Now it is “dynamic stochastic general equilibrium” (DSGE) models inspired by the Lucas critique that have failed to predict or even explain the Great Recession of 2007–2009. More precisely, the implicit “explanations” based on these models are that the recession, including the millions of net jobs lost, was primarily due to large negative shocks to both technology and willingness to work … So can the reputation of modern macroeconomics be rehabilitated by simply modifying DSGE models to include a few more realistic shocks? …

A simple example helps illustrate for the uninitiated just how DSGE models work and why it should come as little surprise that they are largely inadequate for the task of explaining the Great Recession.

For this simple DSGE model, consider the following technical assumptions: i) an infinitely-lived representative agent with rational expectations and additive utility in current and discounted future log consumption and leisure; ii) a Cobb-Douglas aggregate production function with labor-augmenting technology; iii) capital accumulation with a fixed depreciation rate; and iv) a stochastic process for exogenous technology shocks …

wrong-tool-by-jerome-awIt is worth making two basic points about the setup. First, by construction, technology shocks are the only underlying source of fluctuations in this simple model. Thus, if we were to assume that U.S. real GDP was the literal outcome of this model, we would be assuming a priori that fluctuations in real GDP were ultimately due to technology. When faced with the Great Recession, this model would have no choice but to imply that technology shocks were somehow to blame. Second, despite the underlying role of technology, the observed fluctuations in real GDP can be divided into those that directly reflect the behavior of the exogenous shocks and those that reflect the endogenous capital accumulation in response to these shocks.

To be more precise about these two points, it is necessary to assume a particular process for the exogenous technology shocks. In this case, let’s assume technology follows a random walk with drift [and assuming a 100% depreciation rate of capital]…

So, with this simple DSGE model and for typical measures of the capital share, we have the implication that output growth follows an AR(1) process with an AR coefficient of about one third. This is notable given that such a time-series model does reasonably well as a parsimonious description of quarterly real GDP dynamics for the U.S. economy …

However, the rather absurd assumption of a 100% depreciation rate at the quarterly horizon would surely still have prompted a sharp question or two in a University of Chicago seminar back in the days. So, with this in mind, what happens if we consider the more general case?

binary-options-bunny-tophat-magicianUnfortunately, for more realistic depreciation rates, we cannot solve the model analytically. Instead, taking a log-linearization around steady state, we can use standard methods to solve for output growth … This simple DSGE model is able to mimic the apparent AR(1) dynamics in real GDP growth. But it does so by assuming the exogenous technology shocks also follow an AR(1) process with an AR coefficient that happens to be the same as the estimated AR coefficient for output growth. Thus, the magic trick has been revealed: a rabbit was stuffed into the hat and then a rabbit jumped out of the hat …

Despite their increasing sophistication, DSGE models share one key thing in common with their RBC predecessors. After more than two decades of earnest promises to do better in the “future directions” sections of academic papers, they still have those serially-correlated shocks. Thus, the models now “explain” variables like real GDP, inflation, and interest rates as the outcome of more than just serially-correlated technology shocks. They also consider serially-correlated preference shocks and serially-correlated policy shocks …

James Morley

And still mainstream economists seem to be impressed by the ‘rigour’ brought to macroeconomics by New-Classical-New-Keynesian DSGE models and its rational expectations and micrcofoundations!

It is difficult to see why.

Take the rational expectations assumption. Rational expectations in the mainstream economists’ world implies that relevant distributions have to be time independent. This amounts to assuming that an economy is like a closed system with known stochastic probability distributions for all different events. In reality it is straining one’s beliefs to try to represent economies as outcomes of stochastic processes. An existing economy is a single realization tout court, and hardly conceivable as one realization out of an ensemble of economy-worlds, since an economy can hardly be conceived as being completely replicated over time. It is — to say the least — very difficult to see any similarity between these modelling assumptions and the expectations of real persons. In the world of the rational expectations hypothesis we are never disappointed in any other way than as when we lose at the roulette wheels. But real life is not an urn or a roulette wheel. And that’s also the reason why allowing for cases where agents make ‘predictable errors’ in DSGE models doesn’t take us any closer to a relevant and realist depiction of actual economic decisions and behaviours. If we really want to have anything of interest to say on real economies, financial crisis and the decisions and choices real people make we have to replace the rational expectations hypothesis with more relevant and realistic assumptions concerning economic agents and their expectations than childish roulette and urn analogies.

‘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.

No matter how precise and rigorous the analysis, and no matter how hard one tries to cast the argument in modern mathematical form, they do not push economic science forwards one single millimeter 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 modeling 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.