## DSGE models are missing the point

20 Nov, 2017 at 13:41 | Posted in Economics | 3 CommentsIn a recent attempt to defend DSGE modelling, Lawrence Christiano, Martin Eichenbaum and Mathias Trabandt have to admit that DSGE models have failed to predict financial crises. The reason they put forward for this is that the models did not “integrate the shadow banking system into their analysis.” That certainly is true — but the DSGE problems go much deeper than that:

A typical modern approach to writing a paper in DSGE macroeconomics is as follows:

o to establish “stylized facts” about the quantitative interrelationships of certain macroeconomic variables (e.g. moments of the data such as variances, autocorrelations, covariances, …) that have hitherto not been jointly explained;

o to write down a DSGE model of an economy subject to a defined set of shocks that aims to capture the described interrelationships; and

o to show that the model can “replicate” or “match” the chosen moments when it is fed with stochastic shocks generated by the assumed shock process …

However, the test imposed by matching DSGE models to the data is problematic in at least three respects:

First, the set of moments chosen to evaluate the model is largely arbitrary …

Second, for a given set of moments, there is no well-defined statistic to measure the goodness of fit of a DSGE model or to establish what constitutes an improvement in such a framework …

Third, the evaluation is complicated by the fact that, at some level, all economic models are rejected by the data … In addition, DSGE models frequently impose a number of restrictions that are in direct conflict with micro evidence. If a model has been rejected along some dimensions, then a statistic that measures the goodness-of-fit along other dimensions is meaningless …

Focusing on the quantitative fit of models also creates powerful incentives for researchers (i) to introduce elements that bear little resemblance to reality for the sake of achieving a better fit (ii) to introduce opaque elements that provide the researcher with free (or almost free) parameters and (iii) to introduce elements that improve the fit for the reported moments but deteriorate the fit along other unreported dimensions.

Albert Einstein observed that “not everything that counts can be counted, and not everything that can be counted counts.” DSGE models make it easy to offer a wealth of numerical results by following a well-defined set of methods (that requires one or two years of investment in graduate school, but is relatively straightforward to apply thereafter). There is a risk for researchers to focus too much on numerical predictions of questionable reliability and relevance that absorb a lot of time and effort rather than focusing on deeper conceptual questions that are of higher relevance for society.

Great essay, showing that ‘rigorous’ and ‘precise’ DSGE models cannot be considered anything else than unsubstantiated conjectures as long as they aren’t supported by evidence from outside the theory or model. To my knowledge no in any way decisive empirical evidence has been presented.

To reply to Korinek’s devastating critique — as do Christiano et al. — with pie-in-the-sky formulations such as ‘young cutting-edge researchers having promising extensions of the model in the pipeline’ or claiming that “there is no credible alternative,” cannot be the right scientific attitude. No matter how precise and rigorous the analysis, and no matter how hard one tries to cast the argument in modern mathematical form, DSGE models do not push economic science forwards one single millimetre if they do not stand the acid test of relevance to the target. No matter how clear, precise, rigorous or certain the inferences delivered inside these models are, they do not say anything about real-world economies.

Proving things ‘rigorously’ in DSGE models is at most a starting point for doing an interesting and relevant economic analysis. Forgetting to supply export warrants to the real world makes the analysis an empty exercise in formalism without real scientific value.

Mainstream economists think there is a gain from the DSGE style of modelling in its capacity to offer some kind of structure around which to organise discussions. To me, that sounds more like a religious theoretical-methodological dogma, where one paradigm rules in divine hegemony. That’s not progress. That’s the death of economics as a science.

As Korinek argues, using DSGE models “creates a bias towards models that have a well-behaved ergodic steady state.” Since we *know* that most real-world processes do *not* follow an ergodic distribution, this is, to say the least, problematic. To understand real world ‘non-routine’ decisions and unforeseeable changes in behaviour, stationary probability distributions are of no avail. In a world full of genuine uncertainty — where real historical time rules the roost — the probabilities that ruled the past are not those that will rule the future. Imposing invalid probabilistic assumptions on the data make *all* DSGE models statistically misspecified.

Advocates of DSGE modelling want to have deductively automated answers to fundamental causal questions. But to apply ‘thin’ methods we have to have ‘thick’ background knowledge of what’s going on in the real world, and not in idealized models. Conclusions can only be as certain as their premises — and that also applies to the quest for causality and forecasting predictability in DSGE models.

If substantive questions about the real world are being posed, it is the formalistic-mathematical representations utilized that have to match reality, not the other way around. The modelling convention used when constructing DSGE models makes it impossible to fully incorporate things that we know are of paramount importance for understanding modern economies — such as income and wealth inequality, asymmetrical power relations and information, liquidity preference, just to mention a few.

Given all these fundamental problems for the use of these models and their underlying methodology, it is beyond understanding how the DSGE approach has come to be the standard approach in ‘modern’ macroeconomics. DSGE models are based on assumptions profoundly at odds with what we know about real-world economies. That also makes them little more than overconfident story-telling devoid of real scientific value. Macroeconomics would do much better with more substantive diversity and plurality.

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DSGE models embody a story-telling that seems to me to be inexplicably boring. What do these people find interesting in or about these highly stylized jumbles of statistics? I think I would find the discussions at a convention of Sudoku puzzle designers more interesting and of more constructive relevance to understanding the working of the actual economy.

Comment by Bruce Wilder— 21 Nov, 2017 #

I’ve never understood the ‘the maths is beautiful’ refrain. It’s boring and ugly.

Comment by Unlearningecon— 24 Nov, 2017 #

If they are going to include shadow banking, then they must deal with the hundreds of trillions of dollars in derivatives that the world financial sector creates as they wish. See https://www.bis.org/publ/otc_hy1612/triensurvstatannex.pdf

Notional amounts of the global OTC derivatives market totaled $583 trillion in 2010, $696 trillion in 2013, and $544 trillion in 2016. Notional amounts appear as assets on bank balance sheets and are used as collateral for loans. The more assets you show, the better terms you get.

These huge sums eclipse any real economy figures such as world GDP, which is maybe $80 trillion. The financial sector is huge, at least an order of magnitude greater than anything DSGE economists try to model. It’s like modeling the universe without including dark energy or dark matter.

Note the drop from $696 trillion in 2013 to $544 trillion in 2016. I suspect the banks are responding to new capital controls and other regulations by moving their money creation operations into currency swaps, which aren’t regulated. According to a BIS paper https://www.bis.org/publ/qtrpdf/r_qt1709e.pdf , at least $10 trillion is in currency swaps and “missing” from statistical reporting. I suspect the figure is much higher than $10 trillion because bankers are doing what bankers do, getting around regulations and obfuscating their wanton money creation.

I don’t think $150 trillion simply disappeared off bank balance sheets between 2013 and 2016. I think they moved it into hard-to-find areas such as currency swaps and other exotic instruments.

The upshot for economists is that huge, vast sums of dollars are being created outside of the real economy, and only very loosely tied to real output. But no economists include money creation on such a large scale. Yet the figures are there.

Comment by Robert Mitchell— 21 Nov, 2017 #