On DSGE and the art of using absolutely ridiculous modeling assumptions23 January, 2014 at 23:08 | Posted in Economics, Theory of Science & Methodology | 4 Comments
Reading some of the comments — by Noah Smith, David Andolfatto and others — on my post Why Wall Street shorts economists and their DSGE models, I — as usual — get the feeling that mainstream economists when facing anomalies think that there is always some further “technical fix” that will get them out of the quagmire. But are these elaborations and amendments on something basically wrong really going to solve the problem? I doubt it. Acting like the baker’s apprentice who, having forgotten to add yeast to the dough, throws it into the oven afterwards, simply isn’t enough.
When criticizing the basic workhorse DSGE model for its inability to explain involuntary unemployment, some DSGE defenders maintain that later elaborations — e.g. newer search models — manage to do just that. I strongly disagree. One of the more conspicuous problems with those “solutions,” is that they — as e.g. Pissarides’ ”Loss of Skill during Unemployment and the Persistence of Unemployment Shocks” QJE (1992) — are as a rule constructed without seriously trying to warrant that the model immanent assumptions and results are applicable in the real world. External validity is more or less a non-existent problematique sacrificed on the altar of model derivations. This is not by chance. For how could one even imagine to empirically test assumptions such as Pissarides’ ”model 1″ assumptions of reality being adequately represented by ”two overlapping generations of fixed size”, ”wages determined by Nash bargaining”, ”actors maximizing expected utility”,”endogenous job openings”, ”jobmatching describable by a probability distribution,” without coming to the conclusion that this is — in terms of realism and relevance — nothing but nonsense on stilts?
The whole strategy reminds me not so little of the following little tale:
Time after time you hear people speaking in baffled terms about mathematical models that somehow didn’t warn us in time, that were too complicated to understand, and so on. If you have somehow missed such public displays of throwing the model (and quants) under the bus, stay tuned below for examples.
But this is far from the case – most of the really enormous failures of models are explained by people lying …
A common response to these problems is to call for those models to be revamped, to add features that will cover previously unforeseen issues, and generally speaking, to make them more complex.
For a person like myself, who gets paid to “fix the model,” it’s tempting to do just that, to assume the role of the hero who is going to set everything right with a few brilliant ideas and some excellent training data.
Unfortunately, reality is staring me in the face, and it’s telling me that we don’t need more complicated models.
If I go to the trouble of fixing up a model, say by adding counterparty risk considerations, then I’m implicitly assuming the problem with the existing models is that they’re being used honestly but aren’t mathematically up to the task.
If we replace okay models with more complicated models, as many people are suggesting we do, without first addressing the lying problem, it will only allow people to lie even more. This is because the complexity of a model itself is an obstacle to understanding its results, and more complex models allow more manipulation …
I used to work at Riskmetrics, where I saw first-hand how people lie with risk models. But that’s not the only thing I worked on. I also helped out building an analytical wealth management product. This software was sold to banks, and was used by professional “wealth managers” to help people (usually rich people, but not mega-rich people) plan for retirement.
We had a bunch of bells and whistles in the software to impress the clients – Monte Carlo simulations, fancy optimization tools, and more. But in the end, the bank’s and their wealth managers put in their own market assumptions when they used it. Specifically, they put in the forecast market growth for stocks, bonds, alternative investing, etc., as well as the assumed volatility of those categories and indeed the entire covariance matrix representing how correlated the market constituents are to each other.
The result is this: no matter how honest I would try to be with my modeling, I had no way of preventing the model from being misused and misleading to the clients. And it was indeed misused: wealth managers put in absolutely ridiculous assumptions of fantastic returns with vanishingly small risk.