Sunday, May 24, 2015

Beating dead horses

The econ twittersphere has erupted in response to a provocative (but very blog-like) essay by the great Paul Romer, published in the Papers & Proceedings at AER. Romer is annoyed that certain old freshwater econ guys use math in an annoying way. Romer follows up here; Tony Yates has some thoughts here. Noah Smith, always up for a good bashing of said old guys, opines here, making the same points he usually makes (to wit: those silly freshwater guys just build models in which government can't be good, physicists and engineers have physics and engineering models that perform better empirically, here are some examples of silly things predicted by some DSGE models, etc.).

In recent years, guys like Noah have made very clear that they don't like bare bones RBC and that they don't like certain old guys in macroeconomics. But during these years, while the blogosphere has obsessed over this stuff, macroeconomists have been doing a ton of interesting work for which the blog debate is an uninteresting sideshow.

I've said this before and I will say it again: Whatever one might think of the contributions of the certain old guys to macroeconomics, the field has moved lightyears beyond that stuff. Nobody is using bare-bones RBC. The "freshwater vs saltwater" distinction is a redundant taxonomy--as best I can tell, it's really about Calvo pricing vs. flexible price models, while the sticky price assumption is just one of hundreds of ways that people add frictions to the RBC model. If you use the water-based terms instead of just describing specific frictions, you're just facilitating mood affiliation.

Few, if any, of the people writing models with flexible prices (but other frictions) would say that nominal frictions don't matter. It's just that nominal stickiness is one among many ways in which the real world deviates from bare-bones RBC, and every model must assume away something, and sometimes nominal stickiness is that something for good reason (meanwhile, a lot of good Calvo pricing papers ignore important financial sector frictions, not to mention heterogeneity and tons of other stuff, and that's ok). Sure, you can always find an absurd element of any model, as Noah does with relish in his post. But we're stuck with a world in which no model can explain everything, and in any case a paper that's good at some things and bad at others is an opportunity for another paper that's good at a few more things and bad at slightly fewer things. That's the nature of the discipline. It will always be easy to make the discipline look silly to outsiders who haven't confronted the magnitude of the problems we face.

Let me also say this: if there is anyone out there who criticizes the absurd oversimplification that is the representative agent model* but also criticizes mathiness, here's a newsflash: deviations from rep agent require hard math and/or nasty computation. The Mian and Sufi critique requires models in which agents differ at least along a wealth distribution. The Geanakoplos stuff requires hard math. So be careful how you use the term "mathiness" (I think Romer is using it in a reasonably precise way, and a lot of people are misinterpreting him and using it too broadly). More realistic models are going to require harder math, though I agree completely with both Romer and Roger Farmer that adding more math isn't always productive.

So while the blogosphere keeps restating 1970s fights, practicing macroeconomists are doing a lot of really interesting research that makes the freshwater/saltwater taxonomy irrelevant or at least useless. Bashing caricatures of the economics profession is a great way to get followers and sell books, but it doesn't advance the discipline.

UPDATE: This post is more snarky than I intended or am comfortable with. I think Noah and I are actually closer on this than it would seem from this text and the comments below. My basic point is that I hope people do not use the blogosphere as a sufficient statistic for what is going on in modern macroeconomics.

*I actually think rep agent is remarkably useful, particularly compared to how much it costs.


  1. Ryan, I think you're not really getting the point of my post (or of others in the same vein).

    I definitely don't think the key distinction in macro these days is between saltwater and freshwater.

    I mean, it's mildly interesting once in a while, like with the Kocherlakota fracas, but usually not.

    But I do think that the historical fact that RBC got so popular for a decade, without really doing much in the way of explaining data, shows something important about the culture of econ theory (or macro theory, at least): People don't demand that theory match data in order to be considered valuable and good.

    Even though macro has moved far beyond RBC, I'm not sure if that culture has really changed or not.

    If you want someone who thinks flexible vs. sticky prices is still the biggest and most important distinction in macro theory, talk to Matt Rognlie. He does seem to think this.

    1. Note that my examples of wacky assumptions in modern business cycle models usually come from Christiano, Eichenbaum, and Evans (2005), because A) it's a giant model full of moving parts, B) everyone at central banks uses Smets-Wouters which is a close variant, and C) CEE did us the kindness of spelling out their assumptions in plain detail, unlike Smets-Wouters.

      I've met Marty Eichenbaum, and he is probably the most scientifically-minded macro theorist I've met. He knows that 90% of the stuff in his models is massively misspecified. He just wants to do the best he can with what's available.

      But I think most macroeconomists don't think like him. They think "I do this for a living, so it must be good." So they don't constantly try to reduce the misspecification of their models, like Eichenbaum does. That's the macro culture that annoys me.

    2. All I can say is that you and I are seeing different seminars and getting different referee reports on macro papers, because my experiences with both of those things (in macro) aren't consistent with your view about taking models to data. One or both of us is having experiences that aren't representative of current macro. Since I spend a lot of time in macro seminars and I just toured the macro job market and dealt with some macro journals, you can probably guess my prior about which one of us that is.

      I don't have a problem with CEE. I do think it's a bit of a curve-fitting exercise, but it has a lot of value. For one thing, it illustrates the important tradeoff between being able to match every data series in the world and holding on to some theoretical clarity.

    3. I think we probably have different standards for what it means to take a model to the data. "Moment matching" is the sloppiest crapola I've ever seen, yet it seems to get taken seriously by a lot of people. Estimating the parameters of a model with MLE or Bayesian estimation or whatever is useless if the model is misspecified. And worst of all, there are all kinds of intermediate results and predictions within most macro models that are never taken to the data in any way, the idea being that the author basically gets to decide which empirical facts are relevant to the model and which are not.

      I freely admit that you know more than I do about current macro culture. But also, I think you're one of the good guys - you obviously put empiricism first. That may mean you're interacting mostly with other people who are empirically minded. It may be that there are some out there who are not as empirically minded, who are also thriving in the field! ;-)

    4. Yeah I likely have some selection bias, that is true. I hang out with a pretty empirical crowd.

  2. "practicing macroeconomists are doing a lot of really interesting research . . ."

    Until most of the macro profession starts to think critically about the market clearing assumptions, and how these interfere with a meaningful understanding of the role of money and finance in the macroeconomy, I'm not convinced that the macro research agenda can progress.

    It's not clear to me how it is possible to understand the data using the framework of models with standard market clearing, because they are so misleading. e.g. historians regularly make the error of looking for long-term finance because that is what is supposed to promote investment, but the prejudice against the role played by short-term finance in the economy is simply an artifact of flawed models. Unfortunately these models make it almost impossible to "see" the data.

    Here's a blog description (with link to paper) of the kind of friction I think macro needs to discuss, but doesn't: