322 George W. Evans and Seppo Honkapohja

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 expectations theorists endowed agents with the

ability to form conditional expectations, i.e., take averages with respect

to in¬nite 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

¬nite 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 ana-

lyses with a battery of speci¬cation tests that have brutalized their

models. (Recall my earlier reference to Bob™s and Ed™s early 1980s com-

ments 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 experi-

ences of econometricians. The idea is to make the agent acknowledge and

cope with model misspeci¬cation.

Evans and Honkapohja: Is this just to make sure that agents are put

on the same footing as us in our role as econometricians?

Sargent: Yes. And an agent™s response to fear of model misspeci¬ca-

tion contributes behavioral responses that have interesting quantitative

implications. For example, fear of model misspeci¬cation contributes

components of indirect utility functions that in some types of data can

look like heightened risk aversion, but that are actually responses to very

different types of hypothetical mental experiments than are Pratt meas-

ures of risk aversion. For this reason, fear of model misspeci¬cation is a

tool for understanding a variety of asset price spreads. Looked at from

another viewpoint, models of robust decisionmaking contribute a disci-

plined theory of what appears to be an endogenous preference shock.

Another reason is that decisionmaking in the face of fear of model

misspeci¬cation can be a useful normative tool for solving Ramsey

An Interview with Thomas J. Sargent 323

problems. That is why people at central banks are interested in the topic.

They distrust their models.

Evans and Honkapohja: What are some of the connections to learn-

ing theory?

Sargent: There are extensive mathematical connections through the

theory of large deviations. Hansen and I exploit these. Some misspeci-

¬cations are easy to learn about, others are dif¬cult to learn about. By

“dif¬cult” I mean “learn at a slow rate.” Large deviation theory tells us

which misspeci¬cations can be learned about quickly and which can™t.

Hansen and I restricted the amount of misspeci¬cation that our agent

wants to guard against by requiring that it be a misspeci¬cation that is

hard to distinguish from his approximating model. This is how we use

learning theory to make precise what we mean by the phrase “the deci-

sionmaker thinks his model is a good approximation.” There is a race

between a discount factor and a learning rate. With discounting, it makes

sense to try to be robust against plausible alternatives that are dif¬cult to

learn about.

Evans and Honkapohja: Can this model of decisionmaking be recast

in Bayesian terms?

Sargent: It depends on your perspective. We have shown that ex post,

it can, in the sense that you can come up with a prior, a distorted model,

that rationalizes the decisionmaker™s choices. But ex ante you can™t”

the set of misspeci¬cations that the agent fears is too big and he will not

or cannot tell you a prior over that set.

By the way, Lars and I have constructed equilibria with heterogeneous

agents in which the ex post Bayesian analysis implies that agents with

different interests will have different “twisted models.” From the point of

view of a rational expectations econometrician, these agents look as if

they have different beliefs. This is a disciplined way of modeling belief

heterogeneity.

Evans and Honkapohja: Is this a type of behavioral economics or

bounded rationality?

Sargent: Any decision theory is a type of behavioral economics. It is

not a type of bounded rationality. The decisionmaker is actually smarter

than a rational expectations agent because his fear of model misspeci¬cation

is out in the open.

Evans and Honkapohja: Parts of your description of robustness re-

mind us of calibration. Are there connections?

Sargent: I believe there are, but they are yet to be fully exploited.

Robust versions of dynamic estimation problems have been formulated.

In these problems, the decisionmaker does not use standard maximum

likelihood estimators for his approximating model”he distrusts his

324 George W. Evans and Seppo Honkapohja

likelihood function. Therefore, he distorts his likelihood function in pre-

paring his estimates. This twisting is reminiscent of what some calibrators

do, though the robustness procedure is more precisely de¬ned, in the

sense that you can answer your earlier question about “What question is

calibration the answer to?”

Evans and Honkapohja: Why has Sims criticized your work on

robustness?

Sargent: He thinks it is not wise to leave the Bayesian one-model

framework of Savage. He thinks that there are big dividends in terms of

ease of analysis by working hard to represent fear of model misspeci¬cations

in ways that stay within the Bayesian framework.

However, I should say that Lars™s and my readings of Chris™s early

work on approximation of distributed lags were important inspirations

for our work on robustness. Chris authored a beautiful approximation

error formula and showed how to use it to guide the choice of appropri-

ate data ¬lters that would minimize approximation errors. That beautiful

practical analysis of Chris™s had a min“max ¬‚avor and was not self-

consciously Bayesian. One version of Chris™s min“max analysis originated in

a message that Chris wrote to me about a comment in which I had argued

that a rational expectations econometrician should never use seasonally

adjusted data. My argument was very Bayesian in spirit, because I assumed

that the econometrician had the correct model. Chris both read my

comment and wrote his memo on a Minneapolis bus going home from

the U in 1976”that™s how fast Chris is. Chris™s bus memo on seasonality

and approximation error was pretty well known in the macro time-series

community at Minnesota in the late 1970s. (At the time, I don™t know

why, I felt that the fact that Chris could write such an insightful memo

while riding on his 20-minute bus ride home put me in my proper

place.) By the way, in Eric Ghysels™s 1993 Journal of Econometrics special

volume on seasonality, Lars and I wrote a paper that went a long way

towards accepting Sims™s bus memo argument. That Ghysels-volume

paper was one motivation for our robustness research agenda.

Minnesota Economics

Evans and Honkapohja: Along with Carnegie Mellon and Chicago,

Minnesota during the 1970s was at the forefront in developing and

propagating a new dynamic macroeconomics. What ingredients formed

the Minnesota environment?

Sargent: Tension and tolerance. We took strong positions and had

immense disagreements. But the rules of engagement were civil and we

An Interview with Thomas J. Sargent 325

always built each other up to our students. Minnesota in those days had

a remarkable faculty. (It still does!) The mature department leaders,

Leo Hurwicz and John Chipman, set the tone: they advocated taking

your time to learn carefully and they encouraged students to learn math.

Chris Sims and Neil Wallace were my two best colleagues. Both were for-

ever generous with ideas, always extremely critical, but never destructive.

The three of us had strong disagreements but there was also immense

respect. Our seminars were exciting. I interacted intensively with both

Neil and Chris through dissertations committees.

The best thing about Minnesota from the mid-seventies to mid-eighties

was our extraordinary students. These were mostly people who weren™t