to join a line of research called “learning theory,” in which the theoretical un-
derpinnings of rational expectations were examined.
Tom became one of the pioneers in this area as well. His 1989 papers with
Albert Marcet showed how to use the tools of stochastic approximation to an-
alyze convergence of least squares learning to rational expectations equilibrium
in a general framework. His 1993 book Bounded Rationality in Macroeconomics
helped to disseminate the learning approach to a broader audience, and was
part of the rapid growth of research on learning in the 1990s. Tom’s 1999 book
The Conquest of American Inflation called attention to the possibility of “es-
cape routes,” i.e. occasional large deviations from an equilibrium, and led to a
surge of interest in persistent learning dynamics. Closely related to the research
on learning are issues of robustness and model misspecification to which Tom
(with Lars Hansen) has recently made key contributions.
The depth and range of the contributions we have listed is huge, yet this
is not the full extent. Sargent has also done important research in economic
history. His work in the 1980s on episodes of moderate and rapid inflations
and the recent research on monetary standards (with Francois Velde) is much
less technical, but the rational expectations viewpoint remains clearly visible in
these works.
Many collaborators, researchers and students have personally experienced
Tom’s remarkable intellectual depth and energy. His thinking is well reflected in
this interview, which has a somewhat unusual format. It gets to the key issues
very quickly. Only at the end is there commentary on some of his personal
experiences as a scholar.
Rational Expectations Econometrics
Evans and Honkapohja: How did you first get interested in rational
expectations?
Sargent: When I was a graduate student, estimating and interpreting
distributed lags topped the agenda of macroeconomists and other applied econo-
mists. Because distributed lags are high dimensional ob jects, people like Solow,
Jorgenson, Griliches, Nerlove, and Almon sought economical ways to parameter-
ize those distributions in clever ways, for example, by using ratios of low order
polynomials in a lag operator. As beautiful as they are, where on earth do those
things come from? Cagan and Friedman interpreted their adaptive expectations
geometric distributed lag as measuring people’s expectations. At Carnegie, Mike
Lovell told me to read John Muth’s 1960 JASA paper. It rationalized Fried-
man’s adaptive expectations model for permanent income by reverse engineering
a stochastic process for income for which Cagan’s expectation formula equals
a mathematical expectation of future values conditioned on the infinite history
of past incomes. Muth’s message was that the stochastic process being forecast
should dictate both the distributed lag and the conditioning variables that peo-
ple use to forecast the future. The point about conditioning variables primed
us to see the importance of Granger-Wiener causality for macroeconomics.