coherent as you have to be in formal work without tossing out much of the
action. Analyzing the kinds of the transitions that we studied in formal terms
would have required a workable model of the social process of using experi-
ence to induce new models, paradigm shifts and revolutions of ideas, the really
hard unsolved problem that underlies Kreps’s anticipated utility program. (You
wouldn’t be inspired to take Muth’s brilliant leap to rational expectations mod-
els by running regressions.) We didn’t know how to make such a model, but
we nevertheless cast our narratives in terms of a process that, with hindsight,
induced new models from failed experiences with old ones.
Robustness and Model Misspecification
Evans and Honkapohja: You work with Hansen and others on robust
control theory. How is that work related to your work on rational expectations
and on learning?
Sargent: It is connected to both, and to calibration as well. The idea
is to give a decision maker doubts about his model and ask him to make good
decisions when he fears that some other model might actually generate the data.
Evans and Honkapohja: Why is that a good idea?
Sargent: One loose motivation for both rational expectations theory
and learning theories is that the economist’s model should have the property
that the econometrician cannot do better than the agents inside the model.
This criterion was used in the old days to criticize the practice of attributing
to agents adaptive and other naive expectations schemes. So rational expecta-
tions theorists endowed agents with the ability to form conditional expectations,
i.e., take averages with respect to infinite data samples drawn from within the
equilibrium. The idea of learning theory was to take this ‘take averages’ idea
seriously by giving agents data from outside the equilibrium, then to roll up
your sleeves and study whether and at what rate agents who take averages from
finite outside-equilibrium data sets can eventually learn what they needed to
know in a population rational expectations equilibrium. It turned out that they
could. The spirit was to ‘make the agents like econometricians’.
Of course, the typical rational expectations model reverses the situation: the
agent knows more than the econometrician. The agent inside the model knows
the parameters of the true model while the econometrician does not and must
estimate them. Further, thorough rational expectations econometricians often
come away from their analyses with a battery of specification tests that have
brutalized their models. (Recall my earlier reference to Bob’s and Ed’s early
1980s comments to me that ’your likelihood ratio tests are rejecting too many
good models.’)
Using robust control theory is a way to let our agents share the experiences
of econometricians. The idea is to make the agent acknowledge and cope with
model misspecification.
Evans and Honkapohja: Is this just to make sure that agents are put
on the same footing as us in our role as econometricians?
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