Evans and Honkapohja: What do you mean “disappearance”?

Sargent: In rational expectations models, people™s beliefs are among

the outcomes of our theorizing. They are not inputs.

Evans and Honkapohja: Do you think that differences among

people™s models are important aspects of macroeconomic policy debates?

Sargent: The fact is that you simply cannot talk about those differ-

ences within the typical rational expectations model. There is a com-

munism of models. All agents inside the model, the econometrician, and

God share the same model. The powerful and useful empirical implica-

tions of rational expectations”the cross-equation restrictions and the

legitimacy of the appeal to a law of large number in GMM estimation”

derive from that communism of models.

Evans and Honkapohja: What role do cross-equation restrictions

play in Lucas™s Critique?

Sargent: They are everything. The positive part of Lucas™s Critique

was to urge applied macroeconomists and econometricians to develop

ways to implement those cross-equation restrictions. His paper had three

examples. What transcends them is their cross-equation restrictions,

and the absence of free parameters describing expectations. In a nut-

shell, Lucas™s Critique of prerational expectations work was, “you have

ignored cross equation restrictions, and they are all important for policy

evaluation.”

Evans and Honkapohja: What do those cross-equation restrictions

have to say about the evidence in favor of coef¬cient volatility that Bob

Lucas talked about in the ¬rst part of his “Critique”?

Sargent: Little or nothing. Lucas used evidence of coef¬cient drift and

add factors to bash the Keynesians, but as I read his paper, at least, he

didn™t claim to offer an explanation for the observed drift. His three

examples are each time-invariant structures. Data from them would not

have coef¬cient drift even if you ¬t one of those misspeci¬ed Keynesian

models. So the connection of the ¬rst part of his paper to the second

was weak.

Evans and Honkapohja: Do you feel that your work contributed to

the Lucas Critique?

Sargent: It depends what you mean by “contribute.” Lucas attended a

conference on rational expectations at the University of Minnesota in the

spring of 1973. The day after the conference, I received a call from

Pittsburgh. Bob had lost a manuscript and thought he might have left it

at the conference. I went to the room in Ford Hall at which we had held

the conference and found a folder with yellow sheets in it. I looked at the

¬rst few pages. It was Bob™s Critique. I mailed the manuscript back to

Bob. So, yes, I contributed to the Critique.

An Interview with Thomas J. Sargent 313

Evans and Honkapohja: What were the profession™s most important

responses to the Lucas Critique?

Sargent: There were two. The ¬rst and most optimistic response was

complete rational expectations econometrics. A rational expectations equi-

librium is a likelihood function. Maximize it.

Evans and Honkapohja: Why optimistic?

Sargent: You have to believe in your model to use the likelihood

function. It provides a coherent way to estimate objects of interest (pref-

erences, technologies, information sets, measurement processes) within

the context of a trusted model.

Evans and Honkapohja: What was the second response?

Sargent: Various types of calibration. Calibration is less optimistic

about what your theory can accomplish because you™d only use it if

you didn™t fully trust your entire model, meaning that you think your

model is partly misspeci¬ed or incompletely speci¬ed, or if you trusted

someone else™s model and data set more than your own. My recollec-

tion is that Bob Lucas and Ed Prescott were initially very enthusiastic

about rational expectations econometrics. After all, it simply involved

imposing on ourselves the same high standards we had criticized the

Keynesians for failing to live up to. But after about ¬ve years of doing

likelihood ratio tests on rational expectations models, I recall Bob Lucas

and Ed Prescott both telling me that those tests were rejecting too many

good models. The idea of calibration is to ignore some of the probabilistic

implications of your model but to retain others. Somehow, calibration was

intended as a balanced response to professing that your model, though

not correct, is still worthy as a vehicle for quantitative policy analysis.

Evans and Honkapohja: Why do you say “various types of calibration”?

Sargent: Different people mean and do different things by calibration.

Some people mean “use an extraneous estimator.” Take estimates from

some previous study and pretend that they are known numbers. An

obvious dif¬culty of this procedure is that often those extraneous estim-

ates were prepared with an econometric speci¬cation that contradicts

your model. Treating those extraneous parameters as known ignores the

clouds of uncertainty around them, clouds associated with the estimation

uncertainty conveyed by the original researcher, and clouds from the

“speci¬cation risk” associated with putting your faith in the econometric

speci¬cation that another researcher used to prepare his estimates.

Other people, for example, Larry Christiano and Marty Eichenbaum,

by calibration mean GMM estimates using a subset of the moment con-

ditions for the model and data set at hand. Presumably, they impose only

a subset of the moment conditions because they trust some aspects of

their model more than others. This is a type of robustness argument that

314 George W. Evans and Seppo Honkapohja

has been pushed furthest by those now doing semiparametric GMM.

There are ways to calculate the standard errors to account for vaguely

speci¬ed or distrusted aspects of the model. By the way, these ways of

computing standard errors have a min“max ¬‚avor that reminds one of

the robust control theory that Lars Hansen and I are using.

Evans and Honkapohja: We know what question maximum likeli-

hood estimates answers, and the circumstances under which maximum

likelihood estimates, or Bayesian counterparts to them, have good prop-

erties. What question is calibration the answer to?

Sargent: The best answer I know is contained in work by Hansen and

others on GMM. They show the sense in which GMM is the best way to

estimate trusted features of a less than fully trusted model.

Evans and Honkapohja: Do you think calibration in macroeconomics

was an advance?

Sargent: In many ways, yes. I view it as a constructive response to Bob™s

remark that “your likelihood ratio tests are rejecting too many good

models.” In those days, the rational expectations approach to macro-

economics was still being challenged by in¬‚uential people. There was

a danger that skeptics and opponents would misread those likelihood

ratio tests as rejections of an entire class of models, which of course

they were not. (The internal logic of the likelihood function as a complete

model should have made that clear, but apparently it wasn™t at the time!)

The unstated case for calibration was that it was a way to continue the

process of acquiring experience in matching rational expectations models

to data by lowering our standards relative to maximum likelihood, and

emphasizing those features of the data that our models could capture.

Instead of trumpeting their failures in terms of dismal likelihood ratio

statistics, celebrate the features that they could capture and focus atten-

tion on the next unexplained feature that ought to be explained. One

can argue that this was a sensible response to those likelihood ratio tests.

It was also a response to the scarcity of resources at our disposal. Creat-

ing dynamic equilibrium macro theories and building a time-series

econometrics suitable for estimating them were both big tasks. It was a

sensible opinion that the time had come to specialize and to use a sequ-

ential plan of attack: let™s ¬rst devote resources to learning how to create

a range of compelling equilibrium models to incorporate interesting

mechanisms. We™ll be careful about the estimation in later years when we

have mastered the modeling technology.

Evans and Honkapohja: Aren™t applications of likelihood-based

methods in macroeconomics now making something of a comeback?

Sargent: Yes, because, of course, a rational expectations equilibrium is

a likelihood function, so you couldn™t ignore it forever. In the 1980s,

An Interview with Thomas J. Sargent 315

there were occasions when it made sense to say, “It is too dif¬cult to

maximize the likelihood function, and besides if we do, it will blow our

model out of the water.” In the 2000s, there are fewer occasions when

you can get by saying this. First, computers have gotten much faster, and

the Markov Chain Monte Carlo algorithm, which can be viewed as a

clever random search algorithm for climbing a likelihood function, or

building up a posterior, is now often practical. Furthermore, a number of

researchers have constructed rational expectations models with enough

shocks and wedges that they believe it is appropriate to ¬t the data

well with complete likelihood-based procedures. Examples are the recent

models of Otrok and Smets and Wouters. By using log-linear approx-

imations, they can use the same recursive representation of a Gaussian

likelihood function that we were using in the late 1970s and early eighties.