Does it “Take a Model to Beat a Model”?

Imagine being a chemist in the Middle Ages. Your colleagues are all working on fanciful tasks like trying to develop a philosopher’s stone to grant immortality or turning lead into gold. You continually point out to them that all of their attempts have pretty much failed completely. In fact, based on your own research, you have strong reason to believe that the path they are taking is going completely the wrong direction. They aren’t just failing because they haven’t found the right formula to transform metal into gold, but because the task they have set out to do cannot be done. You urge them to abandon their efforts and focus on other areas, but they don’t seem to listen. Instead, their response: “well sure we haven’t been able to turn lead into gold yet, but can you do any better? I don’t see any gold in your hands either.”

You can probably already see where I’m going with this metaphor. Replace chemist with economist and turning lead into gold with DSGE macroeconomics and you’ll have a good sense of what criticizing macro feels like. I don’t think it’s an exaggeration to say that every critique of macro is invariably going to be met by some form of the same counterargument. Of course the model isn’t perfect, but we’re doing our best. We’re continually adding the features to the benchmark model that you claim we are missing. Heterogeneity, financial markets, even behavioral assumptions. They’re coming. And if you’re so smart, come up with something better. It takes a model to beat a model.

I won’t deny there is some truth to this argument. Just because a model is unrealistic, just because it’s missing some feature of reality, doesn’t mean it isn’t useful. It doesn’t mean it isn’t a reasonable first step on the path to something better. The financial crisis didn’t prove that the methods of macroeconomics are wrong. Claims that nobody in the macro profession is asking interesting questions or trying to implement interesting ideas are demonstrably false. We certainly don’t want criticisms of macro to lead to less study of the topic. The questions are too important. The potential gains from solving problems like business cycles or economic growth too great. And if we don’t have anything better, why not keep pushing forward? Why not take the DSGE apparatus as far as we possibly can?

But what I, and many others, have tried to show, is that the methods of macroeconomics are severely constrained by assumptions with questionable theoretical or empirical backing. The foundation that modern macroeconomics is building on is too shaky to support the kinds of improvements that we hope it will eventually make. Now, you can certainly argue that I am wrong and that DSGE models are perfectly capable of answering the questions we ask. That’s quite possible. But if I am not wrong about the flaws in the method, then we shouldn’t need a new model to think about giving up on the current one. When somebody points out a flaw in the foundation of a building, the proper response is not to keep building until they come up with a better solution. It’s to knock the building down and focus all effort on finding that solution.

I can confidently say that if a better alternative to DSGE exists, I will not be the one to develop it. I am nowhere near smart or creative enough to do that. I don’t think any one person is. What I have been trying to do is convince others that it’s worth devoting a little bit more of our research efforts to exploring other methods and to challenge the fundamental assumptions that have held a near monopoly on macroeconomic research for the last 40 years. The more people that focus on finding a model to beat the current model, the better chance we have to actually find one. As economists, we should at the very least be open to the idea that competition is good.

Where do we start? I think agent based simulation models offer one potential path. The key benefit of moving toward a simulation model over a mathematical one is that concerns about tractability are much less pressing. Many of the most concerning assumptions of standard macro models are made because without them the model becomes unsolvable. With an agent based model, it is much easier to incorporate features like heterogeneity and diverse behavioral assumptions and just let the simulation sort out what happens. Equilibrium in an agent based model is not an assumption, but an emergent result. The downside is that an ABM cannot produce nice closed form analytical solutions. But in a world as complex as ours I think restricting ourselves to only being able to answer questions that allow for a closed form solution is a pretty bad idea – it’s looking for your keys under the streetlight because that’s where the light is.

Maybe even more important than developing specific models to challenge the DSGE benchmark is to try to introduce a little more humility into the modeling process. As I’ve discussed before, there isn’t much of a reason to put any stock in quantitative predictions of models that we know bear little resemblance to reality. Estimating the effects of policy to a decimal point is just not something we are capable of doing right now. Let’s stop pretending we can.

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