FRBSF Economic
Letter
2005-33; November 30, 2005
Uncertainty and Monetary Policy
In any meeting of monetary policymakers, uncertainty
is likely to play an important role in their deliberations.
According to Alan Greenspan (2003), "Uncertainty is
not just an important feature of the monetary policy
landscape; it is the defining characteristic of that
landscape." In fact, the recognition that all monetary
policymakers must bow to the presence of uncertainty
appears to underlie Greenspan's (2003) view that central
banks are driven to a "risk-management" approach to
policy, whereby policymakers "need to reach a judgment
about the probabilities, costs, and the benefits of
the various possible outcomes under alternative choices
for policy."
Uncertainty comes in many forms. One obvious form
is simply ignorance about the shocks that will disturb
the economy in the future—oil price shocks are
a good example. But other, perhaps more insidious,
forms of uncertainty can have resounding implications
for how policy should be conducted, three of which
are data uncertainty, parameter uncertainty, and model
uncertainty.
Since uncertainty is such an important issue for policymakers
it should come as no surprise that economists have
made a study of its various guises and developed formal
techniques to help understand and mitigate its effects.
In this Letter I discuss, in broad-brush terms,
some of these techniques and their implications for
the conduct of monetary policy.
Data uncertainty
One form of uncertainty that is ever present is data
uncertainty. Consider the economy's real GDP. For each
and every quarter of the year, three estimates of real
GDP are released: an advance estimate, a preliminary
estimate, and a final estimate. As successive estimates
are released, a greater fraction of the estimate is
actually measured and less is imputed. But some imputation
is involved even for the final GDP release. In fact
the final GDP estimate is not final. Every year a benchmark
revision occurs in which previous estimates of real
GDP are revised, going back several years. Try as we
might, due to measurement difficulties of one sort
or another, we can never know what the economy's real
GDP actually is, or was. This is data uncertainty.
Orphanides (2001) makes an in-depth study of data
revisions, including those to real GDP, emphasizing
the point that any study of past policy decisions should
be based on data that were available to policymakers
at that time, not on data that have been subsequently
revised. This is not a mere quibble. Orphanides shows
that policy rules look very different when they are
estimated on real-time data—that is on the data
available at the time policy decisions were made—rather
than on revised data. In particular, not using real-time
data can give a very misleading impression of monetary
policy's responsiveness to inflation.
A separate issue is how real-time monetary policy
should be conducted when the central bank acknowledges
data uncertainty, since a rule that performs well when
there is no data uncertainty may prove disastrous when
there is. Aoki (2003) examines this issue and obtains
results that are reasonably intuitive: as the amount
of measurement error, or data uncertainty, in a variable
increases, the information content in that variable
should be discounted. So the more poorly real GDP is
measured, the less a policymaker should respond to
movements in real GDP when conducting policy. In effect,
data uncertainty provides reason to proceed cautiously,
attenuating the response coefficients in an optimal
policy rule.
Parameter uncertainty
Distinct from data uncertainty is parameter uncertainty.
Economists use models to understand how the economy
might respond when stimulated in certain ways, and
to create forecasts. These economic models contain
parameters that govern the interactions that occur
within the model, such as how sensitive consumption
or investment is to a 1 percentage point change in
the real interest rate. While economists can use statistical
techniques to try to estimate these parameters, ultimately
their values remain very much uncertain quantities.
How does parameter uncertainty affect how policymakers
should conduct policy? An answer to this question was
provided first in a paper by Brainard (1967). He argued
that, in response to uncertainty about the parameter
on a variable, policymakers should attenuate their
policy response to movements in that variable. While
the motivation is different, this answer is the same
as that suggested by the literature on data uncertainty.
Unfortunately, Brainard's finding, however intuitive,
has been shown not to be general: some forms of parameter
uncertainty suggest that policymakers should discount
incoming data, but others suggest that policymakers
should respond more aggressively to incoming data.
For example, if there is uncertainty about the persistence
of inflation, then it may pay for policymakers to respond
aggressively to increases in inflation in order to
guard against the possibility that shocks may have
an enduring effect on inflation outcomes (Söderström
2002).
Some recent studies have found that parameter uncertainty
is not such a big deal for policymakers. Rudebusch
(2001) considers how parameter uncertainty affects
the coefficients in an optimal policy rule using a
macroeconometric model of the United States and finds
that for his model the effects of parameter uncertainty
are essentially negligible, certainty less important
than those of data uncertainty. But while it is possible
that uncertainty about model parameters may be reasonably
benign from policymakers' perspective, this is not
to say that uncertainty about the goals and conduct
of monetary policy is benign from households' and firms'
perspective. With respect to the latter, studies by
Orphanides and Williams (2005) and Gurkaynak, Sack,
and Swanson (2005) show that better policy outcomes
can be obtained when households and firms are more
certain of the economy's long-run average rate of inflation,
highlighting one reason why some countries may have
adopted policy regimes with explicit inflation targets.
Model uncertainty and
model averaging
While there is uncertainty about the data that enter
into economic models and about the parameters that
govern economic models, the fact that economists often
approach macroeconomic data armed with different models
of the economy suggests that uncertainty, or ambiguity,
about the model could also be potentially important.
From a policymaking perspective, it is quite possible,
indeed reasonable, to think that policymakers may have
several models at their disposal, perhaps reflecting
competing economic theories, each of which could justifiably
be viewed as a reasonable approximation of the interrelationships
at work in the actual economy.
A policy can be made "robust" to model uncertainty
by designing it to perform well on average across all
of the available fully specified models rather than
to reign supreme in any particular model (McCallum
1988). This model-averaging approach is taken in Levin,
Wieland, and Williams (2003), who use five disparate
macroeconometric models of the U.S. economy to study
how best to conduct monetary policy when facing model
uncertainty. Focusing on simple rules in which the
Federal Reserve is assumed to set the federal funds
rate in response to inflation, the output gap (that
is, the difference between actual output and an estimate
of potential output), and the lagged federal funds
rate, they identify a particular policy rule that is
able to perform well across all five models. The policy
rule that they identify is one that contains a short-term
forecast of future inflation, incorporates a large
response to the output gap, and that involves considerable "gradualism," or
interest rate smoothing.
Although the model averaging approach allows us to
get a handle on how to think about model uncertainty
at the level of the policymaker, it is less clear what
the approach has to say about the views of the households
and firms that make up the economy.
Model
uncertainty and robust control
The model-averaging approach to model uncertainty
is not possible when policymakers cannot articulate
and specify the various models that they wish to be
robust against and therefore cannot assign probabilities
to each of the models. This situation is known as Knightian
uncertainty (Knight 1921). In such environments, the
robust control approach comes into play. Robust control
suggests that policymakers should formulate policy
to guard against the worst form of model misspecification
that is possible. Thus, rather than focusing on the "most
likely" outcome or on the average outcome, robust control
argues that policymakers should focus on and defend
against the worst-case outcome. While the robust control
approach may suggest some paranoia on the part of the
policymaker, the intuition for robust control can be
found in such common expressions as "expect the unexpected" and "hope
for the best, but prepare for the worst." A valuable
feature of the robust control approach is that it allows
us to think about and combine model misspecification
from the perspective of the policymaker with model
misspecification from the perspective of households
and firms. After all, there is no reason to think that
policymakers are the only people who have to worry
about model misspecification.
In an interesting application of robust control methods,
Sargent (1999) studies a simple macro-policy model
and shows that robustness, in the "robust control" sense,
does not necessarily lead to policy attenuation. Instead,
the robust policy rule may respond more aggressively
to shocks. The intuition for this result is that, by
pursuing a more aggressive policy, the central bank
can prevent the economy from encountering situations
where model misspecification might be especially damaging.
Conclusion
Uncertainty comes in various forms and is something
that policymakers must continually contend with. Economists
have developed a range of formal methods for thinking
about and analyzing uncertainty, all of which offer
important insights into how policymakers might manage
the problem. While attenuation, the notion that incoming
data should be discounted, is an intuitive reaction
to uncertainty, it is not always appropriate. Unfortunately,
when dealing with uncertainty, there do not seem to
be any hard and fast guidelines for policymakers.
Richard Dennis
Economist
References
Aoki, Kosuke. 2003. On the Optimal Monetary Policy
Response to Noisy Indicators. " Journal of
Monetary Economics 50(3), pp. 501-523.
Brainard, William. 1967. "Uncertainty and the
Effectiveness of Monetary Policy." American
Economic Review 57(2), pp. 411-425.
Greenspan, Alan. 2003. Opening Remarks at "Monetary
Policy under Uncertainty," symposium sponsored
by the Federal Reserve Bank of Kansas City, Jackson
Hole, Wyoming.
Gurkaynak, Refet, Brian Sack, and Eric Swanson. 2005. "The
Sensitivity of Long-Term Interest Rates: Evidence and
Implications for Macroeconomic Models." American
Economic Review 95(1), pp. 425-436.
Knight, Frank. 1921. Risk, Uncertainty, and Profit.
Boston: Houghton Mifflin Co.
Levin, Andrew, Volker Wieland, and John Williams.
2003. "The Performance of Forecast-Based Monetary
Policy Rules under Model Uncertainty." American
Economic Review 93(3), pp. 622-645
McCallum, Bennett. 1988. "Robustness Properties
of a Rule for Monetary Policy." Carnegie-Rochester
Conference Series on Public Policy 29, pp. 175-203.
Orphanides, Athanasios. 2001. ""Monetary Policy
Rules Based on Real-Time Data." American Economic
Review 91(4), pp. 964-985.
Orphanides, Athanasios, and John Williams. 2005. "Inflation
Scares and Forecast-Based Monetary Policy." Review
of Economic Dynamics 8(2), pp. 498-527.
Rudebusch, Glenn. 2001. "Is the Fed Too Timid?
Monetary Policy in an Uncertain World." Review
of Economics and Statistics 83(2), pp. 203-217
Sargent, Thomas. 1999. "Comment: Policy Rules
for Open Economies." In Monetary Policy Rules, ed.
John Taylor. Chicago: University of Chicago Press.
Söderström, Ulf. 2002. "Monetary Policy
with Uncertain Parameters." Journal of Economics 104(1),
pp. 125-145.
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