Macro, science, and falsification

Noah Smith has an interesting post up on the subject. It's one of those posts where on all practical counts I agree with him but I would not agree with a lot of the more abstract points he makes (namely, that macro is not scientific... or to be more fair what he actually said is that it's "barely a science at all"). The reason he says this (and the reason I disagree) is, of course, falsification. I've never been particularly impressed by Popperianism, as readers know. Science is as much about compiling convincing evidence as it is about a strict falsification regime. It's about deriving the best explanations we can about what we observe (which does not require falsification - although of course it never hurts), not about saying something about an ultimate reality. Science accepts that its explanations are imperfect and is happy with broader laws that explain processes well. It doesn't require that we provide an explanation so precise that it can be definitively falsified (although again, that's always nice). Of course someone who worries about being strict on falsification can live in the community of scientists fairly comfortably. Instead of theorizing that "all swans are white", you could always theorize that "a whole lot of swans are white". The latter theory works just fine. "A whole lot of recessions are caused by demand shocks, and this is how we think the ones that are work" is a fine macroeconomic scientific statement too. Most science is about this sort of stuff. Developing good descriptions, from observation, of one process out of many potential processes that may or may not be operating at a particular time. Falsification is never a bad thing when you do this sort of science, but when you realize that in complex systems sometimes you've got lots of processes going on that deserve careful research, the ideal-type of the falsification contest starts to seem a little beside the point of it all.

But like I said, as a practical matter I agree with a lot that Noah has to say. He concludes with: "The solution is for macroeconomists to A) admit their ignorance more often (see this Mankiw article and this Cochrane article for good examples of how to do this), and B) search for better ways to falsify macro theories in a convincing way." Both are very good suggestions, and I'm particularly interested in B.

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Some reasons why people get so down on macro (inappropriately, in my view) is:

1. They have this idealized Popperian physics in their head (is this even how physics works?) of crisp laws describing phenomena and clear cut experiments, and that's just not what defines science nor is it representative of all science. Economics is more like biology: (a.) complex processes all at work at once (so it's not clear there has to be one "right" macroeconomic theory (b.) explanation is more important than prediction, precisely because it is a complex system, and (c.) there's a whole lot of good scientific work to do that is largely descriptive or taxonomical. Physics-envy is not good for economics. We're studying the social behavior of highly evolved apes. Economists are very specialized primatologists. That should give you a hint that you should probably look to biology rather than physics for a template.

2. We expect models to explain a lot more than they were designed to explain. This problem with how people view macroeconomics is well illustrated in a recent post (quoting an older article) by Mark Thoma. He writes: "Macroeconomic models have not fared well in recent years – the models didn’t predict the financial crisis". What a fascinating sentence! Why would anyone expect a macroeconomic model to predict a financial crisis? Who says this is a determinant of whether macroeconomic models have "fared well"? Can I say that household bargaining models haven't "fared well" because they didn't predict the financial crisis? Of course not. That's silly. The financial crisis was not a macroeconomic crisis. It was not driven by macroeconomic processes. The financial crisis was caused by the vagaries of the finance industry. You judge finance models by this, not macroeconomic models. Now, how have macroeconomic models done in predicting how the macroeconomy would respond to the financial crisis? How have macroeconomic models done in explaining the path of output, interest rates, inflation, and employment? Some have certainly done more than others. None predicted a financial crisis because, well, that's what finance guys do. Should we look into macro-finance linkages? Definitely. Definitely, definitely, definitely. But the crisis was caused by the structure of CDOs, by certain risk models, by expectations about house prices, and by the exposure of different financial firms. None of these are really issues that macroeconomists are best placed to speak on.

3. Prediction may be impossible. This kind of expands on point #1. We don't say that seismologists aren't scientists because they can't predict the epicenter and magnitude earthquakes a couple years in advance. We don't say that climatologists aren't scientists because they can't predict right now when and where the hurricanes will hit next hurricane season. We don't say that evolutionary biologists aren't scientists because they can't sketch out the food chain 100 million years from now. And these failures at prediction also don't cause us to abandon the models used by seismologists, climatologists, or evolutionary biologists. Even if you get caught up in this falsification project, falsification doesn't necessarily imply prediction. Prediction of complex systems is very hard. Sometimes we can just explain the behavior of complex systems. Scientists aren't miracle-workers.