A great critique of Rational Expectations

How did I miss this great critique of Rational Expectations? Charles Manski, an econometrician at Northwestern University, published a paper in 2004 in Econometrica looking at the way economists measure expectations. Here is the final working-paper version. Manski spends a lot of his time discussing the possibility of measuring expectations through surveys. But in one section he critiques the idea of Rational Expectations, which is assumed in most economic models. Manski writes:

Suppose that the true state of nature actually is the realization of a random variable distributed P. A decision maker attempting to learn P faces the same inferential problems – identification and induction from finite samples – that empirical economists confront in their research. Whoever one is, decision maker or empirical economist, the inferences that one can logically draw are determined by the available data and the assumptions that one brings to bear. Empirical economists seldom are able to completely learn objective probability distributions of interest, and they often cannot learn much at all. It therefore seems hopelessly optimistic to suppose that, as a rule, expectations are either literally or approximately rational.

Rational Expectations basically say that economic agents behave as if the true model of the economy is the same as the model the economist is currently writing down. But that model includes stochastic processes. And in most situations, it's impossible to pin down the stochastic processes governing the economy - you have to make some guesses. Rational Expectations forces you to assume that economic agents are making all the same guesses you are. That goes way beyond rationality. It is also highly implausible, when you think about it, especially since econometricians themselves will almost always disagree on which guesses are appropriate.

Manski continues:

I would particularly stress that decision makers and empirical economists alike must contend with the logical unobservability of counterfactual outcomes. Much as economists attempt to infer the returns to schooling from data on schooling choices and outcomes, youth may attempt to learn through observation of the outcomes experienced by family, friends, and others who have made their own past schooling decisions. However, youth cannot observe the outcomes that these people would have experienced had they made other decisions. The possibilities for inference, and the implications for decision making, depend fundamentally on the assumptions that youth maintain about these counterfactual outcomes. 

In other words, economic agents just have no physical way of learning about all of the possible outcomes in an economy that never end up happening. 

Here's a simple example. Suppose I think that if I use pachinko machine A, I'll win with a 51% chance and lose with a 49% chance. And suppose that I think that if I use pachinko machine B, I'll win with a 40% chance and lose with a 60% chance. What do I do? I use pachinko machine A every time. Now suppose that I'm right about the odds of machine A (which I confirm by multiple uses), but wrong about machine B. Suppose that machine B actually has odds of 55% win, 45% lose. I should be using machine B, but I never do, so I never find out that I'm wrong, and I keep making the wrong decision! 

Now, if there are lots of people playing on lots of machines and we can all observe each other, it's clear that we'll figure out the odds of all the machines. But many economic models are macro models. The macroeconomy can only make one decision at a time. What would have happened if we had stayed on the gold standard in the Great Depression? We can make guesses, but we'll never really know. So this kind of limited knowledge makes Rational Expectations especially difficult to swallow in the context of macro.

Note that a lot of people think that Rational Expectations becomes a better and better assumption as the economy settles down into a long-term steady state. But the pachinko example above shows how this may not be the case, since in the steady state, the decision maker never learns the truth.

So why does everyone and their dog use Rational Expectations? Manski says that, basically, it's because A) it's easy, and B) there's no obviously better alternative:

Why do economists so often assume that they and the decision makers they study share rational expectations? Part of the reason may be the elegant manner in which these assumptions close an economic model. A researcher specifies his own vision of how the economy works, and he assumes that the persons who populate the economy share this vision. This is tidy and self-gratifying. 

Another part of the reason must be the data used in empirical research. As illustrated in Section 2, choice data do not necessarily enable one to infer the expectations that decision makers hold. Hence, researchers who are uncomfortable with rational expectations assumptions can do no better than invoke some other unsubstantiated assumption. Rather than speculate on how expectations actually are formed, they follow convention and assume rational expectations.

I'd add a third, more cynical reason: Rational Expectations can't be challenged on data grounds. If you measure expectations with surveys, people can poke holes not just in your theoretical model, but in the expectations data that you gathered and the econometric methods that you used to extract a signal from it. But if you assume Rational Expectations, they can only poke holes in the model itself. Basically, substituting theoretical assumptions for empirical results makes a model a more hardened target. If it makes the model less able to fit the data at the end of the day, well..."all models are wrong", right?

Anyway, everyone should go read Manzi's entire paper. Very interesting stuff, even if a decade old.