What does it mean to have "predicted the crisis"?

Since 2008, quite a lot of people have boldly claimed that they "predicted the crisis". Usually, the claimants use this "fact" to argue for the superiority of their economic school of thought, modeling approach, investing approach, or personal intuition. But what does it mean to have "predicted the crisis"?

First of all, there are different things that get labeled "the crisis". These include:

1. The big drop in U.S. housing prices that started in 2006-7.

2. The systemic collapse of the U.S. financial industry that began in 2008.

3. The deep recession and the long stagnation that began in late 2008.

Predicting one of these is not the same as predicting the others. It is possible, for example, to have missed the housing bubble and the finance industry collapse, but to have successfully predicted, after seeing these events happen, that a deep recession and long stagnation would be the result; this is what Marco Del Negro et al. claim to have done, and a number of pundits and commentators made informal recession predictions after housing peaked in 2006. Alternatively, it is possible to have predicted the bursting of the housing bubble without foreseeing the systemic damage that this would cause to the financial system; some economists, such as Dean Baker and Nouriel Roubini (and of course, Robert Shiller), seem to have called the bubble far in advance, as well as some writers like Bill McBride. It is also possible to have predicted the collapse of the big banks and their mortgage-backed bonds - and made money off of this - while staying agnostic about the macroeconomic consequences; this seems to have made a lot of money for investors like Steve Eisman and John Paulson. Of course, in theory it might have been possible to predict all three events.

Then there's the question of what it means to "predict" something. Here are some alternative definitions:

1. You could predict the timing of an event, e.g. when the housing bubble would burst.

2. You could predict the size or severity of an event, e.g. how much house prices would decline or how much the economy would contract in 2009.

3. You could predict the duration of an event, e.g. how long our economy would stagnate after the recession, or how long it would be before housing prices reached their pre-crash peak.

4. You could describe the particular characteristics of an event, e.g. what would cause banks to fail, or whether they would be bailed out, or whether inflation would remain subdued after the recession.

Next, there is the question of with what degree of confidence you make a prediction. Saying "this event is a conceivable possibility" is different than saying "the risk of this event is high," which is different from saying "the risk of this event has increased," which is different from saying "this event will happen."

Also, there is the question of how far in advance a prediction was made. That could be important.

Finally, there is the question of whether the prediction was made by a model or by a human. If it's a model, then there's the hope that humanity has a tool with which to predict future crisis events.

Anyway, how should we evaluate these claims? There are so many different combination of "predictions" and "crises" here that it's very difficult to lay out an explicit taxonomy of who got it "more right," and who got it "less right." As a more humble goal, we can examine a specific individual or model, and identify which events he/she/it predicted, with what degree of confidence, and when.

As an example, let's take Steve Keen.



Steve Keen, formerly a professor at the University of Western Sydney, is known for claiming more loudly and confidently than just about anyone else on the planet that he "predicted the global financial crisis". According to Keen, this should be a reason to believe his extensive critiques of neoclassical (i.e. mainstream) economics, and his suggested alternative paradigm, known as "Post-Keynesianism".

So in what way did Keen "predict the crisis"?

Searching the internet, I can find no record of an ex-ante prediction by Keen of a large-scale U.S. housing bubble. He did, however, predict an Australian housing bubble, in 2007 after the U.S. housing bubble had already begun to pop. That prediction has so far yet to materialize; Australian housing prices have not collapsed yet. As a result of this incorrect prediction, Keen lost a high-profile bet.

Did Keen predict the collapse of the U.S. finance industry (the Lehman shock and subsequent bailouts)? Not that I can find. Nor did he warn of the risk of such an industry collapse, as far as I can find.

How about the recession and stagnation? Here, Keen makes his strongest claim to have made an ex ante prediction. His argument is laid out in this paper. (Warning: as others have noted, Keen's papers are nearly unreadable.)

Much of the paper covers the history of macroeconomics as Keen sees it. Later, on page 10, we get to the part where he explains how he "predicted the crisis". Keen presents a macroeconomic model; actually, a class of macroeconomic models. Each of the models is a system of deterministic Ordinary Differential Equations describing the behavior of macroeconomic aggregates. He claims that this sort of model would allow one to realize that a crisis of the type we observed could potentially occur.

Notice, therefore, that this is not a prediction of timing. It is a prediction of the particular characteristics of a recession. And as to whether or not it is intended to be a prediction of the severity or duration of the recession...that's not clear. Keen isn't saying when a recession would happen, he's saying that his model shows what it would look like.

And what would it look like? Well, one of the models Keen presents (a "Goodwin" model, apparently from the 1960s) produces cycles of employment and output that look like this:

As you can see, these cycles are periodic, and of constant amplitude. But we know that this is not what business cycles really look like. (More complicated versions of this type of model might veer from periodicity into extreme nonlinearity and chaos, but chaotic models by definition have little to no predictive power.)

The next model he references is one of his own, produced in 1995. That model contains the possibility of something like a complete economic collapse:

My own simulations in Keen (1995) illustrated this possibility of a debt-induced collapse if the rate of interest was too high. For a low rate, a convergence to equilibrium occurred (Figure 4): 

At a higher rate, the system approached the infinite debt to output ratio equilibrium...

However, we have not observed an approach toward the infinite-debt-to-output ratio and near-total unemployment equilibrium that . Also, interest rates were still historically low when the financial crisis began. So this 1995 Keen model does not appear to describe the crisis we really had. Keen also adds:

[T]he 1995 model lacked price dynamics.

It's also noteworthy that Keen's 1995 model, like the "Goodwin model", contains plenty of periodicity, which as I mentioned is not observed in real life.

Keen then goes on to present a model that does include price dynamics. The figures he presents from that model is labeled "Schandl (2011)", indicating that it was made after the crisis and cannot therefore cannot be regarded as a prediction. Note that in that model, as presented by Keen, the economic collapse takes 40+ years to happen, and involves unemployment going to 100%:

In any case, it is clearly apparent that nowhere in this paper - or in any other paper that I can find - does Keen present a model whose output bears even a passing resemblance to the crisis we experienced in the late 2000s. (As an aside, note that many models, including a simple neoclassical Ramsey model, have equilibria in which the economy collapses completely. Building such a model is very very easy. But complete economic collapses - total and permanent cessations of economic activity - haven't yet been seen in the real world...ever.)

Therefore, we can conclude that there is no Steve Keen model that predicted the recession and long stagnation that we've experienced. And in fact, there does not seem to be any "Post-Keynesian model" whose features closely resemble the financial crises and recessions that we see in the real world.

So did Steve Keen himself warn in the early or mid 2000s of the impending possibility of an economic collapse? He claims that he did warn of an "impending global recession" in 2005 (see also here). I cannot find any actual writings by Keen from 2005, but I will take him at his word, since if he had made this up, I'm sure that his fellow Aussies would quickly tar and feather him for it. (If you have links to the 2005 prediction, please post them in the comments section.)

So Steve Keen presumably did warn in 2005 that a global recession was coming. This means that, counting his prediction of an imminent Australian crash, he has a 50% success rate. Remember that, according to Bayes' Theorem, the predictions of someone with an unconditional 50% success rate (i.e., coin flips) convey no information.

But is that his true success rate? After all, how many earlier predictions of imminent global recession has Keen made, that did not materialize? According to this website, Keen was predicting an imminent global recession as early as 1995. It was 12 or 13 years before his prediction came true; this long time lag makes the prediction a bit less impressive, since someone who in 1933 predicted a global recession - which did come, 80 years later - would nevertheless now be seen as having been "wrong". Now, 12 years is better than 80 years, of course.

Anyway, so we see that Steve Keen's prediction of the global financial crisis was considerably less impressive than his bold claims would have us believe. He does not have a model that can predict bubbles, financial collapses, or recessions. His personal warnings of doom often don't seem to materialize for over a decade...if they materialize at all. If you trust Steve Keen as an economist or as a personal prognosticator based on his 2005 warnings of imminent global recession, you may be falling victim to the common behavioral phenomenon of overconfidence. (Not that I expect this fact to give pause to many of his...um...ardent followers. Remember that pundits get more fans by displaying self-confidence than by being right!)

Of course, all this is not to say that Keen should receive zero plaudits, respect, or commendation for his 2005 warning - or, for that matter, for his 1995 and 2007 warnings. There are plenty of people out there who said that finance has nothing to do with recessions. There are plenty of people out there - including some very prominent mainstream economists - who said that big recessions couldn't happen anymore. However right Keen did or didn't get it - and even if he made his predictions just by reading old Minsky books and nodding his head in vague agreement - those mainstream people got things far less right.

Anyway, a similar exercise can be applied to any other economist, model, or pundit whom you think may have "predicted the crisis". You will obtain varying results, though my bet is that few will be as spectacular as you might hope.

In conclusion: Predictions are hard, especially about the future. Sometimes people get things right because they understand how the world works, and sometimes they get things right by luck. The idea of a brilliant Cassandra-like sage, shouting in the wilderness while everyone ignores his or her trenchant warnings, is occasionally true, but not as much as we would like to think.


(Update: Naturally, a bunch of people have been asking me: "So, Noah, blah blah blah, but who do you think predicted the crisis the best?" Well, I don't know. Back in 2002 and 2003 I was reading Dean Baker talking about a housing bubble and bank failures. And I remember believing that, and as a result not being too surprised when the crisis came. I'm fairly sure Baker also predicted that the macroeconomic knock-on effects would be severe. Nor do I recall him predicting a bunch of other crises that never happened. So from my very limited set of knowledge, I'd guess that Baker did very well as a prognosticator. But to really know, I'd have to go back and check systematically. Note also that Dean is a quite humble guy and doesn't go around thumping his chest about having "called the crisis"...)

DSGE + financial frictions = macro that works?

In my last post, I wrote:

So far, we don't seem to have gotten a heck of a lot of a return from the massive amount of intellectual capital that we have invested in making, exploring, and applying [DSGE] models. In principle, though, there's no reason why they can't be useful.

One of the areas I cited was forecasting. In addition to the studies I cited by Refet Gurkaynak, many people have criticized macro models for missing the big recession of 2008Q4-2009. For example, in this blog post, Volker Wieland and Maik Wolters demonstrate how DSGE models failed to forecast the big recession, even after the financial crisis itself had happened:

This would seem to be a problem. 

But it's worth it to note that, since the 2008 crisis, the macro profession does not seem to have dropped DSGE like a dirty dishrag. Instead, what most business cycle theorists seem to have done is simply to add financial frictions to the models. Which, after all, kind of makes sense; a financial crisis seems to have caused the big recession, and financial crises were the big obvious thing that was missing from the most popular New Keynesian DSGE models.

So, there are a lot of smart macroeconomists out there. Why are they not abandoning DSGE? Many "sociological" explanations are possible, of course - herd behavior, sunk cost fallacy, hysteresis and heterogeneous human capital (i.e. DSGE may be all they know how to do), and so on. But there's also another possibility, which is that maybe DSGE models, augmented by financial frictions, really do have promise as a technology.

This is the position taken by Marco Del Negro, Marc P. Giannoni, and Frank Schorfheide of the New York Fed. In a 2013 working paper, they demonstrate that a certain DSGE model was able to forecast the big post-crisis recession.

The model they use is a combination of two existing models: 1) the famous and popular Smets-Wouters (2007) New Keynesian model that I discussed in my last post, and 2) the "financial accelerator" model of Bernanke, Gertler, and Gilchrist (1999). They find that this hybrid financial New Keynesian model is able to predict the recession pretty well as of 2008Q3! Check out these graphs (red lines are 2008Q3 forecasts, dotted black lines are real events):

I don't know about you, but to me that looks pretty darn good!

I don't want to downplay or pooh-pooh this result. I want to see this checked carefully, of course, with some tables that quantify the model's forecasting performance, including its long-term forecasting performance. I will need more convincing, as will the macroeconomics profession and the world at large. And forecasting is, of course, not the only purpose of macro models. But this does look really good, and I think it supports my statement that "in principle, there is no reason why [DSGEs] can't be useful."

Remember, sometimes technologies take a long time to mature. People thought machine guns were a joke after they failed to help the French in the War of 1870. But after World War 1, nobody was laughing anymore.

However, I do have an observation to make. The Bernanke et al. (1999) financial-accelerator model has been around for quite a while. It was certainly around well before the 2008 crisis. And we had certainly had financial crises before, as had many other countries. Why was the Bernanke model not widely used to warn of the economic dangers of a financial crisis? Why was it not universally used for forecasting? Why are we only looking carefully at financial frictions after they blew a giant gaping hole in the world economy?

It seems to me that it must have to do with the scientific culture of macroeconomics. If macro as a whole had demanded good quantitative results from its models, then people would not have been satisfied with the pre-crisis finance-less New Keynesian models, or with the RBC models before them. They would have said "This approach might work, but it's not working yet, let's keep changing things to see what does work." Of course, some people said this, but apparently not enough. 

Instead, my guess is that many people in the macro field were probably content to use DSGE models for storytelling purposes, and had little hope that the models could ever really forecast the actual economy. With low expectations, people didn't push to improve the existing models as hard as they might have. But that is just my guess; I wasn't really around.

So to people who want to throw DSGE in the dustbin of history, I say: You might want to rethink that. But to people who view the del Negro paper as a vindication of modern macro theory, I say: Why didn't we do this back in 2007? And are we condemned to "always fight the last war"?

Update: Mark Thoma has some very good thoughts on why we didn't use this sort of model pre-2008, even though we had the chance.

Update 2: Some commenters and Twitter people have been suggesting that the authors tweaked ("calibrated") the parameters of the model in order to produce the impressive results seen above. The authors say in the paper (p. 13, section 3.1) that they did not do this; rather, they estimated the model using only data before 2008Q3. 

Which is good, because calibrating parameters to produce better forecasts is definitely something you are not supposed to do!! There is a difference between "fitting" and "pseudo-out-of-sample forecasting". The red lines seen in the picture above are labeled "forecasts". To do a "pseudo-out-of-sample forecast", you train (fit) the model using only data before 2008Q3, and then you produce a forecast and compare it with the post-2008Q3 data to see how good your forecast was. You should never fiddle with the model parameters to make the "forecast" come out better! 

From Section 3.1 of the paper it seems fairly clear that del Negro et al. did not make this mistake. But I think the authors should explain the forecasting procedure itself in greater detail in the next iteration of the working paper...just in case readers worry about this.

What can you do with a DSGE model?

When the Bank of England invited me to give a talk at their workshop on macroeconomics, I wasn't sure if they wanted me to provoke (i.e. troll) them with the kind of skeptical stuff I usually write on this blog, or to talk about my own research on artificial markets and expectations. So I did both. Now, this is a central bank event, which means secrecy prevails - so I can't tell you what the reaction was to my talk, or what other people said in theirs. But I thought I'd reproduce part of my talk in a blog post - the part where I talked about DSGE models. (In other words, the provocative part.)

"DSGE" is a loose term. It usually implies much more than dynamics, stochastics, and general equilibrium; colloquially, to be "DSGE" your model probably has to have things like infinitely far-sighted rational expectations, rapid clearing of goods markets, certain simple types of agent aggregation, etc. So when I talk about "DSGE models", I'm loosely referring to ones whose form is based on the 1982 Kydland & Prescott "RBC" model.

In recent times, of course, RBC models themselves have fallen out of favor somewhat in the mainstream business-cycle-modeling community, and have gone on to colonize other fields like asset pricing, international finance, and labor econ. As of 2013, the most "mainstream" DSGE models of the business cycle are "New Keynesian" models. The most important of these is the Smets-Wouters model, which has gained a huge amount of attention, especially from central banks, for seeming to be able to forecast the macroeconomy better than certain popular alternative approaches. If you know only one DSGE model, Smets-Wouters is the one you should know.

Anyway, my talk asked the question: "What can you do with a DSGE model?" Most people who evaluate the DSGE paradigm don't focus on this question; they either trace the historical reasons for the adoption of DSGE (the Lucas Critique, etc.), or they discuss the ways DSGE models might be improved. Instead, in my talk, I wanted to take the perspective of an alien econ prof who showed up on Earth in 2013 and tried to evaluate what human macroeconomic theorists were doing.

A DSGE model is just a tool. It's a gizmo, like a fork lift or a lithium-ion battery. The U.S. and Europe have invested an enormous amount of intellectual capital - thousands of person-years of our best and brightest minds - in creating, testing, and using these tools.

So what can you do with these tools?


1. Forecast the economy?

One thing you might want to do with a business cycle model is to forecast the business cycle. DSGE models have improved enormously in this regard. Though early RBC models were notoriously bad at forecasting, more recent, complex DSGE models have proven much better, and are now considered slightly better than vector autoregressions, and about as good as the Fed's own forecasts.

But as Rochelle Edge and Refet Gurkaynak show in their seminal 2010 paper, even the best DSGE models have very low forecasting power. Check out these tables from that paper:

These tables show the forecasting performance for the Smets-Wouters model (which, remember, is the "best in class") from 1992 through 2006. The first table is for inflation forecasts, the second is for growth forecasts. Look at the R-squared values. These numbers loosely describe the amount of the actual macroeconomic aggregate (inflation or growth) that the model was able to predict. An R-squared of 1 would mean that the forecasts were perfect. You'll notice that most of the numbers are very, very low. The Smets-Wouters model was able to predict a bit of inflation one quarter out (though the Fed's internal forecasts were much better at that horizon), and not at all after one quarter. As for growth, the DSGE model had very low forecasting power even one quarter ahead.

Now, this doesn't necessarily mean that DSGE models are sub-optimal forecasters. These things might just be very very hard to predict! Humanity may simply not have any good tools (yet) for predicting macroeconomies, just like we aren't yet able to predict earthquakes.

But there's also some evidence that we could be doing better than we are. In this 2013 paper, Gurkaynak et al. test the "forecast efficiency" of DSGE models, and find that their forecasts are not optimal forecasts. Also, they find that simple univariate AR models are often significantly better at forecasting things like inflation and GDP growth than the best available DSGE models! This is not an encouraging finding for the DSGE paradigm, since AR models are just about the simplest thing you can use.

Also, in this discussion of forecasting, remember that the deck has already been stacked in favor of DSGE models. Why? Because of publicity bias and overfitting. If DSGE models don't do well at forecasting, researchers will add features until they do better. As soon as they do well enough to look good, researchers will publicize the success. This is a perfectly appropriate thing to do, of course - it's like improving any machine until it's good enough to sell. But it means that the publicized models will have a tendency to overfit the data, meaning that their out-of-sample performance will usually be worse than their in-sample and pseudo-out-of-sample performance.

(Update: Via a commenter, here's a good survey of DSGE models' forecasting ability, including how they did in the Great Recession. See my new post for more...)

In other words, DSGE models are probably not very good as forecasting tools...yet. But they're about as good as anything else we have. And they have improved considerably compared to their early incarnations.


2. Give policy advice?

This is what DSGE models are "supposed to do" - in other words, most academics will tell you that this is the purpose of the models. Actually, a model can be perfectly good for policy advice even if it's bad at forecasting. This is because forecasts have to deal with lots of different effects and noise and stuff that's all happening simultaneously, while policy advice only requires you to understand one phenomenon in isolation.

But here's the problem: To get good policy advice, you need to know which model to use, and when. So how do you choose between the various DSGE models? After all, there's a million and one of them out there. And they're usually mutually contradictory; since they're fitted using many of the same macroeconomic time-series (e.g. U.S. post-WW2 GDP, employment, and inflation), one of them being a good model (even just in one specific situation) means the others must then not be good models.

So how do you choose which model to use to give you advice? Old methods like "moment matching", which were used to "validate" the original RBC models, are, simply put, not very helpful at all.

What about hypothesis testing? Again, not very helpful. If you make the model itself the null, then of course you'll reject it, because any model will be too simplified to explain everything that's going on in the economy. If you make the null the hypothesis that the DSGE model parameters equal zero, you'll almost always reject that null, even if the model is grossly misspecified.

In principle, I think you should use some kind of goodness-of-fit criterion, like an R-squared, using out-of-sample data and adjusted to favor parsimonious models. At the macro conferences and seminars I've attended, I haven't see people saying "Look at the out-of-sample adjusted R-squared of this model! We should use this one for policy!" Maybe they do say this, though, and I just haven't seen it. (Update: Here, some people, including Smets and Wouters, do evaluate the fit! Definitely check out this paper if you're into macro modeling.)

But anyway, there's a few more problems here. One is the lack of clearly defined scope conditions; macro theorists rarely work on the difficult problem of when to stop using one model and start using another (see next section). Another is the nonlinearity problem; most DSGE models are linearized, which makes them easier (i.e. possible) to work with, but means that their policy recommendations often don't even match the model.

(As an aside, many people say "OK, we don't know which DSGE model is right, so just combine a bunch of models, with some weights." Fine...but the weights aren't structural parameters, so by doing this you give up the supposed "structural-ness" of DSGE models, which is the main reason people use DSGE models instead of a spreadsheet in the first place.)

So to sum up, DSGE models could offer policy advice if you used an appropriate model selection criterion, and dealt carefully with a bunch of other thorny issues, AND happened to find a model that seemed to fit the data decently well under some clearly defined set of observable conditions. But I don't think we seem to be there yet.


3. Map from DSGE models to policy advice?

OK, so it's really hard to give definitive policy advice with DSGE models. Maybe you could instead use DSGE models as maps from policymakers' assumptions to policy advice? I.e., you could say "Hey, policymaker, if you believe A and B and C, then here are the implications for policies X and Y and Z." In other words, since DSGE models are internally consistent, maybe they can help tell policymakers what they themselves think can be done with regards to the macroeconomy. (Another way of saying this is that maybe we can leave model selection up to the priors of the policymaker.)

There's just one problem with this. DSGE models are highly stylized, meaning that it's often not possible even to figure out whether you buy an assumption or not.

Let me demonstrate this. Let's take a look at a DSGE model - say, Christiano, Eichenbaum, and Evans (2005). This New Keynesian model is very similar to the Smets-Wouters model mentioned above. Here is a VERY truncated list of the assumptions necessary for this model to work:

• Production consists of many intermediate goods, produced by monopolists, and one single consumption good" that is a CES combination of all the intermediate goods.
• Firms who produce the consumption good make no profits.
• Firms rent their capital in a perfectly competitive market.
• Firms hire labor in a perfectly competitive market.
• New firms cannot enter into, or exit from, markets.
• All capital is owned by households, and firms act to maximize profits (no agency problems).
• Firms can only change their prices at random times. These times are all independent of each other, and independent of anything about the firm, and independent of anything in the wider economy. (This is "Calvo pricing". The magic entity that allows some firms to change their prices is called the "Calvo Fairy").
• The wage demanded by households is also subject to Calvo pricing (i.e. it can only be changed at random times).
• Households purchase financial securities whose payoffs depend on whether the household is able to reoptimize its wage decision or not. Because they purchase these odd financial assets, all households have the same amount of of consumption and asset holdings.
• Households derive utility from the change in their consumption, not from its level ("habit formation"). Households also don't like to work.
• Households are rational, forward-looking, and utility-maximizing.

OK, I'll stop. Like I said, this is a VERY truncated list; the full list is maybe two or three times this long.

How many of these assumptions do you believe? I'm not sure that's even possible to answer. Formally, most of these are false. Some are very obviously false. The question is how good an approximation of reality they are. But how do we know that either?? Is it a good approximation of reality to say that households purchase financial securities whose payoffs depend on whether the household is able to reoptimize its wage decision or not? How would I even know? 

In principle, you could look at the micro evidence and see which of these assumptions looks kinda-sorta like real micro behavior. Some people have tried to do that with a few of the assumptions of the Smets-Wouters model; their results are not exactly encouraging. But if you tried to go ask a policymaker "Which of these things do you believe?", you'd get a blank stare.

So DSGE models don't make a clear map from assumptions to conclusions. But how about using them just to explore the robustness of models to variations in assumptions? A central bank (or the academic macro community) could make a bunch of DSGE models and compare their results, just to see how different modeling assumptions affect conclusions. In fact, that's probably what the academic macro community has been doing for the past 30 years. This seems somewhat useful to me, but there's a problem. DSGE models are not very tractable, so it's probably the case that nearly all of the modeling assumptions usable in DSGE models are poor approximations of reality. In that case, we'll be stuck searching next to the lamppost.

4. Communicate ideas?

DSGE models can definitely be used as a language in which to communicate ideas about how the economy works. But they are probably not the best such language. Simpler econ models, like OLG models, or even partial-equilibrium models, are much more flexible, and can be understood much more quickly by an interlocutor. DSGE models have a ton of moving parts, and it's generally very hard to see which assumptions end up causing which results. The better a model matches data or forecasts future data, the more moving parts it will generally have. This is called the "realism-tractability tradeoff". 

So if you only work with DSGE models, and if you try to understand everything in terms of DSGE models, you'll have a hard time communicating with other economists. I can see this being a problem in a central bank, where people need to communicate ideas very quickly in times of crisis.

So, what else would you have us do?

There are a number of alternatives that have been proposed to DSGE models. Different alternatives are generally proposed for the different purposes listed above.

For communicating ideas, the most popular alternatives are simpler, OLG-type models (which are, technically, DSGE, though not what we typically call "DSGE"!), and partial-equilibrium models (suggested by Robert Solow). I've seen some people use these at seminars, especially the OLG type, so I think this alternative may be catching on.

For forecasting, the common alternatives are "spreadsheet" type models (Chris Sims' dismissive term) that don't assume structural-ness. This is the kind of model used by the Fed (the FRB/US) and by some private forecasting firms like Macroadvisers.

Policy advice is the thorniest question, since you need your model to be structural. For this, the main alternative that has been put forth is called "agent-based modeling". I don't know too much about this, and the name is weird, because DSGE models are also agent-based. But basically what it seems to mean is to specify a set of microfoundations (behavioral rules for agents), and then do a big simulation. The big difference between this and DSGE is that with DSGE you can write down a set of equations that supposedly govern the macroeconomy, and with ABM you can't.

So are we wasting our time making all these DSGE models, or not?

My answer is: I'm not sure. So far, we don't seem to have gotten a heck of a lot of a return from the massive amount of intellectual capital that we have invested in making, exploring, and applying these models. In principle, though, there's no reason why they can't be useful. They have flaws, but not any clear "fatal flaw". They're not the only game in town, and realization of that fact seems to be slowly spreading, though cultural momentum may mean that the more recently invented alternatives (ABM) will take decades to catch up in popularity, if they ever do.

Bets do not (necessarily) reveal beliefs

Bryan Caplan is well-known for demanding that people bet on their macroeconomic beliefs and theories. The idea (which I endorse, btw) is that people don't really know much about macroeconomics, and tend to project an unwarranted sense of certitude in their ideas. Of course, Bryan is hardly alone in this belief; Alex Tabarrok famously declared that "a bet is a tax on bullshit".

But this idea, attractive as it is, is not quite true. The reason is something that I've decided to call the Fundamental Error of Risk. It's a mistake that most people make (myself often included!), and that an intro finance class spends months correcting. The mistake is looking at the risk and return of single assets instead of total portfolios. Basically, the risk of an asset - which includes a bet! - is based mainly on how that asset relates to other assets in your portfolio.

This means when people make bets, you don't necessarily know anything about what they really believe. Here is an example. A while ago I made a bet with Brad DeLong that U.S. inflation would go over 5% by 7/28/2015. Brad, who bet against inflation, gave me 50-to-1 odds. Now, if this were my only inflation-related bet, you could infer that I believe that there is a greater than 2% chance of 5% inflation between now and 7/28/2015. But you cannot infer that. In fact, my bet with Brad reveals nothing whatsoever about my inflation beliefs.

Why? Because I also made the exact opposite bet (i.e. that inflation would stay under 5%) with Patrick Chovanec, and gave him only 25-to-1 odds! In other words, I can't possibly lose money, no matter what inflation does (if pizza bets could scale perfectly, I could have executed an arbitrage, but I didn't bother; as things stand, I either break even or win 25 pizza dinner equivalents).

So we see that a bet does not reveal beliefs, because a bet is often used as a hedge. To use another example, I might bet on Sarah Palin winning the presidency, in order to partially hedge my personal sadness in that unfortunate state of the world.

Actually, if you take modern portfolio theory seriously - if you don't believe in any sort of mental accounting at all - then you'd have to look at my entire financial portfolio in order to determine what I really believe about inflation. Even had I not made the countervailing bet with Patrick, I might have been net long in nominal debt (i.e., I might have some cash in a bank account), meaning that my bet with Brad might have just been a hedge against my overall inflation risk.

This is definitely a problem that crops up in finance experiments. Experimentalists try to measure subjects' beliefs about asset price changes by asking them to make side bets about those changes. But we have to be careful to make sure that subjects can't use those side bets as a hedge against their choices in the other parts of their experiment. (You can do this by designing the payoffs such that it's optimal for people to bet on their true beliefs in the side bet, or you can randomly assign people to "prediction" and "investing" groups).

So we see that bets are not necessarily taxes on bullshit. This only becomes more apparent when we bring non-monetary payoffs into the picture. In reality, people make public bets based on all kinds of considerations other than financial gains - ego, fun, the need for posturing, or an excuse to go out for pizza. It's definitely not clear how these other payoffs interact with the monetary payoff of the bet, or with the payoffs of a person's other asset holdings (including other opportunities for ego, posturing, fun, and pizza dinner!). For example, in my case, I made the bet with Brad largely to help publicize the fact of low inflation, and to have an excuse to go to the excellent Zachary's Pizza. And I made the countervailing bet with Patrick not to hedge the risk of a $20 loss, but so I could write blog posts like this. (Also because I am highly mercurial and whimsical.)

Tyler Cowen summed it up best when he tweeted: "I say portfolios reveal beliefs, bets reveal personality traits and public posturing." Exactly.

Short blogging hiatus

In Europe for a week, won't be able to blog...

Why do people support austerity? A conjecture.

Paul Romer once said that "A crisis is a terrible thing to waste." A crisis, it is widely believed, gives you the chance to change long-entrenched institutions and make long-needed reforms. It's hard to read that quote without thinking the uncomfortable thought: Doesn't that mean that provoking, or at least allowing, a crisis is the best way to improve your institutions for the long-term?

This thought has been running through my head as I have interacted with three groups of people: 1) Southern European economists, 2) Western "Japan hands", and 3) American opponents of monetary and fiscal stabilization policy.

Regarding South European economists, my evidence is anecdotal, but every single Italian, Spanish, and Greek economist I've talked to has seemed very down on the notion of fiscal stimulus, and highly disdainful of Paul Krugman. Alberto Alesina seems to be an exemplar of their thinking. When discussing stimulus spending, they tend to predict that this spending will be captured by special interests and wasted. Monetary easing receives scarcely more respect. Inevitably, any discussion of the European crisis leads quickly to a discussion of broken institutions in the Southern European countries - poor tax collection systems, over-regulation, sclerotic labor markets, political corruption, and even a poor cultural work ethic.

Now, this could simply be selection bias; the U.S. is considered a bastion of laissez-faire, conservative macroeconomics, so it's possible that the conservative South Europeans are the ones who make it here. But interestingly, I see a very similar attitude among long-time Western observers of Japan (called "Japan hands"), who are mostly very skeptical of Abenomics, and very focused on structural issues. For example, here is Peter Drysdale:

The first two ‘arrows’ [in Abe's quiver] are crude Keynesianism and are controversial, not least because, if they work, they could bring unintended consequences for the currency and the Japanese government bond market... 

The ‘third arrow’ of revitalisation is therefore critical for the success of all these measures. If there is no effective reform program for promoting private sector investment-led growth, the chances of a bond market collapse and a fiscal mess multiply dramatically... 

A return to stable, relatively rapid growth, requires a more flexible and competitive Japanese economy. As Harner explains, ‘restrictions, anticompetitive and onerous laws and regulations, multi-tiered, bureaucratic interference and inflexibility, relatively high taxes — all these obstacles to free market exchange and competition have sapped profitability, international competitiveness, and growth from vast swaths of Japan’s economy’. 

Without getting rid of these burdens, Japan is not going to be able to grow its way out of stagnation and the risks would then be for deepening of the crisis.

As for American opponents of stabilization policy, these include John Cochrane, who pooh-poohs both fiscal and monetary stimulus, saying that we need to get rid of "sand in the gears" of our institutions in order to promote growth. They also include Tyler Cowen, who often disparages Keynesianism (though he sits on the fence in terms of monetary easing), and who often writes about the need to improve our political institutions.

What unites all these and other "austerians"? There are several possibilities. One is that austerity is a good idea, and that these smart people recognize that it is a good idea. Another is that these are political conservatives who are worried that countercyclical macroeconomic policy will redistribute income and regulatory privilege away from themselves or their favored social groups. A third is that the psychological impulse toward austerity - tighten your belt in bad times! - is simply very very strong among all humans. And a fourth possibility, favored by Paul Krugman, is the idea that austerity is perceived as morally virtuous.

I want to suggest a fifth possibility. I conjecture that "austerians" are concerned that anti-recessionary macro policy will allow a country to "muddle through" a crisis without improving its institutions. In other words, they fear that a successful stimulus would be wasting a good crisis.

Consider the perspective of someone who has long advocated institutional reforms. For example, imagine yourself as a Western "Japan hand". For decades, you have watched Japan stagnate. You have seen the revolving door of prime ministers come and go, come and go. You have watched the long-ruling LDP dish out trillions of dollars of taxpayer money to pay politically connected construction firms to pour concrete over every riverbed in the country, even as women were forced into unproductive housewifery by a sexist and hidebound corporate culture and foreign imports were blocked by ever more creative non-tariff barriers.

And as you watched Japan's economy stagnate and its productivity fall behind, you waited. You waited and waited for the day when things would get too dire, and the old system would eventually collapse under its own weight, and Japan would be forced to undergo an economic and social revolution. "One day," you told yourself, "they're not going to be able to muddle through anymore."

In 2011, it seemed that that day had finally come. Japan's economy had taken powerful blows from the 2008 crisis and the 2011 earthquake. The Fukushima nuclear accident had exposed the depths of government corruption. The long-ruling LDP had been replaced by the DPJ, but it was clear that the new guys were cut from the same tattered cloth, and only a massive political "realignment" could restore efficacy to Japan's Diet. And most of all, the Japanese debt continued to skyrocket, until it seemed inevitable that deep cutbacks were coming.

And then came Shinzo Abe, a stalwart of the old LDP, swept into power on a promise to beat deflation and use monetary stimulus to get Japan back on its feet. And Abenomics seemed to be working: the yen fell, inflation expectations budged, and the stock market soared. Suddenly there seemed to be a real possibility that Japan would "muddle through" yet again. Sure, Abe has also promised structural reforms, but - you think to yourself - you've heard that song and dance before. If Japan manages to muddle through under Abe's aggressive recession-fighting policy, there will be no real incentive for the old system to change. The day of reckoning will be pushed back another decade.

I can only imagine that a similar thought process is running through the heads of many South Europeans as they watch the macroeconomic debate. If monetary stimulus (including a euro exit) and fiscal stimulus manage to just barely save Greece and Italy and Spain from their own days of reckoning, won't the euro-sclerosis just deepen before things finally collapse in ten years' time? And I imagine that something similar might be running through the minds of John Cochrane and Tyler Cowen (Update: And Richard Fisher!), as they decry "sand in the gears". Suppose a Krugman-style stimulus really did work! Wouldn't that allow the sand to stay in the gears, reducing our long-term growth rate just to produce a little short-term stability?

In other words, maybe people like the idea of austerity because they think an economic stagnation is our best chance to address what they perceive to be our long-term challenges. Allowing a crisis might be less terrible than wasting it.

Now, when stated that way, the idea sounds kind of silly - why don't we just periodically bomb our own cities, in the hope that governance will improve during the rebuilding? But I find it very difficult to state with any confidence that the idea is wrong. When economists discuss the costs of stabilization policy, they limit their discussion to distortionary taxation, unexpected inflation, and things like that. They almost never bring politics or institutions into the picture. The fact is, we just don't know how institutions really work. So I can't dismiss the idea that anti-recessionary macro policy might, in fact, rob us of our best chances to make needed reforms.

But what I think we should do is to discuss this idea explicitly. If people really do think that the danger of stimulus is not that it might fail, but that it might succeed, they need to say so. Only then, I believe, can we have an optimal public discussion about costs and benefits.

Update: Eerily, the very day after I wrote this post, Steven Pearlstein of the Washington Post made exactly this argument for austerity. Tyler Cowen links approvingly, calling the argument "wisdom".

Science fiction novels for economists

Diane Coyle has a blog post called "Classics for economists," and someone on Twitter requested that I do a companion piece called "Science fiction for economists", so here it is.

Really, most science fiction is about economics. What makes most future visions interesting is not just the technical particulars of the cool new Stuff, but the social ramifications. Here are some of the sci-fi books that I thought dealt with important economic issues in the most insightful and interesting ways. I also chose only books that I think are well-written, with well-conceived characters, engaging plots, and skillful writing.


1. A Deepness in the Sky, by Vernor Vinge



In addition to being quite possibly the best science fiction novel I've ever read, Deepness is also a great meditation on public economics. When Vernor Vinge became famous in the 80s, he was a hard-core libertarian - his novel The Peace War, and its sequel short story "The Ungoverned", are like a Real Business Cycle model come to life, with lone-wolf genius engineers teaming up with private police forces to bring down a fascist technocratic government made up of...university administrators. Ha. But by the 90s, Vinge's views on government and markets had become markedly more nuanced - in the swashbuckling space opera A Fire Upon the Deep, we see private security forces failing miserably when faced with a powerful external threat (in fact, that book made me think of the "Tamerlane Principle"). Security, Vinge realizes, is a public good.

In Deepness, Vinge adds another public good: Research. The narrative of Deepness is split between a race of spider-people with roughly 20th-century technology, and a spacefaring guild of human merchants called the Qeng Ho. On the spider world, the protagonist is a brilliant scientist named Sherkaner Underhill, who is basically a Von Neumann or Feynman type. Sherkaner is the ultimate lone genius, but he ends up needing the government to fund his research. In space, meanwhile, the heroic merchant entrepreneur Pham Nuwen (who is a recurring protagonist in Vinge novels) struggles to decide whether he should turn his merchant fleet into an interstellar government. Governments, he finds, are good at producing really new scientific breakthroughs, but eventually they become unwieldy and stifle the economy and society, then collapse under their own institutional weight. The very very end of the book is - or at least, seemed to me to be - a metaphor for the Great Stagnation and the death (and future rebirth) of Big Science.


2. Makers, by Cory Doctorow



Cory Doctorow is known both for his science fiction and for being the creator of the blog Boing Boing, one of the oldest and best blogs on the internet. In Makers, Doctorow dishes up a near future that is almost spooky in its prophetic vision. The book is all about economics, the death of corporations, the rise of freelance and temp economies, the death of old media and the rise of blogs, and the disruptive impact of technology on people's jobs. It envisions the rise of 3D printing, the startup craze (and the startup glut), and the use of intellectual property as corporations' weapon of choice to fight back against progress. It's incredibly well-written, but also extremely sad, just to warn you.


3. The Dispossessed, by Ursula K. LeGuin



It's incredibly hard to imagine a world without private property, but LeGuin pulls it off. Spoilers: A world without property is pretty boring and fairly poor. But LeGuin also shows us another world, much like our own, where the anti-property anarcho-syndicalist movement was suppressed and tamed, much like Marxism was suppressed and tamed here on Earth. What's interesting is that although anarcho-syndicalism doesn't work incredibly well on the world where it's implemented, the anarcho-syndicalist idea and movement serve as a sort of permanent opposition force on the capitalist world. When I read Robert M. Buckley writing that Marxism has fulfilled a similar role in the West here on Earth, I immediately thought of The Dispossessed.


4. Down and Out in the Magic Kingdom, by Cory Doctorow



Doctorow again. In this book, he examines what a true post-scarcity society would look like. Spoiler: It looks a lot like a bunch of sarcastic bohemian Canadian people. But basically, I think that's probably what the future will look like, at least if we're lucky. Anyway, this book is notable for the concept of "whuffie", an online currency based on peer approval, which arguably inspired Facebook's "like" button.


5. Rainbows End, by Vernor Vinge



Vinge again. Rainbows End is a sad, thoughtful novel about old age and obsolescence (notice that there is no apostrophe in the title). But it's also one of the most visionary novels about future labor markets. In an interconnected world in which skills never stay fresh for long and most value is created through entertainment, old engineers have to go back to high school, and new corporations are started by teenagers collaborating online with strangers halfway around the world. Rainbows End is also famous for envisioning the technology of Augmented Reality; this novel probably inspired Google Glass. Interestingly, Vinge continues his evolution toward a balanced view of public goods, adding education to the list of things that government needs to provide. (Update: In an email, Vinge points out that he never specified that the high school in Rainbows End was government-funded! Touche!)


6. Accelerando, by Charles Stross



Charles Stross, another noted blogger, loves to play with ideas, even if he doesn't believe in them. In Accelerando, he mainly plays with the idea of the Singularity, but he also plays with a bunch of far-out funky future economics. In one part, the main character, impresario and wandering entrepreneur Manfred Macx, uses advanced computer algorithms to successfully implement an optimal centrally planned economy, by predicting what humans will want before they even know they want it. Macx's various disruptive innovations inevitably draw the ire of the law, and he creates a protective cloud of AI lawyers to wage constant "lawfare" against governments and corporations alike. In another part of the book, the entire solar system is taken over by sentient High-Frequency Trading algorithms. But I haven't really spoiled the book for you, since these are only about 0.1% of the ideas contained within. Note: Stross has also written a series called the Merchant Princes series that deals even more with economics, but I haven't read it.


7. Lucifer's Hammer, by Larry Niven and Jerry Pournelle



Lucifer's Hammer is a story about a comet hitting Earth, and the aftermath. It's notable for its quaint Reaganite conservative politics (it came out in 1977), and does make a couple of glaring economic mistakes (for example, a guy trying to build a nuclear power plant is an independent wildcatting entrepreneur instead of a giant government-backed corporation). But it makes up for that with its excellent portrayal of what the economy would be like in the immediate aftermath of an abrupt civilizational collapse. Hint: Farming, containment of contagious diseases, de-specialization of labor, and collective security become very very important.


8. The Windup Girl, by Paolo Bacigalupi



Brutally dark and hopeless, The Windup Girl is a book about peak oil, global civilizational decline, and the (temporary) end of positive-sum economies. In a suddenly overpopulated world, humans are forced into a constant Hobbesian zero-sum game, and most moral norms go right out the window. Warning: This is very tough book to read. But it serves as an important reminder of the Malthusian menace that forever lurks just outside the circle of light provided by the flickering candle-flame of modern technology.


9. The Moon is a Harsh Mistress, by Robert Heinlein



Actually a mythic retelling of the American Revolution, The Moon is a Harsh Mistress contains some very interesting thoughts on colonialism and the Resource Curse. Unsurprisingly, terrorism is used as a way to make resource colonialism too expensive for the occupying power. Unfortunately, we don't get to see the political struggles and despotic regimes that probably arose in the aftermath of lunar independence. But Heinlein also does use the book to play with some interesting libertarian ideas, like a privatized court system.


10. Schismatrix, by Bruce Sterling



Simply one of the most wide-ranging and visionary science fiction novels ever written. Bruce Sterling is like an eternally erupting quasar of creativity, and this is his finest book. None of the economics here makes any sense - or, more accurately, it all takes place in such a funky, crazy future world that it's impossible to know if it makes any sense.


11. Permutation City, by Greg Egan



If there's any sci-fi novel that beats Schismatrix for far-out blow-your-brain-out-the-back-of-your-skull vision, it's Permutation City. If I hadn't already had the idea for D-Mod, this book (written over a decade before I thought of the concept) would have given it to me fully formed. Permutation City is about the ultimate nature and purpose of human consciousness and experience, and yet it has implications for technologies that are being developed right now, as we speak.


12. Reamde, by Neal Stephenson



Most people would recommend Stephenson's Snow Crash or Cryptonomicon, but for economics I like Reamde the best. Although not technically sci-fi, it has that flavor. The hero is an aging tech entrepreneur who owns a game that's a combination of World of Warcraft and Bitcoin (yes, this book predicts Bitcoin). It also deals with the economic incentives of the Russian mob, the challenges facing smart young tech workers in China and Hungary, and lots of other cool features of today's global economy. It's not Stephenson's #1 awesomest book, though; that would be Anathem.


13. The Game of Thrones series, by George R.R. Martin



Actually, I lied earlier...the person who requested a "Sci-fi for economists" list also asked me to include fantasy. But the Game of Thrones books (actually called A Song of Ice and Fire, though few use that name anymore) are really the only fantasy novels I can think of that deal with economics in an interesting way. You get to see a lot of the messed-up economies of medieval times, including feudalism, slavery, anarchy, blood sport, and the difficulty of international trade with poor information and unreliable transportation.


Anyway, there's my list. I haven't read everything out there, obviously - I hear that Ken MacLeod's books have a lot of economics in them, for instance, as well as some of Heinlein's other works. But if you're in the econ field and you want to think big deep thoughts about economics under different technological and social conditions, these books are for you. They're also a lot of fun.

Update: Mark Palko looks at "Crime novels for economists". Diane, looks like you started a meme!

Update 2: Other sci-fi recommendations via Paul Krugman and Tim Worstall.

Update 3: A couple of additions...


14. The Year of the Flood, by Margaret Atwood



I had a very good reason for omitting this book from the original list: I hadn't read it yet. Now I have. The Year of the Flood is the companion novel to Oryx and Crake, a philosophical dystopia/apocalypse novel (and one of the best such novels ever written, IMHO). But you can read The Year of the Flood on its own. This book is, basically, about a market dystopia. Everything is for sale in this future, and yet negative externalities, asymmetric information, and weak institutions make the world a nightmare.


15. When Gravity Fails, by George Alec Effinger



Don't know why I forgot this one earlier. It's basically a novel about underground economies and organized crime. Interesting for those who like to think about the economics of such things. Also, an extremely well-written and fun book.


16. Stand on Zanzibar, by John Bruenner



A lot of commenters have been mentioning this one, and they're absolutely right. Stand on Zanzibar is a little dated, but it really picks up on a lot of economic trends that were just starting in the 60s (e.g. the pricing of previously plentiful commodities such as water). The economics are all about the effects of overpopulation, which seems less menacing in the 2010s, but is still a major problem in parts of the world. A great futurist novel from the past.


Note: Still no Foundation series. I love the Foundation series, and it's really fun, but the economics are crappy (agglomeration economies encourage the collapse of empires?). And more importantly, the whole conceit of psychohistory is very dangerous for economists; our models will never forecast that well, the Universe is stochastic rather than deterministic, and we should avoid the temptation for intellectual hubris.  Foundation is social science's "physics envy" writ large. We are not, and will never be, Hari Seldon, so Foundation, classic as it is, doesn't go on my list!

Of course "hedge funds" lose money

Matt O'Brien, one of my partners-in-crime over at the Atlantic, has a piece criticizing hedge fund managers who go on TV to advocate hard-money policies. (Joe Weisenthal has a similar piece.) I agree with the criticism. But Matt also calls hedge fund managers out for their poor investment performance. As this article from The Economist shows, super-expensive hedge funds have done terribly over the last decade, when compared with a simple low-cost diversified portfolio of stocks and bonds. Matt says: "Hey hedge fund guys, if you can't even beat the market, why should we trust you on policy issues?"

I think this latter criticism is a bit misplaced, for two reasons. The first (and less important) reason is that to evaluate hedge funds - or any investment - you need to look not only at the return, but at the risk. If hedge funds have higher return-to-risk ratios (such as Sharpe ratios) than a passive stock-bond portfolio, then they are a better investment. Why? Because in that case you can borrow money and invest it in hedge funds, and your leverage will increase the returns (and the risk) of the hedge fund investment. If the hedge funds have a higher Sharpe ratio than the passive portfolio, you can leverage up until your risk is the same as the passive portfolio but your return is higher. In that case, you will have beaten the market, even if the hedge funds in which you invested did not beat the market. A number of top hedge funds have earned lower returns than the market since the financial crisis, but with much lower risk.

See?

Now, I said that this is the "less important" reason. This is because even after adjusting for risk, hedge funds as a class probably underperformed the market. And they can be expected to continue to underperform the market, as a class. But that's just because hedge funds as a class are not particularly special, interesting, valuable, or desirable.

What is a "hedge fund"? It's a legal category, like "mutual fund". The "hedge fund" category is basically a "none of the above" legal category, meaning that hedge funds, alone among money management companies, have essentially no restrictions on the kinds of assets they are allowed to trade. To start a hedge fund, all you have to do is be a "qualified investor" with $5 million in capital, or be a "sophisticated investor". That means that as a hedge fund you can be essentially any Tom, Dick, or Harry, and you can try essentially any strategy. You could have macaque monkeys pick stocks and call it a "hedge fund". The catch-all "hedge fund" category attracts many of the best ideas in the investing world, but also many of the worst. And there's a lot more bad ideas than good ones. And you can't just tell which is bad and which is good by looking at size and fame, because many of the bad ones get lucky and get some temporary good returns, which results in people handing them giant wads of cash (which they then proceed to lose, while taking a giant fee).

Thus, just throwing your money at anything that is called a "hedge fund", just because you have heard that some "hedge funds" have managed to earn spectacular returns, is an extraordinarily bad idea.To put it another way: Anthony Scaramucci, organizer of the SALT hedge fund conference in Las Vegas, writes: "Mutual funds are the propeller planes, while hedge funds are the fighter jets." But that's not true. Some of them really are fighter jets. And some of them are beat-up old pickup trucks covered in papier-mache to make them look like fighter jets from a distance. And you aren't allowed to get anywhere near the planes to touch them and see which is which. And you forgot your glasses.

Anyway, I'm sure many rich people do invest in anything called a "hedge fund", but they're just throwing their money away (fortunately they have plenty to spare). But if America's pension funds, mutual funds, and insurance companies are doing this, then we have a problem.

In any case, we shouldn't be surprised that hedge funds as a class have been getting crappy returns of late. In fact, we've seen this sort of pattern before. In the 1990s, "venture capital" firms earned amazing returns, and a bunch of people heard about it and started throwing their money at anything that called itself a "venture capital" fund. New funds flooded the field to take advantage of this inflow of dumb money. Returns subsequently collapsed and have not recovered, though the old established firms continued making outsized returns (but stopped taking new investments, because when you get big it's harder to grow fast). The same thing happened with "private equity" (leveraged buyout) firms, who made a killing in the 00s but have not been doing so well since. And the same thing probably happened with mutual funds, back in the 60s when they became prominent and earned a lot of money.

So there is a very interesting behavioral story going on here. Why do people hurl their money blindly at the flavor-of-the-week money-management company category? Why do they fail to understand that there are good and bad hedge funds, just like there are good and bad architects or doctors or web designers? I don't know, but it's a fertile topic for behavioral finance research.

(And as a final note, the big worry when investing in hedge funds should probably be fees, not past performance. Even the best hedge funds may charge you such high fees that the extra returns they earn you get eaten up. So watch out.)

Back to the original subject, though. Matt shouldn't castigate "hedge funds" as a whole for making crappy returns, because it's just a legal category, not a hive mind. But his basic point stands anyway. You shouldn't trust hedge fund guys on policy issues. In fact, he even understates his case. Even if a hedge fund guy makes more money than God, year in and year out, you shouldn't trust him on policy issues any more than a highly successful physicist or heart surgeon or poker player. A money management company is not a nation-state.


Update: On Twitter, Giorgio Vitale brought to my attention the fact that the graph Matt shows is not actually hedge fund returns (those are often undisclosed), but the returns on an index that tries to track broad hedge-fund performance. That's good to know, though my points all still apply...

ACV vs Stated Amount vs Agreed Values for Vehicles

Every time you get into your car and start the engine it is very likely that pennies fall off. Well, not actual pennies but the value of the car drops a very small amount each mile it is driven. This is because most cars are a depreciating asset. With this in mind let's talk about three common ways you can insure the value of your vehicle. The three ways are Actual Cash
Value (ACV), Stated Amount and Agreed Value.

Actual Cash Value is the most common form of valuing a car by insurance companies. What this means is that after an accident they take the original value of the car when it was brand new and they then depreciate the car over time until the date of the claim. They taking into account the miles driven, prior damage to the vehicle, wear and tear and maintenance upkeep of the vehicle. The farther away you are from the date the car was made the lower the value of the car.

Stated Amount is a little bit different. In this case you would tell the insurance company what you feel your vehicle is worth, say $30,000. This $30,000 is now the most the insurance company will pay out for the car, however when you have a claim they will research to see what other vehicles similar to yours are being valued for. If that value is less than the $30,000 they will give you the lesser amount. You often see this in collectors cars or cars that have a lot of specialize equipment attached to the body of the vehicle.

Agreed Value is where both the insurance company and you come to a prearranged value for your vehicle. When you agree upon this value, say it is $30,000 again, when a claim arises you are going to automatically be paid the agreed upon value of $30,000. Unlike Stated Amount, they do not go out and decide if the market still feels your car is worth a certain amount, they just agree to pay the agreed upon value that was settled before the claim even happened. This is most used for classic/collector cars. In fact it is best to make sure your classic/collector car is an Agreed Value instead of a State Amount. Often this requires an appraisal which may cost a little money to have done. One other thing to take into account when vehicles are insured for an Agreed Value, they can often have a limit on how many miles the vehicle can be driven each year.

For more information on valuations of vehicles please feel free to get in touch with Fey Insurance Services. We have been serving the Oxford, OH and Cincinnati, OH areas since 1958.

If you get a PhD, get an economics PhD

People often ask me: "Noah, what career path can I take where I'm virtually guaranteed to get a well-paying job in my field of interest, which doesn't force me to work 80 hours a week, and which gives me both autonomy and intellectual excitement?" Well, actually, I lied, no one asks me that. But they should ask me that, because I do know of such a career path, and it's called the economics PhD.

"What?!!", you sputter. "What about all those articles telling me never, ever, nerver, nenver to get a PhD?! Didn't you read those?! Don't you know that PhDs are proliferating like mushrooms even as tenure-track jobs disappear? Do you want us to be stuck in eternal postdoc hell, or turn into adjunct-faculty wage-slaves?!"

To which I respond: There are PhDs, and there are PhDs, and then there are econ PhDs.

Basically, I think of PhDs as mostly falling into one of three categories:

1. Lifestyle PhDs. These include math, literature and the humanities, theoretical physics, history, many social sciences, and the arts. These are PhDs you do because you really, really, really love just sitting and thinking about stuff. You work on you own interests, at your own pace. If you want to be a poor bohemian scholar who lives a pure "life of the mind," these PhDs are for you. I totally respect people who intentionally choose this lifestyle; I'd be pretty happy doing it myself, I think. Don't expect to get a job in your field when you graduate, though.

2. Lab science PhDs. These include biology, chemistry, neuroscience, electrical engineering, etc. These are PhDs you do because you're either a suicidal fool or an incomprehensible sociopath. They mainly involve utterly brutal hours slaving away in a laboratory on someone else's project for your entire late 20s, followed by years of postdoc hell for your early 30s, with a low percentage chance of a tenure-track faculty position. To find out what these PhD programs are like, read this blog post. If you are considering getting a lab science PhD, please immediately hit yourself in the face with a brick. Now you know what it's like.

(Note: People have been pointing out that EE isn't as bad as the other lab sciences, with somewhat more autonomy and better job prospects. That's consistent with my observations. But econ still beats it by a mile...)

3. PhDs that work. I'm not exactly sure which PhDs fall into this category, but my guess is that it includes marketing, applied math and statistics, finance, computer science, accounting, and management. It definitely, however, includes economics. Economics is the best PhD you can possibly get.

Why get a PhD in economics? Here's why:

Reason 1: YOU GET A JOB.

Can I say it any more clearly? An econ PhD at even a middle-ranked school leads, with near-absolute certainty, to a well-paying job in an economics-related field. I believe the University of Michigan, for example, has gone many, many years without having a PhD student graduate without a job in hand.

You will not always get a tenure-track job, though there are a lot more of those available right now than in other fields (thanks, I am guessing, to the nationwide explosion in business schools, which hire a lot of econ PhDs, including yours truly). But if you don't get a tenure-track job, you will get a well-paid job as a consultant, or a well-paid job in finance, or a decently-well-paid job in one of the many, many government agencies that hire armies of economists. All of these are what are commonly referred to as "good jobs," with good pay, decent job security, non-brutal working conditions, and close relation to the economics field.

Now, this may be less true at lower-ranked schools; I don't have the data. I imagine it's not as certain, but still far, far better than for lab science PhDs at similarly ranked schools.

Why do so very few newly minted econ PhDs face the prospect of unemployment? Part of it is due to the econ field's extremely well-managed (and centrally planned!) job market. Part of it is due to the large demand from the lucrative consulting and finance industries. And part is due to the aforementioned proliferation of b-schools. There may be other reasons I don't know. But in an America where nearly every career path is looking more and more like a gamble, the econ PhD remains a rock of stability - the closest thing you'll find to a direct escalator to the upper middle class.


Reason 2: You get autonomy.

Unlike the hellish lab science PhD programs, an econ grad student is not tied to an advisor. Since profs don't usually fund econ students out of grants (few even have big grants), econ grad students mostly pay their way by teaching. This means you usually have to teach, but that is not nearly as much work as working in a lab. Even when a professor does support you with a grant, (s)he generally employs you as a research assistant, and gives you ample time to work on your own research.

Compare this to a lab science PhD, in which you basically do the project your advisor tells you to do, and you succeed or fail in part based on whether your advisor chooses a project that works out. Your destiny is out of your hands, your creativity is squelched, and your life is utterly at the mercy of a single taskmaster. In economics, on the other hand, you can start doing your own original, independent research the minute you show up (or even before!). Profs generally encourage you to start your own projects. Unlike in lab science PhD programs (but like in "lifestyle" PhD programs), your time is mostly your own to manage.

This means that as an econ grad student, you'll have a life. Or a chance at having a life, anyway.


Reason 3: You get intellectual fulfillment.

Econ is not as intellectually deep as some fields, like physics, math, or literature. But it's deep enough to keep you intellectually engaged. Econ allows you to think about human interactions, and social phenomena, in a number of different intellectually rigorous ways (e.g. game theory, incentives, decision theory, quantification of norms and values, bounded rationality, etc.). That's cool stuff.

And economists, even if their research is highly specialized, are encouraged to think about all different kinds of topics in the field, and encouraged to think freely and originally. That's something few people appreciate. In a lab science, in contrast, you are encouraged to burrow down in your area of hyper-specialization.

In econ, furthermore, you get exposed to a bunch of different disciplines; you get to learn some statistics, a little math, some sociology, a bit of psychology, and maybe even some history.

Also, as an economist, your status as an intellectual will not disappear when you get a job. Even if you go to work as a consultant or a financier, your thoughts will be welcomed and considered by economists in the blogosphere. And you can even publish econ papers as a non-academic.

In fact, it's also worth pointing out that econ is a field in which outsiders and mavericks are able to challenge the status quo. This is in spite of the econ profession's well-known deference to intellectual authority figures. The simple fact is that econ, you don't need money to advance new ideas, as you do in biology or chemistry. And you don't need math wizardry either, as you would if you wanted to introduce new ideas in physics.


Reason 4: The risk of failure is low.

In economics PhD programs, the main risk of failure is not passing your prelim exams. This happens to a substantial fraction of people who get admitted to econ programs (maybe 25% or fewer at Michigan). But if you flunk out, you get a complimentary Master's degree, which is probably worth the 2 years that you'll have spent in the program. And after you pass the prelims, there is little risk of not finishing a dissertation; unlike in most fields, you do not have to publish to graduate.


Caveats about the econ PhD

Of course, I don't want to make it seem like the econ PhD is an utterly dominant strategy for life fulfillment. There are some caveats that you should definitely take into account.

First, there is the fact that an econ PhD program is still a PhD program. That means, first of all, that you will be in poverty in your late 20s. That is not fun for most people (some "lifestyle PhD" students and bohemian artists excepted). Also, econ PhD programs force you to manage your own time, while giving you very little feedback about how well or badly you're actually doing. That can be stressful and depressing.

Second, be aware that the culture of economics is still fairly conservative, and not in the good way. Econ is one of the few places in our society where overtly racist and sexist ideas are not totally taboo (Steve Landsburg is an extreme example, but that gives you the general flavor). Discrimination against women, in particular, probably still exists, though I'd say (or I'd hope, anyway) that it's on the wane.

Finally, there is the fact that if enough people read and believe this blog post (ha!), it will cease to be true. There's a piece of economics for you: as soon as people become aware that a thing's value is greater than its price, they will start bidding up the price. But information diffuses slowly. Expect the econ PhD to lose its luster in five to ten years, but that still gives you a window of time.


Anyway, despite these caveats, the econ PhD still seems like quite a sweet deal to me. And compared to a hellish, soul-crushing, and economically dubious lab science PhD, econ seems like a slam dunk. There are very few such bargains left in the American labor market. Grab this one while it's still on the shelves.


Update: Here's a 1999 paper documenting that the econ PhD is, economically speaking, a really good deal. Also, here is Bryan Caplan saying some very similar things back in 2005.

Update 2: A grad student friend writes:

[E]ven going to the abysmally ranked [econ]department that I go to I have no worries at all about getting a good job after I graduate. It may not be an academic job, but that's fine by me (or if it's an academic job it might be in a policy department rather than an econ department).

Another anecdote supporting the thesis that even econ PhDs at low-ranked schools don't worry much about employment...