all the available instruments simply reproduced, or nearly reproduced,

OLS. Maximum likelihood estimators tended to be hard to compute,

and then once computed tended to be often unreasonable, because they

corresponded to isolated peaks. Use of small-sample distributions of

estimators to form con¬dence intervals and tests was impossible at models

224 Lars Peter Hansen

Chris Sims commenting from the ¬‚oor at the Swedish Riksbank,

Figure 10.6

2003.

of this scale, and the asymptotic theory clearly was unreliable because of

the scant degrees of freedom.

Academic time-series modeling was focusing on unit roots and coin-

tegration, suggesting hierarchical layers of statistical tests to pin down the

cointegration structure before estimation. However, in very large models,

carrying out such layers of tests is generally impractical.

Academic macroeconomic theorizing was focusing on rational expecta-

tions, which was not in itself a problem. But leading ¬gures, such as

Sargent and Lucas, associated rational expectations with the fallacious

view that there is a fundamental distinction between analyzing a change

in policy “rule” and analyzing a change in a policy variable. A change

of policy rule can in fact be only consistently modeled as a particular,

nonlinear sort of stochastic shock. The fallacious contrary view led to a

generation of graduate students who believed that the bread and butter

of quantitative policy analysis”making projections conditional on values

of random variables that appear explicitly in a model”was somehow

deeply mistaken or internally contradictory. The result was a long period

with little or no academic interest in contributing to or criticizing the

models actually used in making monetary policy.

The models are now in a sorry state, but we may be at the point where

Bayesian methods and thinking can address these problems and begin

An Interview with Christopher A. Sims 225

to close the gap between academic macro and econometrics and the

actual practice of quantitative policy modeling. Some recent papers by

Smets and Wouters (e.g., 2002, 2003) are particularly promising along

this line.

Hansen: You recently published a paper on “rational inattention”

[Sims (2003a)] in which you apply results from information theory to

build a model of sluggishness in decisionmaking. What led you to use

this formalism, and where do you see this research headed?

Sims: I wrote a paper called “Stickiness” (Sims 1998b) a few years

ago in which I set out to show that variations on standard theoretical

assumptions about menu costs and inertia could match the qualitative

behavior of the macro data. I noted, though, that the usual theoretical

setups implied that either prices were sticky and real variables “jumpy,”

or real variables were sticky and prices jumpy. The data show that both

classes of variables are about equally inertial. Furthermore, any sort

of adjustment cost formulation tends to imply not only that the variables

subject to adjustment costs should respond slowly and smoothly to

other variables, but also that they should have smooth time paths. The

data show the slow and smooth cross-variable responses, but not the

correspondingly smooth time paths. The stickiness paper showed how

you could get both, but via a kind of hierarchical adjustment cost setup

that seems hard to connect to data or even to economic intuition.

At the end of that paper is an appendix pointing out that there might

be reason to think that inertia due to information-processing con-

straints, modeled using the notion of Shannon channel capacity, could

account for the way the data behave in a more intuitively appealing way.

The more recent paper you mention works out the application of the

method to general linear-quadratic dynamic optimization problems, and

shows that it does in fact account for the qualitative nature of observed

inertia.

Few economists know any information theory, though many have told

me they ¬nd the intuition behind the formalism appealing. For the time

being, these ideas are propagating slowly because there are few people

able to actually advance the formal frontier. I™m working on the area

myself, trying to construct easily used software that will let these methods

be applied more widely. The rational inattention setup implies that peo-

ple will behave as if they face signal extraction problems even when there

are no external costs to obtaining precise information. This should en-

courage more attention to models with imperfectly informed agents, and

in fact has already done so to some extent [e.g., Woodford (2001)], even

before models that ground the form of the signal extraction problems in

information theory are available.

226 Lars Peter Hansen

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228 John Y. Campbell