Friday, April 5, 2013

What you can learn from DSGE

                                       *** UPDATE BELOW ***

Anyone who has read much of this blog would expect my answer to the above question to be "NOTHING AT ALL!!!!!!!!!!!!!!!!!" Its true, I'm not a fan at all of Dynamic Stochastic General Equilibrium models, and think they offer poor tools for exploring the behaviour of any economy. That said, I also think economists should be ready and willing to use any model whatsoever if they honestly believe it might give some real practical insight into how things work. I (grudgingly) suppose that DSGE models might sometimes fall into this category.

So that's what I want to explore here, and I do briefly below. But first a few words on what I find objectionable about DSGE models.

The first thing is that the agents in such models are generally assumed to be optimisers. They have a utility function and are assumed to maximize this utility by solving some optimization problem over a path in time. [I'm using as my model the well known Smets-Wouters model as described in this European Central Bank document written, fittingly enough, by Smets and Wouters.] Personally, I find this to be a rather hugely implausible account of how any person or firm makes decisions when facing anything but the simplest problems. So it would seem like a miracle to me if the optimal behaviors predicted by the models would turn out to resemble even crudely the behavior of real individuals or firms.

Having said that, if I try to be generous, I can suppose that maybe, just maybe, the actual behaviour of people, while it isn't optimizing anything, might in the aggregate come out to something that isn't at least too far away from the optimal behavior, at least in some cases. I would guess there must be armies of economists out there collecting data on just this question, comparing the actions of real individuals and firms to the optimal predictions of the models. Maybe it isn't always bad. If I twist my arm, I can accept that this way of treating decision making as optimization sometimes lead to interesting insights (for people facing very smple decisions, this would of course be more likely).

The second thing I find bad about DSGE models is their use of the so-called representative agent. In the Smets-Wouters model, for example, there is essentially one representative consumer who makes decisions regarding labor and consumption, and then one representative firm which makes decisions on investment, etc. If you read the paper you will see it mention "a continuum of households" indexed by a continuous parameter, and this makes it seem at first like there is actually an infinite number of agents. Not really, as the index only refers to the kind of labor. Each agent makes decisions independently to optimize their utility; there are no interactions between the agents, no one can conduct a trade with another or influence their behavior, etc. So in essence there is really just one representative laborer and one representative firm, who interact with one another in the market. This I also find wholly unconvincing as the real economy emerges out of the complex interactions of millions of agents doing widely different things. Modelling an economy like this seems like modelling the flow of a river by thinking about the behaviour of a single representative water molecule, bouncing along the river bed, rather then thinking about the interactions of many which create pressure, eddies, turbulence, waves and so on. It seems highly unlikely to be very instructive.

But again, let me be generous. Perhaps, in some amazing way, this unbelievably crude approximation might sometimes give you some shred of insight. Maybe you can get lucky and find that a collective outcome can be understood by simply averaging over the behaviors of the many individuals. In situations where people do make up their own minds, independently and by seeking their own information, this might work. Perhaps this is how people behave in response to their perceptions of the macroeconomy, although it seems to me that what they hear from others, what they read and see in the media, probably has a huge effect and so they don't act independently at all.

But maybe you can still learn something from this approximation, sometimes. Does anyone out there know if there is research exploring this matter of when or under what conditions the representative agent approximation is OK because people DO act independently? I'm sure this must exist and it would be interesting to know more about it. I guess the RBC crowd must have an extensive program studying the empirical limits to the applicability of this approximation? 

So, those are my two biggest reasons for finding it hard to believe the DSGE framework. To these I might add a disbelief that the agents in economy do rapidly find their way to an equilibrium in which "production equals demand by households for consumption and investment and the government." We might stay well away from that point, and things might generally change so quickly that no equilibrium ever comes about. But let's ignore that. Maybe we're lucky and the equilibrium does come about.

So then, what can we learn from DSGE, and why this post? If I toss aside the worries I've voiced above, I'm willing to entertain the possibility that one might learn something from DSGE models. In particular, while browsing the web site of Nathan Palmer, a PhD student in the Department of Computational Social Science at George Mason University, I came across mention of two lines of work within the context of the DSGE formalism that I do think are interesting. I think more people should know about them.

First is work exploring the idea of "natural expectations." A nice example is this fairly recent paper by Andreas Fuster, David Laibson, and Brock Mendel. Most DSGE models, including the Smets-Wouters model, assume that the representative agents have rational expectations, i.e. they process information perfectly and have a wholly unbiased view of future possibilities. What this paper does is to relax that assumption in a DSGE model, assuming instead that people have more realistic "natural" or "intuitive expectations." Look at the empirical literature and you find that there's lots of evidence that investors and people of all kinds tend to overestimate recent trends in time series and expect them to continue. This paper explores some of this empirical literature, but then goes to its main purpose -- to include these trend following expectations into a DSGE model.

As they note, a seminal failure of rational expectations DSGE models is that they struggle "to explain some of the most prominent facts we observe in macroeconomics, such as large swings in asset prices, in other words “bubbles”, as well as credit cycles, investment cycles, and other mechanisms that contribute to the length and severity of economic contractions." These kinds of things, in contrast, do emerge quite readily from a DSGE model once the expectations of the agents is made a little more realistic. From the paper:
.....we embed natural expectations in a simple dynamic macroeconomic model and compare the simulated properties of the model to the available empirical evidence. The model’s predictions match many patterns observed in macroeconomic and financial time series, such as high volatility of asset prices, predictable up‐and‐down cycles in equity returns, and a negative relationship between current consumption growth and future equity returns.   
That is interesting, and all from a DSGE model. Whether you believe it or not depends on what you think about the objections I voiced above about the components of DSGE models, but it is at least nice that this single step towards realism pays some nice dividends in giving more plausible outcomes. This is a useful line of research.

Related work, equally interesting, is that of Paolo Gelain, Kevin J. Lansing and Caterina Mendicino, described in this working paper of the Federal Reserve Bank of San Francisco. This paper essentially does much the same thing as the one I just discussed, though in the context of the housing market. It uses a DSGE with trend following expectations for some of the agents to explore how a government might best try to keep housing bubbles in check through change in interest rates or restrictions on  leverage, i.e. how much a potential home buyer can borrow relative to the house value, or restrictions on how much they can borrow relative to income. The latter seems to work best. As they summarize:
Standard DSGE models with fully-rational expectations have difficulty producing large swings in house prices and household debt that resemble the patterns observed in many industrial countries over the past decade. We show that the introduction of simple moving-average forecast rules for a subset of agents can significantly magnify the volatility and persistence of house prices and household debt relative to otherwise similar model with fully-rational expectations. We evaluate various policy actions that might be used to dampen the resulting excess volatility, including a direct response to house price growth or credit growth in the central bank’s interest rate rule, the imposition of a more restrictive loan-to-value ratio, and the use of a modified collateral constraint that takes into account the borrower’s wage income. Of these, we find that a debt-to-income type constraint is the most effective tool for dampening overall excess volatility in the model economy. 
Again, this is really interesting stuff, worthwhile research, economics that is moving, to my mind, in the right direction, showing us what we should expect to be possible in an economy once we take the realistic and highly heterogenous behaviour of real people into account.

So there. I've said some not so nasty things about DSGE models! Now I think I need a stiff drink.

*** UPDATE ***

One other thing to mention. I'm happy to see this kind of work, and I applaud those doing it. But I do seriously doubt whether embedding the idea of trend following inside a DSGE model does anything to teach us about why markets often undergo bubble-like phenomena and have quite strong fluctuations in general. Does the theoretical framework add anything?

Imagine someone said the following to you:
 "Lots of people, especially in financial markets and the housing market, are prone to speculating and buying in the hope of making a profit when prices go up. This becomes more likely if people have recently seen prices rising, and their friends making profits. This situation  can lead to herding type behavior where many people act similarly and create positive feedbacks and asset bubbles, which eventually crash back to reality. The problem is generally made worse, for obvious reasons, if people can borrow very easily to leverage their investment..." 
I think most people would say "yes, of course." I suspect that many economists would also. This explanation, couched in words, is for me every bit as convincing as the similar dynamic wrapped up in the framework of DSGE. Indeed, it is even more convincing as it doesn't try to jump awkwardly through a series of bizarre methodological hoops along the way. In this sense, DSGE seems more like a straitjacket than anything else. I can't see how it adds anything to the plausibility of a story.

So, I guess, sorry for the title of this post. Should have been "What you can learn from DSGE: things you would be much better off learning elsewhere."


  1. Modelling an economy like this seems like modelling the flow of a river by thinking about the behaviour of a single representative water molecule, bouncing along the river bed, rather then thinking about the interactions of many which create pressure, eddies, turbulence, waves and so on.

    This doesn't sound quite fair. I know nothing about hydrology, but I'd guess that when those guys want to model "aggregate" river flows, they don't build 3D mesh of whole river bed and solve Navier-Stokes equations for every wave and turbulence either. DSGE models are aimed at explaining fluctuations in macroeconomic aggregates, at say quarterly frequency, and naturally will abstract from lots of finer details, especially those happening on shorter timescales. For example, the Flash Crash surely tells us something about the market microstructure and HFT, but it didn't have any macroeconomic consequences.

    But yeah, some assumptions, like representative agent, are hardly satisfying. Then again, maybe heterogeneity matters for some things and not for others. There are many papers with heterogeneity, but usually they have narrow focus and deal with some specific question (some surveys - Guvenen: "Macroeconomics with Heterogeneity: A Practical Guide", or Heathcote, Storesletten & Violante: "Quantitative Macroeconomics with Heterogeneous Households", should be easy to find through G. Scholar).

    1. Ivan,

      Fair point... in hydrology (about which I don't know too much either) or in any area of physics, you generally have a hierarchy of different kinds of models, useful for different purposes. You don't include details when you have good reason to think you can get by without them (or you don't include them, hope for the best, test your theory, and admit it doesn't work if that is the case). Chemistry is chemistry and doesn't need to employ all the nitty gritty of atomic physics or quantum electrodynamics except in rare cases (processes over very short timescales, coherent transport in photoreceptors, etc.) The same should obviously also be the case in economics.

      The question then is what to include and what to ignore. In physics perhaps we have the luxury of often being able to derive large scale simply models as approximations to models including more detail at lower scales. Take detailed Navier-Stokes and integrate over lots of fast dynamics on short spatial scales and you get more coarse grained fluid equations capturing crude relations such as conservation of energy and momentum while losing the finer detail. But you have reason to believe the model should work as an approximation. In economics it would seem to me that the goal should be similar. If you're going to use a model with two representative agents, do you have confidence that, for the purposes you are using it, you aren't ignoring lots of interesting dynamics in doing so? The question ultimately hinges on how you answer that question.

    2. I think it would behoove you to learn something about economics before speaking in public about it. Maybe cite the people actually doing good work on incomplete information, not the two crap papers that you picked off a website of a GMU CompSci student who knows nothing about modern economics.

  2. Some interesting commentary on your post here:

  3. Let me start off by saying: DSGE, for everything you say about it, sadly has more apparent insight/merit still than anything econophysics has ever produced. I don't see how a theoretical framework-- which essentially says of consumers that they will rationally select the single best deal (utility) they can find from their "consumption set"-- is somehow less plausible than simply fitting power law parameters to data in an incredibly ad-hoc fashion and claiming it's science, much less an improvement.

    It's of course blatantly unfair to characterize DGE models in general as useless: they are quite helpful at modeling policy reform!!

    For one thing, you surely understand the concept of modeling something *as if* they were maximizing the discounted value of present and future utility, rather than actually thinking that agents actually bust out a calculator and do such a high-dimensional dynamic programming problem. No, I would not say it is not at all absurd to assume that agents behave *as if* they are trying to get the most possible value for their dollar, essentially. You can add in asymmetric information frictions and transaction costs to reflect why consumers often appear to make sub-optimal decisions, which economists have in fact done with some achievement. This brings me to my next point...

    This was even mentioned in that forum link above by Anonymous, but the author appears to ignore volumes of economic and macroeconomic research on the topic. There are multiple models of heterogenous agents which add flavors of what you might term "realism" and do produce slightly different outcomes. However, surely you remember how physicists, too, often reach for a simpler model- the model of the cannonball that doesn't factor in wind resistance- as a first-order-approximation of what the answer looks like. Economists do the exact same, the difference being people rarely assume physicists lack anything more advanced.


    A search on the largest economic research paper database, the economic equivalent of googling it, and let's see what we have just from the first few results:

    -"Behavioral biases and representative agent" Elyès Jouini & Clotilde Napp (2012)

    -"Can a Representative-Agent Model Represent a Heterogeneous-Agent Economy?" Sungbae An & Yongsung Chang & Sun-Bin Kim (2008)

    -"On the performance of the representative agent during out-of-equilibrium dynamics" Zakaria Babutsidze (2011)

    There are even search results on the topic, which predate the 21st century.


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