Prejšnji teden sem objavil uvodnik Oliviera Blancharda, glavnega ekonomista IMF in neokeynesianca po ekonomski filozofiji, v katerem je povedal, da so sodobni neokeynesianski modeli (DSGE) odpovedali v času krize in da niso primerni za napovedovanje, ko se gospodarstvo nahaja v “temnih kotičkih”. DSGE modeli naj bi bili sicer še vedno primerni, ko imamo “mirno morje”, za “razburkano morje” pa potrebujemo specifične modele. Zato naj “cveti tisoč cvetov”.*

Noah Smith je lucidno podvomil v Blanchardove izjave. Sprašuje se, kako naj vemo, kateri model uporabiti v “normalnih časih”? Kako naj vemo, kateri model je primeren za katero situacijo? Lucidnost pa sledi v spoznanju, da je to bolj kot ne akademska debata, kjer se akademiki med seboj “prepucavajo” z drobnimi spremembami konkurenčnih modelčkov, ki pa nimajo nobene empirične veljave. Namreč, tisti, ki “delajo” ekonomsko politiko, teh sofisticiranih igračk ne jemljejo resno, pač pa temeljijo odločitve na “starih” (keynesianskih) ekonometričnih modelih, na intuiciji in lastni presoji. Tisti, ki delajo modelčke, pozabljajo na njihovo dejansko uporabnost.

There’s at least one theoretical problem with this idea: How do we know which standard model to use in normal times? For example, take the Smets-Wouters model, which is the main modern model used by central banks. How do we know the Smets-Wouters model is a good one? Well, people who like this model would say that it matches certain features of past business cycles — the variance of gross domestic products, the correlation of GDP and investment, and the like.

But what if some of those past business cycles were caused by the “dark corners”? How can we use Smets-Wouters to describe normal times — but not abnormal times — when its parameters are fit to data that includes both normal and abnormal times?

There is also at least one practical problem with Blanchard’s suggestion. Econ models — unlike models in, say, physics — don’t come with any guide to when to use them. To take a well-known example from physics, it’s easy to know when to use quantum mechanics — you use it when things are very small or very cold. If all you want to do is put a man on the moon, you don’t need QM.

Macro models aren’t like this. Suppose you have a model of a “dark corner” — the Diamond-Dybvig model of bank runs. When should a policy maker pay attention to this model? It isn’t clear. Blanchard hopes that macroeconomists will develop good quantitative indicators of systemic risk, but so far that is just a hope.

But all of this discussion is — quite literally — academic. In reality, what macroeconomists say in their papers has very little effect on what policy makers actually do.

Take fiscal stimulus, for example. Do you think U.S. representatives and senators, or the president, paid any attention to the academic literature on stimulus? Surely some advisers, such as Larry Summers and Christina Romer, knew the models. But in the end, the size and type of stimulus was determined by politics, not by the best academic estimates of the parameters of the most popular dynamic stochastic general equilibrium model.

One might hope that monetary policy would be a bit different. The Fed is staffed with academically trained economists who know how to sling Smets-Wouters or draw on Diamond-Dybvig. But actually, you see the Fed relying a lot on older models that academic macro gave up on long ago. You also see the Fed relying on pure judgment, especially when assessing the likelihood of one of Blanchard’s “dark corners.”

Is this a problem with policy makers or with academia? Are policy makers ignoring the useful advice of trained experts? Or have the experts focused so much on exploring ideas — on letting a hundred flowers bloom — that they have neglected to think hard about how those ideas might be practically applied?

When I look at the macro literature (as I still do, far more frequently than my job demands), I see a huge array of mutually exclusive models, most of which purport to explain the same set of facts, and none of which offers much insight on when you might want to use it. How is any policy maker supposed to draw insight from that morass? As in 2008, I think Blanchard’s view of his field is far too rosy.

Vir:  Noah Smith, BloombergView

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* Za tiste, ki ne spremljate makroekonomije, je pomembna še ta ta informacija, da je to že druga “ocena stanja” v akademski makroekononomiji v avtorstvu Blancharda v zadnjih šestih letih. Avgusta 2008, en mesec pred bankrotom Lehman Brothers, je namreč Blanchard v preglednem članku v NBER napisal, da je “stanje makroekonomije dobro” (mislil je na stanje akademske makro in na konvergenco med makroekonomisti glede glavnih predpostavk in metodologije). Natančneje:

For a long while after the explosion of macroeconomics in the 1970s, the field looked like a battlefield. Over time however, largely because facts do not go away, a largely shared vision both of fluctuations and of methodology has emerged. Not everything is fine. Like all revolutions, this one has come with the destruction of some knowledge, and suffers from extremism and herding. None of this deadly however. The state of macro is good.