I think this is right, but I'd put it differently.Actually, this condition occurs most of the time. At a minimum, the condition of asymmetry of information -- as Joseph Stiglitz has pointed out -- occurs virtually all of the time.
Models are built to answer questions, and the models economists have been using do, in fact, help us find answers to some important questions. But the models were not very good (at all) at answering the questions that are important right now. They have been largely stripped of their usefulness for actual policy in a world where markets simply break down.
The reason is that in order to get to mathematical forms that can be solved, the models had to be simplified. And when they are simplified, something must be sacrificed. So what do you sacrifice?
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The models we built were very useful for asking whether the federal funds rate should go up or down a quarter point when the economy was hovering in the neighborhood of full employment, or when we found ourselves in mild, "normal" recessions. The models could tell us what type of monetary policy rule is best for stabilizing the economy. But the models had almost nothing to say about a world where markets melt down, where prices depart from fundamentals, or when markets are incomplete.
Further, the models did not predict the breakdown of the markets, which Thoma now turns to.But it IS the math.
When this crisis hit, I looked into our tool bag of models and policy recommendations and came up empty for the most part. It was disappointing. There was really no choice but to go back to older Keynesian style models for insight.
The reason the Keynesian model is finding new life is that it specifically built to answer the questions that are important at the moment. The theorists who built modern macro models, those largely in control of where the profession has spent its effort in recent decades, did not even envision that this could happen, let alone build it into their models. Markets work, they don't break down, so why waste time thinking about those possibilities?
So it's not the math, the modeling choices that were made and the inevitable sacrifices to reality that entails reflected the importance those making the choices gave to various questions. We weren't forced to this end by the mathematics, we asked the wrong questions and built the wrong models.
Mathematics doesn't work on economics. Calculus, the second derivative. Value at Risk. As John Maynard Keynes said, "It is better to be approximately right than precisely wrong." Yet a great deal of economics is math-based efforts to be precise. Statistical models are built with far too few observations, and assume for example, a reversion to a mean as if it were the natural movement of the tides.
It is human behavior, for God's sake. While similar episodes may behave similarly, we have wars and radically varying regulatory frameworks, demographics, and variables that are not internally consistent.
Let's take this last point. The interest rate is not a molecule that will behave consistently, much less have a consistent effect on other similar variables. Economic variables like the interest rate are aggregates of different parts at different times. The interest rate, for example, combines central bank action, competition for money, availability of savings, risk aversion, perceptions of currency strength, perceptions of the future, and so on. The interest rate can be high because the supply of money is low or high because there is too much money and inflation threatens returns. If each of the internal parts of the interest rate could be given its own vector, it might be possible to form a useful variable out of the interest rate. But even this is impossible.
You only have to look at how the Federal Reserve treats all inflation as demand-pull inflation to be remedied with higher interest rates to see how much they are operating in a hypothetical universe. Mathematically precise. Practically useless.
Thoma continues:
New Keynesians have been trying to answer: Can we, using equilibrium models with rational agents and complete markets, add frictions to the model - e.g. sluggish wage and price adjustment - you'll see this called "Calvo pricing" - in a way that allows us to approximate the actual movements in key macroeconomic variables of the last 40 or 50 years.I hope so.
Real Business Cycle theorists also use equilibrium models with rational agents and complete markets, and they look at whether supply-side shocks such as shocks to productivity or labor supply can, by themselves, explain movements in the economy. They largely reject demand-side explanations for movements in macro variables.
The fight - and main question in academics - has been about what drives macroeconomic variables in normal times, demand-side shocks (monetary policy, fiscal policy, investment, net exports) or supply-side shocks (productivity, labor supply). And it's been a fairly brutal fight at times - you've seen some of that come out during the current policy debate. That debate within the profession has dictated the research agenda.
What happens in non-normal times, i.e. when markets break down, or when markets are not complete, agents are not rational, etc., was far down the agenda of important questions, partly because those in control of the journals, those who largely dictated the direction of research, did not think those questions were very important. Some don't even believe that policy can help the economy, so why put effort into studying it?
I think that the current crisis has dealt a bigger blow to macroeconomic theory and modeling than many of us realize.
Mark Thoma
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