From Uneasy Money:
Paul Romer has been engaged for some time in a worthy campaign against the travesty of modern macroeconomics. A little over a year ago I commented favorablyabout Romer’s takedown of Robert Lucas, but I also defended George Stigleragainst what I thought was an unfair attempt by Romer to identify George Stigler as an inspiration and role model for Lucas’s transgressions. Now just a week ago, a paper based on Romer’s Commons Memorial Lecture to the Omicron Delta Epsilon Society, has become just about the hottest item in the econ-blogosophere, even drawing the attention of Daniel Drezner in the Washington Post.
I have already written critically about modern macroeconomics in my five years of blogging, and here are some links to previous posts (link, link, link, link). It’s good to see that Romer is continuing to voice his criticisms, and that they are gaining a lot of attention. But the macroeconomic hierarchy is used to criticism, and has its standard responses to criticism, which are being dutifully deployed by defenders of the powers that be.
Romer’s most effective rhetorical strategy is to point out that the RBC core of modern DSGE models posit unobservable taste and technology shocks to account for fluctuations in the economic time series, but that these taste and technology shocks are themselves simply inferred from the fluctuations in the times-series data, so that the entire structure of modern macroeconometrics is little more than an elaborate and sophisticated exercise in question-begging.
In this post, I just want to highlight one of the favorite catch-phrases of modern macroeconomics which serves as a kind of default excuse and self-justification for the rampant empirical failures of modern macroeconomics (documented by Lipsey and Carlaw as I showed in this post). When confronted by evidence that the predictions of their models are wrong, the standard and almost comically self-confident response of the modern macroeconomists is: All models are false. By which the modern macroeconomists apparently mean something like: “And if they are all false anyway, you can’t hold us accountable, because any model can be proven wrong. What really matters is that our models, being microfounded, are not subject to the Lucas Critique, and since all other models than ours are not micro-founded, and, therefore, being subject to the Lucas Critique, they are simply unworthy of consideration. This is what I have called methodological arrogance. That response is simply not true, because the Lucas Critique applies even to micro-founded models, those models being strictly valid only in equilibrium settings and being unable to predict the adjustment of economies in the transition between equilibrium states. All models are subject to the Lucas Critique.
Here is Romer’s take:
In response to the observation that the shocks are imaginary, a standard defense invokes Milton Friedman’s (1953) methodological assertion from unnamed authority that “the more significant the theory, the more unrealistic the assumptions (p.14).” More recently, “all models are false” seems to have become the universal hand-wave for dismissing any fact that does not conform to the model that is the current favorite.
Friedman’s methodological assertion would have been correct had Friedman substituted “simple” for “unrealistic.” Sometimes simplifications are unrealistic, but they don’t have to be. A simplification is a generalization of something complicated. By simplifying, we can transform a problem that had been too complex to handle into a problem more easily analyzed. But such simplifications aren’t necessarily unrealistic. To say that all models are false is simply a dodge to avoid having to account for failure. The excuse of course is that all those other models are subject to the Lucas Critique, so my model wins. But your model is subject to the Lucas Critique even though you claim it’s not, so even according to the rules you have arbitrarily laid down, you don’t win.
So I was just curious about where the little phrase “all models are false” came from. I was expecting that Karl Popper might have said it, in which case to use the phrase as a defense mechanism against empirical refutation would have been a particularly fraudulent tactic, because it would have been a perversion of Popper’s methodological stance, which was to force our theoretical constructs to face up to, not to insulate it from, empirical testing. But when I googled “all theories are false” what I found was not Popper, but the British statistician, G. E. P. Box who wrote in his paper “Science and Statistics” based on his R. A. Fisher Memorial Lecture to the American Statistical Association: “All models are wrong.” Here’s the exact quote:
Since all models are wrong the scientist cannot obtain a “correct” one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity.
Since all models are wrong the scientist must be alert to what is importantly wrong. It is inappropriate to be concerned about mice when there are tigers abroad. Pure mathematics is concerned with propositions like “given that A is true, does B necessarily follow?” Since the statement is a conditional one, it has nothing whatsoever to do with the truth of A nor of the consequences B in relation to real life. The pure mathematician, acting in that capacity, need not, and perhaps should not, have any contact with practical matters at all.
In applying mathematics to subjects such as physics or statistics we make tentative assumptions about the real world which we know are false but which we believe may be useful nonetheless. The physicist knows that particles have mass and yet certain results, approximating what really happens, may be derived from the assumption that they do not. Equally, the statistician knows, for example, that in nature there never was a normal distribution, there never was a straight line, yet with normal and linear assumptions, known to be false, he can often derive results which match, to a useful approximation, those found in the real world. It follows that, although rigorous derivation of logical consequences is of great importance to statistics, such derivations are necessarily encapsulated in the knowledge that premise, and hence consequence, do not describe natural truth.
It follows that we cannot know that any statistical technique we develop is useful unless we use it. Major advances in science and in the science of statistics in particular, usually occur, therefore, as the result of the theory-practice iteration.
One of the most annoying conceits of modern macroeconomists is the constant self-congratulatory references to themselves as scientists because of their ostentatious use of axiomatic reasoning, formal proofs, and higher mathematical techniques. The tiresome self-congratulation might get toned down ever so slightly if they bothered to read and take to heart Box’s lecture.