Simon has a nice post on the contrast between economic models which are theoretically coherent but empirically weak, such as microfounded DSGE models, and empirically stronger but theoretically weak models such as VARs. This poses the question: why do we need both?

To see why, think about American football. A team has four attempts ("downs") to advance ten yards. If it doesn't do so, its opponents gain possession. Many teams therefore often punt the ball downfield on the fourth down, so that they concede possession as far from their goal as possible.

How would an economist model this behaviour?

We could do so in atheoretical statistical terms, by simply regressing the probability of a team punting upon a few variables: distance from goals, yardage needed, quality of running backs and so on. This should yield decent predictions.

But what if the rules were to be changed one season, so a team were allowed six downs before conceding possession? Teams would then be much less likely to punt, and more likely to try to win yardage by running or passing. Our atheoretical model would fail. But a microfounded model based upon teams trying to rationally maximize expected points would probably do better.

My analogy is not original. It's exactly the one Thomas Sargent used (pdf) back in 1980 to argue for what we now call microfounded models. Such models, he said, allow us to better predict the effect of changes in policy:

The systematic behavior of private agents and the random behavior of market outcomes both will change whenever agents' constraints change, as when government policy or other parts of the environment change. To make reliable statements about policy interventions, we need dynamic models and econometric procedures which are consistent with this general presumption.

The question is: how widely applicable is Sargent's metaphor?

I suspect it is in many contexts, not least of which is regulatory behaviour. It implies, for example, that simple regulations requiring banks to hold more capital will lead not necessarily to safer banks but to them shovelling risk into off-balance sheet vehicles.

I'm not so sure, however, about its applicability to macroeconomic policy, in part simply because people have better things to do than pay attention to policy changes. For example, the Thatcher government in 1980 announced targets for monetary growth which, it hoped, would lead to lower inflation expectations and hence to lower actual inflation without a great loss of output. In fact, output slumped, perhaps in part because inflation expectations didn't fall as the government hoped. And more recently, households' inflation expectations have been formed more by a rule of thumb ("inflation will be the same as it has been, adjusted up if it's been low and down if high") than by the inflation target.

This is not to say that the Sargent metaphor (and Lucas critique) are always irrelevant. They might well be useful in analysing big changes to policy. The question is: what counts as big?

My point here is the one Dani Rodrik has made. The right model is a matter of horses for courses. Atheoretical statistical relationships serve us well most of the time. But common sense tells us they will sometimes fail us. Our problem is to know when that "sometimes" is.

This is not to say that the microfounded model must always be one based upon rational utility maximization; it could instead be one in which agents use rules of thumb.

In fact, Sargent's metaphor tells us this. David Romer has shown (pdf) that football teams' behaviour on the fourth down "departs from the behavior that would maximize their chances of winning in a way that is highly systematic, clear-cut, and statistically significant." There's much more to microfoundations than simple ideas of rational maximization.