Home > Uncategorized > Macroeconomic just-so stories you really do not want to buy

Macroeconomic just-so stories you really do not want to buy

from Lars Syll

pinnocThus your standard New Keynesian model will use Calvo pricing and model the current inflation rate as tightly coupled to the present value of expected future output gaps. Is this a requirement anyone really wants to put on the model intended to help us understand the world that actually exists out there? Thus your standard New Keynesian model will calculate the expected path of consumption as the solution to some Euler equation plus an intertemporal budget constraint, with current wealth and the projected real interest rate path as the only factors that matter. This is fine if you want to demonstrate that the model can produce macroeconomic pathologies. But is it a not-stupid thing to do if you want your model to fit reality?

I remember attending the first lecture in Tom Sargent’s evening macroeconomics class back when I was in undergraduate: very smart man from whom I have learned the enormous amount, and well deserving his Nobel Prize. But…

He said … we were going to build a rigorous, micro founded model of the demand for money: We would assume that everyone lived for two periods, worked in the first period when they were young and sold what they produced to the old, held money as they aged, and then when they were old use their money to buy the goods newly produced by the new generation of young. Tom called this “microfoundations” and thought it gave powerful insights into the demand for money that you could not get from money-in-the-utility-function models.

I thought that it was a just-so story, and that whatever insights it purchased for you were probably not things you really wanted to buy. I thought it was dangerous to presume that you understood something because you had “microfoundations” when those microfoundations were wrong. After all, Ptolemaic astronomy had microfoundations: Mercury moved more rapidly than Saturn because the Angel of Mercury left his wings more rapidly than the Angel of Saturn and because Mercury was lighter than Saturn…

Brad DeLong

Brad DeLong is of course absolutely right here, and one could only wish that other mainstream economists would listen to him … 

Oxford macroeconomist Simon Wren-Lewis elaborates in a post on his blog on why he thinks the New Classical Counterrevolution was so successful in replacing older theories, despite the fact that the New Classical models were not able to explain what happened to output and inflation in the 1970s and 1980s:

The new theoretical ideas New Classical economists brought to the table were impressive, particularly to those just schooled in graduate micro. Rational expectations is the clearest example …

However, once the basics of New Keynesian theory had been established, it was quite possible to incorporate concepts like rational expectations or Ricardian Eqivalence into a traditional structural econometric model (SEM) …

The real problem with any attempt at synthesis is that a SEM is always going to be vulnerable to the key criticism in Lucas and Sargent, 1979: without a completely consistent microfounded theoretical base, there was the near certainty of inconsistency brought about by inappropriate identification restrictions …

So why does this matter? … If mainstream academic macroeconomists were seduced by anything, it was a methodology – a way of doing the subject which appeared closer to what at least some of their microeconomic colleagues were doing at the time, and which was very different to the methodology of macroeconomics before the New Classical Counterrevolution. The old methodology was eclectic and messy, juggling the competing claims of data and theory. The new methodology was rigorous!

Unlike Brad DeLong, Wren-Lewis seems to be impressed by the ‘rigour’ brought to macroeconomics by the New Classical counterrevolution and its rational expectations, microfoundations and ‘Lucas Critique’.

It is difficult to see why.

3634flimWren-Lewis’s ‘portrayal’ of rational expectations is not as innocent as it may look. Rational expectations in the neoclassical economists’s 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 the ‘New Keynesian’ models doesn’t take us a 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’ New Classical models — and that goes for the ‘New Keynesian’ variety too — 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 per se say anything about real world economies.

Proving things ‘rigorously’ in mathematical 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.

  1. April 26, 2016 at 7:14 am

    Great summary of the problems. But you miss the real point. Professional economics, at least since the turn to classicalism and even more so since the take over by neo-classicalism is not about studying the economics invented by the “person on street.” At best those guys are dumb and brutish and certainly not very interesting. No, professional economics is about inventing an economy which these dumb brutes are then taught so they have some small chance of becoming something other than dumb brutes. Based on the whole of human history people do not have a tendency to “truck and barter.” But professional economists are convinced that the world and people in it will be better off if they do indeed have a tendency to “truck and barter.” And these economists have made it their goal to create just this tendency.

  2. April 26, 2016 at 7:25 pm

    “True science and understanding only advances in my field when you have experiments, or experimental data. If models are all you’re doing, you’re not advancing knowledge.”

    So says Dr. Joyce Penner, Distinguished University Professor of Atmospheric Science at the University of Michigan in the recent issue of The Michigan Engineer. It would appear that even in the hard sciences the empirical basis of true science needs to be mentioned now and then. I presume that it does not fall on deaf ears in her field of science.

    • April 26, 2016 at 7:58 pm

      One of my jobs is model construction. When I testify my first comment, before the question comes to me on cross examination is — this model is wrong, I just don’t know in which direction or by what amount. In fact, all models are wrong in this way. But models can be useful tools for encouraging research or just brain storming. Then the interrogation of the details of the particular model for the hearing begins. So I agree with Dr. Penner!

    • David Chester
      April 27, 2016 at 9:39 am

      But we cannot experiment with the national economy, all we can do is to compare our forecasts of a past situation with what actually occurs. Usually the situation in a real economy is so complicated that we need a great deal of averaging and placing of quantities into idealized groups, before this kind of comparison can start. Can anyone suggest a good simulation program that might apply here?

      • April 28, 2016 at 4:43 am

        There are right now over 100 models used by such folks as the Federal Reserve, Bank of England, Deutsche Bank, Federal Reserve Bank of NY, etc. to forecast and predict the US and world economies. These run from the straight DSGE model (multiple variable optimization) at the NY Fed. to the system dynamics model at MIT’s Jay W. Forrester institute which in 2012 predicted a world-wide global economic collapse within 20 years. The math runs from simple (optimization, multi-variant) to 2nd order differential equations with feedback models like at MIT. Take your pick. None of them gets any better than 20-25% accuracy and all do poorly in dealing with out of ordinary (so called “Black Swan”) events. Some friends and I once estimated the number of feedback loops required to model world economic interactions. We gave up at 10,000. There are computers than can handle even such complex models, but no one I know can set up one. Right now so called “chaotic” models are making a run at dealing with such events as the expansion of the universe and the interactions of subatomic particles as they approach the speed of light. These could probably be applied to economics. But I have no idea on where to even begin to set up the relationships in the model for such work.

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