Wednesday, May 25, 2011

What graduate school economics did and did not teach some random dude

I`ve got no idea who this guy is – found links to these posts from Tyler Cowen’s blog – but I found his reflection on his graduate economics education (see also part 2)  insightful and interesting.

Some highlights (that is to say – things that remind me of my own opinions ;-)

coming as I did from a physics background, I found several things that annoyed me about the course (besides the fact that I got a B). One was that, in spite of all the mathematical precision of these theories, very few of them offered any way to calculateany economic quantity. In physics, theories are tools for turning quantitative observations into quantitative predictions. In macroeconomics, there was plenty of math, but it seemed to be used primarily as a descriptive tool for explicating ideas about how the world might work. At the end of the course, I realized that if someone asked me to tell them what unemployment would be next month, I would have no idea how to answer them.

As Richard Feynman once said about a theory he didn't like: "I don’t like that they’re not calculating anything. I don’t like that they don’t check their ideas. I don’t like that for anything that disagrees with an experiment, they cook up an explanation - a fix-up to say, 'Well, it might be true.'"

That was the second problem I had with the course: it didn't discuss how we knew if these theories were right or wrong. We did learn Bob Hall's test of the PIH. That was good. But when it came to all the other theories, empirics were only briefly mentioned, if at all, and never explained in detail. When we learned RBC, we were told that the measure of its success in explaining the data was - get this - that if you tweaked the parameters just right, you could get the theory to produce economic fluctuations of about the same size as the ones we see in real life. When I heard this, I thought "You have got to be kidding me!" Actually, what I thought was a bit more...um...colorful.

(This absurdly un-scientific approach, which goes by the euphemistic name of "moment matching," gave me my bitter and enduring hatred of Real Business Cycle theory, about which Niklas Blanchard and others have teased me. I keep waiting for the ghost ofFrancis Bacon or Isaac Newton to appear and smite Ed Prescott for putting theory ahead of measurement. It hasn't happened.)

[…]

DeLong and Summers are right to point the finger at the economics field itself. Senior professors at economics departments around the country are the ones who give the nod to job candidates steeped in neoclassical models and DSGE math. The editors of Econometrica, the American Economic Review, the Quarterly Journal of Economics, and the other top journals are the ones who publish paper after paper on these subjects, who accept "moment matching" as a standard of empirical verification, who approve of pages upon pages of math that tells "stories" instead of making quantitative predictions, etc. And the Nobel Prize committee is responsible for giving a (pseudo-)Nobel Prize to Ed Prescott for the RBC model, another to Robert Lucas for the Rational Expectations Hypothesis, and another to Friedrich Hayek for being a cranky econ blogger before it was popular.

And from the follow-up blog-post which discusses the field-courses he chose (which, AFAIK are the courses he voluntarily chose):

The field course addressed some, but not all, of the complaints I had had about my first-year course. There was more focus on calculating observable quantities, and on making predictions about phenomena other than the ones that inspired a model's creation. That was very good.

But it was telling that even when the models made wrong predictions, this was not presented as a reason to reject the models (as it would be in, say, biology). This was how I realized that macroeconomics is a science in its extreme infancy. Basically, we don't have any macro models that really work, in the sense that models "work" in biology or meteorology. Often, therefore the measure of a good theory is whether itseems to point us in the direction of models that might work someday.

[…]

all of the mathematical formalism and kludgy numerical solutions of DSGE give you basically zero forecasting ability (and, in almost all cases, no better than an SVAR). All you get from using DSGE, it seems, is the opportunity to puff up your chest and say "Well, MY model is fully microfounded, and contains only 'deep structural' parameters like tastes and technology!"...Well, that, and a shot at publication in a top journal.

Finally, my field course taught me what a bad deal the whole neoclassical paradigm was. When people like Jordi Gali found that RBC models didn't square with the evidence, it did not give any discernible pause to the multitudes of researchers who assume that technology shocks cause recessions. The aforementioned paper by Basu, Fernald and Kimball uses RBC's own framework to show its internal contradictions - it jumps through all the hoops set up by Lucas and Prescott - but I don't exactly expect it to derail the neoclassical program any more than did Gali.