rational expectations agent because his fear of model misspecification is out in
the open.
Evans and Honkapohja: Parts of your description of robustness remind
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 prob-
lems, the decision maker does not use standard maximum likelihood estimators
for his approximating model — he distrusts his likelihood function. Therefore,
he distorts his likelihood function in preparing his estimates. This twisting is
reminiscent of what some calibrators do, though the robustness procedure is
more precisely defined, 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 robust-
ness?
Sargent: He thinks it is not wise to leave the Bayesian one-model frame-
work of Savage. He thinks that there are big dividends in terms of ease of
analysis by working hard to represent fear of model misspecifications 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 appropriate data filters that would
minimize approximation errors. That beautiful practical analysis of Chris’s
had a min-max flavor 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 twenty 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 propagat-
ing a new dynamic macroeconomics. What ingredients formed the Minnesota
environment?
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