An Interview with Thomas J. Sargent

struggles based on different information or different interests — which is all they
could possibly reflect within a ‘communist’ rational expectations model. Some
disputes over government policy originate in the fact that advocates have differ-
ent models of the way the economy functions, and it can be difficult to criticize
their models on empirical grounds because they fit the data from the prevailing
equilibrium.

Evans and Honkapohja: What else has learning theory contributed?

Sargent:    A couple of important things. First, it contains some results

about rates of convergence to a rational expectations equilibrium that can be
informative about how difficult it is to learn an equilibrium. Second, we have
discovered that even when convergence occurs with probability one, sample
paths can exhibit exotic trajectories called ‘escape routes’. These escape routes
exhibit long-lived departures from a self-confirming equilibrium and can visit
ob jects that qualify as ‘near equilibria’. The escape paths can be characterized
by an elegant control problem and contribute a form of ‘near rational’ dynamics
that can have amazing properties. I first encountered these ideas while working
on my Conquest book. In-Koo Cho and Noah Williams have pushed these ideas
further. I suspect that these escape routes will prove to be a useful addition to
our toolkit. For example, they can sustain the kind of drifting parameters that
Lucas brought out in the first part of his Critique, but that, until recently, most
of us have usually refrained from interpreting as equilibrium outcomes. A good
example of the type of phenomena that drifting coefficients with escapes from
a self-confirming equilibrium can explain is contained in the recent AER paper
on recurrent hyperinflations by Albert Marcet and Juan Pablo Nicolini.

Evans and Honkapohja: With your co-author Tim Cogley, you have
been studying drifting coefficients and volatilities. Did Lucas’s Critique fuel
your work with Cogley?

Sargent: Yes. Sims claims that while there is ample evidence for drifting
volatilities, the evidence for drifting coefficients is weak. And he uses that fact
to argue that U.S. data are consistent with time-invariant government mone-
tary and fiscal policy rules throughout the post WWII period. So when bad
macroeconomic outcomes occurred, it was due to bad luck in the form of big
shocks, not bad policy in the form of decision rules that had drifted into becom-
ing too accommodating or too tight. It is true that detecting drifts in the AR
coefficients in a VAR is much more difficult than detecting drifts in innovation
volatilities — this is clearest in continuous time settings that finance people work
in. (Lars Hansen has taught this to me in the context of our work on robust-
ness.) Thus, Sims and other ‘bad luck, not bad policy’ advocates say that the
drift spotted by Lucas is misinterpreted if it is regarded as indicating drifting
decision rules, e.g., drifting monetary policy rules. The reason is that, by in ef-
fect projecting in wrong directions, it misreads stochastic volatility as reflecting
drift in agents’ decision rules. These are obviously very important issues that
can be sorted out only with an econometric framework that countenances both

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