can always add another tweak.”

—-

Neel Krishnaswami: “That’s because they know what they are talking about. If you listen to sociologists, you will hear the same thing, only more loudly and larded with rude comments about the folly of economists who are too quick to ignore important effects.”

Do you mean that they know that everybody is tweaking their models? Or that they know that, without appeal to general criteria such as parsimony, virtually any model can be tweaked ad infinitum, and therefore not falsified? The general tenor of what I read is along these lines: You can always tweak the models, so no model can be falsified, so what can we do? I do not read this: You can always tweak the models, so no model can be falsified, so let’s get some discipline.

Neel Krishnaswami: “Parsimony is not a magic wand. It is a claim about the structure of the causal relationships within a system, which can be either true or false, and must be justified with evidence and argument.”

Parsimony is not a claim, it’s a criterion. There is more than one kind of parsimony. Occam’s razor minimizes entities, which I suppose will also cover variables. Minimal algorithmic complexity and minimum description length are modern forms of parsimony. Axioms and postulates can also be minimized.

Now, why parsimony is a criterion is not so easy to say. Is it simply esthetic? More parsimonious explanations are more beautiful. Is it explanatory power? More parsimonious explanations explain more with less. Is it probability? More parsimonious explanations are more probable, a priori.

Something that is related to parsimony is, I think, the sense that tweaking is like cheating. It is as though you are debating with someone and you raise objections to their claims, and they modify or take back or redefine something they said to meet each objection that you raise. You can never pin them down. If falsifiability means anything, it means making clear and definite claims that pin you down. There is an austere discipline to science that I miss in these discussions of tweaking.

Like astronomy, macroeconomics is not a laboratory science. One recent lament that I have seen is that, well, once competing models have been tweaked to cover our current situation, we will have to wait X years to test them. (When, of course, they will be tweaked again. ;)) There is an important anti-tweaking lesson from the field of machine learning: Never tweak your model so that it predicts everything, because you need to avoid overfitting the model to the data, and you need to have a good measure of the error of the model. Tweak the competing models on most of the data and test them against the rest.

Neel Krisnaswami: “Basically, theory in social science is under-determined by the data, and there is no prospect of that fact ever changing. This means that social scientists need lots and lots of yellow books to make even modest claims—you need fancy math to wring conclusions out of weak assumptions.”

That is another problem. Math lets you build magnificent edifices, but to what avail if they rest upon sand?

All scientific theory is underdetermined by the data. Does economics suffer from tweakism?

Billikin : “Parsimony is not a claim, it’s a criterion.”

I would argue that, proceeding from the sort of data you have access to in the social sciences to the sort of hypotheses you end up forming, parsimony is implicitly a claim (and I may be mistaken, but this may also be what Neel is arguing). But why proceed from the data to these parsimonious hypotheses?

In the natural sciences, the answer seems clear : falsifiability. Scientific theories are falsifiable. When contradictions to a scientific theory are seen, the theory is dropped (examples are many – luminous ether, phlogiston, et cetera. Yes, phrenology is a better strawman candidate (in some respects) because the rejection of the theory has not proceeded from falsification, as I argue below). Here, I distinguish the natural sciences from the social sciences as well as the formal sciences (mathematics, logic, some areas of computer science).

In the social sciences, the answer is less clear. This is Milton Friedman’s argument in The Methodology of Positive Economics : if you are going to start dropping social science theories because their assumptions have been falsified, pretty soon you are going to have no theories left at all. (By quoting Friedman, I don’t nescessarily agree with everything that Friedman has said in the paper). So Friedman argues that assumptions in the social sciences cannot be questioned. IIRC, he uses the example of a ball falling through a vacuum to illustrate the impossibility of ever obtaining the conditions nescessary to verify a theory (which was a bad example, in my opinion). To me, this is a key difference in methodology between the social sciences and the basic sciences. Assumptions in the social sciences can be unverifiable and even blatantly false.

I am less sure than Neel is that mathematics is a narrative (and so, I suppose, I disagree with John Quiggin as well. Take problems in engineering or operations management, for example.) Mathematical and computational solutions to problems are “correct solutions” in that they are the best possible solution for the initial conditions in the models and/or the values of the parameters specified. Further, mathematics is the only tool available to keep track of simultaneous and multifacted relationships among entities. Argumentation (and formal logic) cannot be used to describe these relationships completely nor can they be used to solve the problem at hand. This is why engineering models are also “correct solutions” in many respects. They are the correct solution to the problem at hand given initial conditions in the model and/or the values of the parameters specified. Just because there are some problems that cannot be solved using mathematics does not mean that there are no problems that can be solved using mathematics.

Seems to me you’re disagreeing over terminology, not concept. Those initial conditions, and boundary values of the parameters specified, constitute the “narrative” of which they spoke.

I think so too. I would phrase it as mathematics being part of a narrative since it is the narrative that supplies the initial conditions and boundary values for parameters. I think the process of theoretical economics research goes something like this : you specify a model (e.g. for a particular situation), show that you make as few assumptions as possible, and then show that it is helpful in explaining something (say. that situation). This is not that different from the process in the natural sciences, except that there tend to be a few more assumptions in social science theories. Questioning the assumptions could lead to a better understanding of the limitations of the theories, but in the end, there is still the question of whether this is simply the best thing we have that works. Or in other words, we don’t know which of those assumptions we should drop. Rationality in favor of predictable irrationality? Consistency of preferences in favor of possible racial biases in people?

@Keith Ellis

“As a student of the history and philosophy of science, one of my biggest annoyances is this cultural tendency, in both scientific and popular culture, to by default accord contemporary scientific theories rigor and rationality and all past, disproven theories slipshod irrationality.”

I would have thought that Kuhn drove a stake through the heart of this tendency.