When I wrote my earlier post recounting what I learned in econ grad school, I realized shortly after I finished it that I might have sounded like I was being a little too harsh on my own econ department, which is really quite a good one. That's why I added the following:
In my second year I took a macro field sequence, which taught me all about demand-based models, frictions, heterogeneity, and other interesting stuff. I don't want to make it sound like graduate school taught me nothing about how to understand the recession...it taught me plenty. It just all came in the field course...
I realize now that this update deserved its own post. After all, the course I described in my last post was one single semester, out of four that I've spent learning macro. If we're trying to assess how well grad school trains macroeconomists, we should talk about the field classes that they're required to take.
My second-year field class was divided into four half-semester portions. Each had its own theme. Broadly, these were: 1) Heterogeneous-agent models, 2) Sticky-price models, 3) Neo-monetarism, and 4) Labor search. Some highlights:
* We spent quite a lot of time on heterogeneous-agent models, e.g. Krusell-Smith model. These models turn out to be very tricky to solve numerically. So far, they have also been mostly wrong in their predictions. But they are very interesting nonetheless.
* We learned about sticky-price models and their cousins, Greg Mankiw's sticky-information models (Mankiw is pictured above). I really liked Mankiw's model; although it (like most macro models) is a "storytelling" model with some implausible assumptions and no real predictive power, the story it tells points in some very interesting research directions, since it involves much more interesting microfoundations than the standard "tastes and technology."
* We briefly covered structural vector autoregressions, or SVARs (I also learned these in a stats class). I liked these because the focus was on making forecasts...finally, someone calculating something! Also, they were honest about their limitations; their standard error bars were so big that they had very little predictive power more than one quarter into the future, but they admitted and prominently displayed this fact, instead of using something like "moment matching" to try to exaggerate their empirical success.
* We studied this very interesting paper by Basu, Fernald and Kimball. Basically, the paper constructs a very general form of the RBC model, and finds that it can't explain economic fluctuations. The reason is that improvements in technology, which are what cause booms in Prescott's original RBC setup, actually cause recessions once you allow for things like imperfect competition. This reinforces similar results by Jordi Gali, who used SVARs but arrived at the exact same conclusion.
* We learned some neo-monetarist models (by the way, what I learned was called "neo-monetarism" seems very different from what Stephen Williamson thinks it is). The neo-monetarist policy response to recessions, I learned, is quantitative easing. Or, as my advisor Miles Kimball put it: "Print money and buy stuff!" (He actually repeated this line four times in a row. When I asked him later what he thought of Bernanke's response to the recession, he grinned hugely and said "He printed money and bought stuff!") I also learned that some neo-monetarist models have a role for fiscal policy, but only for a short time after a particularly severe drop in investment.
* We studied labor search models, e.g. the Mortensen-Pissarides model (which recently won its creators the pseudo-Nobel). Although these models, like the heterogeneity models, make some incorrect predictions, they are commendable for admitting this fact. I liked these models because they relied on interesting and observable microfoundations (e.g. the job matching function).
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 it seems to point us in the direction of models that might work someday.
Anyway, Brad DeLong would still probably have some issues with my field course. We did learn a lot of demand-side models, and a bit of history as well (I learned about Wicksell, and about the Great Depression, both for the first time). But never once was finance mentioned. I learned about the existence of financial accelerator models in an email from a friend at Berkeley...
There were two other big conclusions I drew from that course.
The first was that the DSGE framework is a straitjacket that is strangling the field. It's very costly in terms of time and computing resources to solve a model with more than one or two "frictions" (i.e. realistic elements), with more than a few structural parameters, with hysteresis, or with heterogeneity, etc. This means that what ends up getting published are the very simplest models - the basic RBC model, for example. (Incidentally, that also biases the field toward models in which markets are close to efficient, and in which government policy thus plays only a small role.)
Worse, 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.
It was only after taking the macro field course that I began to suspect that there might be a political motive behind the neoclassical research program (I catch on quick, eh?). "Why does anyone still use RBC?" I asked one of the profs (not an RBC supporter himself). "Well," he said, stroking his chin, "it's very politically appealing to a lot of people. There's no role for government."
That made me mad! "Politically appealing"?! What about Science? What about the creation of technologies that give humankind mastery over our universe? Maybe macro models aren't very useful right now, but might they not be in the future? The fact is, there are plenty of smart, serious macroeconomists out there trying to find something that works. But they are swimming against not one, but three onrushing tides - the limited nature of the data, the difficulty of replicating a macroeconomy, and the political pressure for economists to come up with models that tell the government to sit on its hands.
Macro is a noble undertaking, but it's 0.01 steps forward, N(0,1) steps back...
I don't want to be discouraging, but the optimistic line when I arrived at grad school in 1985 was that macro was in its infancy and might be taking its first steps towards models which would actually work.ReplyDelete
Also this had already been the line for at least 10 years.
Thanks for the confessionals, Noah, Now you might want to take a look at Wynne Godley and Marc Lavoie, Monetary Economics: An Integrated Approach to Credit, Money, Income, Production and Wealth (Palgrave Macmillan, 2007), on stock-flow consistent macro models.ReplyDelete
Looking up neo-monetarist models...can't find anything...link, pretty please?ReplyDelete
(I catch on quick, eh?).ReplyDelete
Em, er sure.
I thought RBC/Neoclassicism was BS from the getgo.
But then maybe I had eyeballs.
But DSGE was rammed down our throats as it was supposedly what separated the PhDs from the boys.
I passed the macro comp and promptly vomited all over my shoes.
Isn't it great not believing in the field you major in?
@Anon: Link added!ReplyDelete
"I catch on quick, eh?"ReplyDelete
Yes - you do. But I don´t think ideology is the main reason for the obvious right wing bias in all of economics.
Instead, as you say:
"(Incidentally, that also biases the field toward models in which markets are close to efficient, and in which government policy thus plays only a small role.) "
That is not only true for macro, but for more or less all of economics (and for all of political philosophy, policy discussions etc. as well - but they at least don´t, in addition, have to model their claims mathematically).
How many percent of the economic articles that contain a model with some friction or market imperfection do you think contain exactly one, and how many contain more than one?
And how many percent of the empirical articles does not involve a single imperfections (mine does not - and it certainly does not have anything to do with me believing that the market I studied is without them)?
The main things I learned in Econ Grad school is thatReplyDelete
a) econ professors are narrow-minded and refuse to be questioned or challenged and
b) econ grad school teaches you almost nothing about economics; the field is entirely about math proofs.
I suspect this experience varies by school and instructor(s).
This confirms the decision I made after majoring in Economics as an undergraduate not to go on to graduate school in the field, for the very reasons enumerated above. What a wise decision!ReplyDelete
this is funny.ReplyDelete
polls of academic economists have c. 4/5 of them voting for the dems and yet you claim there is a vast right-wing conspiracy at work!
i think the voting is what's called "revealed preference". did you cover that in years 1 or 2?
How many sciences have the word "voodoo" as part of their description?ReplyDelete
That was an interesting article. I have to say that I find the following claim really shocking:ReplyDelete
"it's very politically appealing to a lot of people. There's no role for government."
It implies that some (respectable) economists are less interested in understanding how the world really works and more in justifying their sacred dogmas...
Actually, the Miles Kimball "neomonetarist" paper you link to is what Woodford would later call "New Keynesian". It's basically RBC with sticky prices grafted on. This work has nothing at all to do with what Randy Wright and I would call New Monetarism. Our work is all about monetary frictions, credit frictions, banking, etc.
As I suspected. Yeah, these terms are pretty slippery!
Working in a field that's frustratingly stuck in its infantcy isn't discouraging. It's exciting.
"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.
It was only after taking the macro field course that I began to suspect that there might be a political motive behind the neoclassical research program (I catch on quick, eh?). "Why does anyone still use RBC?" I asked one of the profs (not an RBC supporter himself). "Well," he said, stroking his chin, "it's very politically appealing to a lot of people. There's no role for government." "
Lucas once gave a talk where he argued that what made Keynes such a great economist was that he believed the government couldn't just sit back and allow depressions. Lucas believes the Great Depression was caused by tight money. I wouldn't group in in with those RBC economists who are in denial about the importance of demand shocks (even if Lucas doesn't use the term demand shock.) He's a realist, not an ideologue.
As far as the RBC research program, I share your low opinion.
Actually, my "beef" with Lucas is not that he insisted on RBC. It's that I think his insistence on microfoundations of one certain type was way too narrow ("tastes and technology" are not always "structural", but other things sometimes are). Even that is probably more the fault of people who rigidly interpreted the paradigm he set up. That's what I meant by "the hoops [Lucas] set up".
It seems an easy response to Basu, Fernald, and Kimball is that the economy is very competitive and markets are pretty complete for practical purposes.ReplyDelete
"However, the evidence is consistent with simple sticky-price models, which predict the results we find: When technology improves, input use and investment demand generally fall in the short run, and output itself may also fall."
So the great recession was caused by big technology improvements in 2007?
Well, no matter what and how well the macro-econ discipline/science (whatever you call) is going with respect to methodology, there might be still someways that you can (or I mean one can) discover by attempting to focus on just economic questions. This is for sure what all good economists do yet some people indeed more focus on designing environments through which you can be more flexible in terms of modeling markets (based on your definitions), preferences, the roles of institutions and the rules of the games. This might be an area that one could use serious math skills (even true for others) for example not maybe only focusing to predict future but maybe just to understand the "mechanisms". One direction would be to explore the works of Daron Acemoglu (MIT) among others, for example. As future economists (as a PhD student now), it is time for us not to be pessimistic and/or confused. Let's go for it!ReplyDelete
"polls of academic economists have c. 4/5 of them voting for the dems and yet you claim there is a vast right-wing conspiracy at work!"ReplyDelete
These things aren't mutually exclusive.
Math was embraced and hardest in econ first by socialists and left of center economists. And they used math (and bogus philosphy of science) to marginslize the great "free market" economists like Hayek who provided timeless accounts of the limitations of math constructs and math predictions in economics (and the fraudulence of a fake image of science as the model of all science).ReplyDelete
Math gives a formal metric for evaluation -- it's the "publish or perish" and secure tenure and status angle you point to first which matters. DSGE is merely the latest and greatest math metric for certifying "knowledge" and "science" in the discipline.
Math was embraced the hardest in econ first by socialists and left of center economists, (e.g. Lerner & Samuelson & Arrow).
Read David Colander on how the "rocket science" ethic has taken over economics -- how the grad schools have been dominated by math and physics majors with no economic training for decades. Colander also gives an account of why this happened, and why such things as the history of econ thought were shit canned.ReplyDelete
Or read the 1991 AEA Committee on Graduate Education report documenting the way grad schools were cranking out math "rocket scientists" with little or no knowledge of anything but a thin slice of math and econometrics.
Colander is mildly left of center.
Charlie Clarke said...ReplyDelete
" So the great recession was caused by big technology improvements in 2007?"
Which was preceded by a collapse in the housing market and a wave of catastrophe sweeping through the financial markets *purely* by coincidence.
Noah, had you ever studied economics extensively before grad school? If not, why are you in an econ program and what did you expect to get out of it? (I ask sincerely.)ReplyDelete
I studied and fell in love with the discipline as an undergrad, so I've come to see the tools taught in grad school as a supplement to the "economic way of thinking," but certainly not a replacement. What do you see it as? I'm curious.
Noah, excellent posts. They are a testimony of why I chose not to go for a phd in econ.ReplyDelete
You've only seen the tip of the iceberg. You probably don't even know about this:
It's not just that RBC/DSGE models don't match the data. They have been known to be logically inconsistent for decades. I hope you get interested and read the papers on this subject.
Interesting blog entry... now how did I stumble upon this page?!ReplyDelete
"It's not just that RBC/DSGE models don't match the data. They have been known to be logically inconsistent for decades. I hope you get interested and read the papers on this subject. "ReplyDelete
This statement contains essentially no correct statements. But the grammar is mostly correct, so it has that going for it.
"They are a testimony of why I chose not to go for a phd in econ. "ReplyDelete
And econ is all the better for it.
I wrote the comment with the reference on the theory of capital controversies.ReplyDelete
After that, two comments were made trashing my comment. That is, without any arguments.
Do they have any clue of what i am talking about?
ps: btw, my grammar was correct?!? that is a great complement to me. I'm not a native english speaker, and I'm much more fluent in portuguese and french than in english.
"The first was that the DSGE framework is a straitjacket that is strangling the field."ReplyDelete
Another objection to the DSGE models is that the basic assumptions are not simplifying assumptions, they are COUNTERFACTUAL assumptions. The assumptions that the households live forever and have perfect foresight about the means and the (normal) statistical distributions into the eternal future about the various variables is incredibly implausible. If non-economists realized that these were the assumptions on which "modern" macroeconomics is based and on which it derives its conclusions, economists would be a general laughighstock and subject to widespread ridicule.
The New Keynesians by adding various epicycles to the New Classical version of the DSGE model: sticky prices, wages, and expectations, have made it less blatently unrealistic so that useful results can be derived. But macroeconomics can never be truly relevant until this straightjacket is discarded.
"This confirms the decision I made after majoring in Economics as an undergraduate not to go on to graduate school in the field, for the very reasons enumerated above. What a wise decision!"ReplyDelete
For someone really interested in how the economy works it is essential that they learn the orthodox models well and become proficient in them. The better one understands them the more able one is to blow holes in them and come up with better alternatives.
"I wouldn't group in in with those RBC economists who are in denial about the importance of demand shocks (even if Lucas doesn't use the term demand shock.) He's a realist, not an ideologue."ReplyDelete
Scott, look at the current posting "The Architect of Modern Macroeconomics speaks!"
Lucas argues that "The reason for our current ongoing weakness in employment and business investment is the recent expansion of the U.S. welfare/regulatory state."
That is the position of an ideologue, not a realist.
Thanks for the update! I enjoyed your original post.ReplyDelete
1. Unless you're studying history, what's "interesting" about a model that is both difficult and wrong?
2. If macroeconomics isn't discarding models that are demonstrably wrong, I would suggest that far beyond being "a science in its extreme infancy", it's not a science at all.
To call something a science when its attitude is "we realize our theories have zero predictive or explanatory power, but we're going to keep them anyway" is a disservice to real sciences.
Full Employment Hawk said...ReplyDelete
" For someone really interested in how the economy works it is essential that they learn the orthodox models well and become proficient in them. The better one understands them the more able one is to blow holes in them and come up with better alternatives."
Except that it's been pointed out that the elites of the discipline don't work according to any sort of scientific method, and don't care about those holes.
Still, it sounds like success in the field has more to do with "Knowing and reacting to these theories" rather than understanding the real world.ReplyDelete