by ‘Deep parameters’ and microfoundations In a post last week, Simon Wren-Lewis was discussing if modern academic macroeconomics is eclectic or not. When it comes to methodology it seems as though his conclusion is that it is not: The New Classical Counter Revolution of the 1970s and 1980s … was primarily a revolution about methodology, about arguing that all models should be microfounded, and in terms of mainstream macro it was completely successful. It also tried to link this to a revolution about policy, about overthrowing Keynesian economics, and this ultimately failed. But perhaps as a result, methodology and policy get confused. Mainstream academic macro is very eclectic in the range of policy questions it can address, and conclusions it can arrive at, but in terms of methodology it is quite the opposite. In an earlier post he elaborated on why the New Classical Counterrevolution was so successful in replacing older theories, despite the fact that the New Classical models weren’t 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.
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‘Deep parameters’ and microfoundations
In a post last week, Simon Wren-Lewis was discussing if modern academic macroeconomics is eclectic or not. When it comes to methodology it seems as though his conclusion is that it is not:
The New Classical Counter Revolution of the 1970s and 1980s … was primarily a revolution about methodology, about arguing that all models should be microfounded, and in terms of mainstream macro it was completely successful. It also tried to link this to a revolution about policy, about overthrowing Keynesian economics, and this ultimately failed. But perhaps as a result, methodology and policy get confused. Mainstream academic macro is very eclectic in the range of policy questions it can address, and conclusions it can arrive at, but in terms of methodology it is quite the opposite.
In an earlier post he elaborated on why the New Classical Counterrevolution was so successful in replacing older theories, despite the fact that the New Classical models weren’t 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!
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’.
I fail to see why.
Wren-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.
The predominant strategy in mainstream macroeconomics today is to build models and make things happen in these ‘analogue-economy models.’ But although macro-econometrics may have supplied economists with rigorous replicas of real economies, if the goal of theory is to be able to make accurate forecasts or explain what happens in real economies, this ability to — ad nauseam — construct toy models, does not give much leverage.
‘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.
And — applying a ‘Lucas critique’ on New Classical and ‘New Keynesian’ models, it is obvious that they too fail.
Changing ‘policy rules’ cannot just be presumed not to influence investment and consumption behavior and a fortiori technology, thereby contradicting the invariance assumption. Technology and tastes cannot live up to the status of an economy’s deep and structurally stable Holy Grail. They too are part and parcel of an ever-changing and open economy. Lucas hope of being able to model the economy as ‘a FORTRAN program’ and ‘gain some confidence that the component parts of the program are in some sense reliable prior to running it’ therefore seems – from an ontological point of view – totally misdirected. The failure in the attempt to anchor the analysis in the alleged stable deep parameters ‘tastes’ and ‘technology’ shows that if you neglect ontological considerations pertaining to the target system, ultimately reality gets its revenge when at last questions of bridging and exportation of model exercises are laid on the table.
People like Dani Rodrik and Simon Wren-Lewis are proud of having an ever-growing smorgasbord of models to cherry-pick from (as long as, of course, the models do not question the standard modeling strategy) when performing their analyses. The ‘rigorous’ and ‘precise’ deductions made in these closed models, however, are not in any way matched by a similar stringency or precision when it comes to what ought to be the most important stage of any research — making statements and explaining things in real economies. Although almost every mainstream economist holds the view that thought-experimental modeling has to be followed by confronting the models with reality — which is what they indirectly want to predict/explain/understand using their models — they all of a sudden become exceedingly vague and imprecise. It is as if all the intellectual force has been invested in the modeling stage and nothing is left for what really matters — what exactly do these models teach us about real economies.
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.