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Steve Keen Defends Heterodox Macro Against Noah Smith

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Robust and compelling.


“Ideas like Minsky’s, with no equations”?

If it’s equations and Minsky you want, try this macroeconomics paper “Destabilizing a stable crisis: Employment persistence and government intervention in macroeconomics” (Costa Lima, Grasselli, Wang & Wu 2014). And I defy any Neoclassical to tell the authors (including mathematician Matheus Grasselli, whose PhD was entitled “Classical and Quantum Information Geometry“) that they lack the mathematical ability to understand Neoclassical models.

The mathematics used in heterodox papers like this one is in fact harder than that used by the mainstream, because it rejects a crucial “simplifying assumption” that mainstreamers routinely use to make their models easier to handle: imposing linearity on unstable nonlinear systems.

Imposing linearity on a nonlinear system is a valid procedure if, and only if, the equilibrium around which the model is linearized is stable. But the canonical model from which DSGE models were derived—Ramsey’s 1925 optimal savings model—has an unstable equilibrium that is similar to the shape of a horse’s saddle. Imagine trying to drop a ball onto a saddle so that it doesn’t slide off—impossible, no?

Not if you’re a “representative agent” with “rational expectations”! Neoclassical modelers assume that the “representative agents” in their models are in effect clever enough to be able to drop a ball onto the economic saddle and have it remain on it, rather than sliding off (they call it imposing a “transversality condition”).

The mathematically more valid approach is to accept that, if your model’s equilibria are unstable, then your model will display far-from-equilibrium dynamics, rather than oscillating about and converging on an equilibrium. This requires you to understand and apply techniques from complex systems analysis, which is much more sophisticated than the mathematics Neoclassical modelers use (see the wonderful free ChaosBook http://www.chaosbook.org/ for details). The upside of this effort though is that since the real world is nonlinear, you are much closer to capturing it with fundamentally nonlinear techniques than you are by pretending to model it as if it is linear.

Edited by zugzwang

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