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Finance and Economics Discussion Series
Divisions of Research & Statistics and Monetary Affairs
Federal Reserve Board, Washington, D.C.

Will Monetary Policy Become More of a Science?

Frederic S. Mishkin
2007-44

NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary
materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth
are those of the authors and do not indicate concurrence by other members of the research staff or the
Board of Governors. References in publications to the Finance and Economics Discussion Series (other than
acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

Will Monetary Policy Become More of a Science?
Frederic S. Mishkin

Member
Board of Governors of the Federal Reserve System

September 2007

Prepared for the Deutsche Bundesbank conference “Monetary Policy Over Fifty Years,” held in
Frankfurt am Main, Germany, September 21, 2007. The views expressed here are my own and
are not necessarily those of the Board of Governors or the Federal Reserve System. I thank
Michael Kiley, Andrew Levin, and Robert Tetlow for their helpful comments and assistance.

Over the past three decades, we have seen a remarkable change in the performance of
monetary policy. By the end of the 1970s, inflation had risen to very high levels, with many
countries in the Organisation for Economic Co-operation and Development (OECD)
experiencing double-digit inflation rates (figure 1). Most OECD countries today have inflation
rates around the 2 percent level, which is consistent with what most economists see as price
stability, and the volatility of inflation has also fallen dramatically (figure 2). One concern might
be that the low and stable levels of inflation might have been achieved at the expense of higher
volatility in output, but that is not what has occurred. Output volatility has also declined in most
OECD countries (figure 3). The improved performance of monetary policy has been associated
with advances in the science of monetary policy, that is, a set of principles that have been
developed from rigorous theory and empirical work that have come to guide the thinking of
monetary policy practitioners.
In this paper, I will review the progress that the science of monetary policy has made
over recent decades. In my view, this progress has significantly expanded the degree to which
the practice of monetary policy reflects the application of a core set of “scientific” principles.
Does this progress mean that, as Keynes put it, monetary policy will become as boring as
dentistry--i.e., that policy will be reduced to the routine application of core principles, much like
filling cavities?1 I will argue that there remains, and will likely always remain, elements of art in
the conduct of monetary policy; in other words, substantial judgment will always be needed to
achieve desirable outcomes on both the inflation and employment fronts.

1

Given that my wife was a dentist, I have to say that Keynes may have been unfair to dentists. I am sure that many
of them find their work very exciting.

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I.
Advances in the Science of Monetary Policy in Recent Decades

Over the last five decades, monetary economists have developed a set of basic scientific
principles, derived from theory and empirical evidence, that now guide thinking at almost all
central banks and explain much of the success in the conduct of monetary policy. I will outline
my views on the key principles and how they were developed over the last fifty or so years. The
principles are: 1) inflation is always and everywhere a monetary phenomenon; 2) price stability
has important benefits; 3) there is no long-run tradeoff between unemployment and inflation; 4)
expectations play a crucial role in the determination of inflation and in the transmission of
monetary policy to the macroeconomy; 5) real interest rates need to rise with higher inflation,
i.e., the Taylor Principle; 6) monetary policy is subject to the time-inconsistency problem; 7)
central bank independence helps improve the efficiency of monetary policy; 8) commitment to a
strong nominal anchor is central to producing good monetary policy outcomes; and 9) financial
frictions play an important role in business cycles. I will examine each principle in turn.

1. Inflation is Always and Everywhere a Monetary Phenomenon

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By the 1950s and 1960s, the majority of macroeconomists had converged on a consensus
view of macroeconomic fluctuations that downplayed the role of monetary factors. Much of this
consensus reflected the aftermath of the Great Depression and Keynes’ seminal The General
Theory of Employment, Interest, and Prices, which emphasized shortfalls in aggregate demand
as the source of the Great Depression and the role of fiscal factors as possible remedies. In
contrast, research by Milton Friedman and others in what became known as the “monetarist”
tradition (Friedman and Meiselman, 1963; Friedman and Schwartz, 1963a,b) attributed much of
the economic malaise of the Depression to poor monetary policy decisions and more generally
argued that the growth in the money supply was a key determinant of aggregate economic
activity and, particularly, inflation. Over time, this research, as well as Friedman’s predictions
that expansionary monetary policy in the 1960s would lead to high inflation and high interest
rates (Friedman, 1968), had a major impact on the economics profession, with almost all
economists eventually coming to agree with the Friedman’s famous adage, “Inflation is always
and everywhere a monetary phenomenon” (Friedman 1963, p. 17), as long as inflation is
referring to a sustained increase in the price level (e.g., Mishkin, 2007a).
General agreement with Friedman’s adage did not mean that all economists subscribed to
the view that the money growth was the most informative piece of information about inflation,
but rather that the ultimate source of inflation was overly expansionary monetary policy. In
particular, an important imprint of this line of thought was that central bankers came to recognize
that keeping inflation under control was their responsibility.2

2

Furthermore, monetarist research led Keynesian economists--for example Franco Modigliani--to search for
transmission mechanisms linking monetary policy to output and inflation (Mishkin, 2007a, chapter 23).

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2. The Benefits of Price Stability

With the rise of inflation in the 1960s and 1970s, economists, and also the public and
politicians, began to discuss the high costs of inflation (for example, see the surveys in Fischer,
1993; and Anderson and Gruen, 1995). High inflation undermines the role of money as a
medium of exchange by acting as a tax on cash holdings. On top of this, a high-inflation
environment leads to overinvestment in the financial sector, which expands to help individuals
and businesses escape some of the costs of inflation (English, 1996). Inflation leads to
uncertainty about relative prices and the future price level, making it harder for firms and
individuals to make appropriate decisions, thereby decreasing economic efficiency (Lucas, 1972;
Briault, 1995). The interaction of the tax system and inflation also increases distortions that
adversely affect economic activity (Feldstein, 1997). Unanticipated inflation causes
redistributions of wealth, and, to the extent that high inflation tends to be associated with volatile
inflation, these distortions may boost the costs of borrowing. Finally, some households
undoubtedly do not fully understand the implications of a general trend in prices--that is, they
may suffer from nominal illusion--making financial planning more difficult.3 The total effect of
these distortions became more fully appreciated over the course of the 1970s, and the recognition

3

Of course, economic theory implies that inflation can be either too high or too low. The discussion has
emphasized costs associated with high inflation. But there are also potentially important costs associated with rates
of inflation that are very low. For example, Akerlof, Dickens, and Perry (1996) suggest that downward nominal
wage rigidity could result in severe difficulties for economic performance at some times when inflation is too low.
Other research has shown that the zero lower bound on nominal interest rates can lower economic efficiency if
inflation is too low (e.g., Reifschneider and Williams, 2000). Eggertsson and Woodford (2003) discuss strategies to
address the zero-lower-bound problem.

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of the high costs of inflation led to the view that low and stable inflation can increase the level of
resources productively employed in the economy.4, 5

3. No Long-Run Tradeoff Between Unemployment and Inflation

A paper published in 1960 by Paul Samuelson and Robert Solow argued that work by
A.W. Phillips (1958), which became known as the Phillips curve, suggested that there was a
long-run tradeoff between unemployment and inflation and that this tradeoff should be exploited.
Under this view, the policymaker would have to choose between two competing goals--inflation
and unemployment--and decide how high an inflation rate he or she would be willing to accept to
attain a lower unemployment rate. Indeed, Samuelson and Solow even mentioned that a
nonperfectionist goal of a 3 percent unemployment rate could be achieved at what they
considered to be a not-too-high inflation rate of 4 percent to 5 percent per year. This thinking
was influential, and probably contributed to monetary and fiscal policy activism aimed at
bringing the economy to levels of employment that, with hindsight, were not sustainable.
Indeed, the economic record from the late 1960s through the 1970s was not a happy one:
Inflation accelerated, with the inflation rate in the United States and other industrialized
countries eventually climbing above 10 percent in the 1970s, leading to what has been dubbed
“The Great Inflation.”
4

A further possibility is that low inflation may even help increase the rate of economic growth. While time-series
studies of individual countries and cross-national comparisons of growth rates were not in total agreement
(Anderson and Gruen, 1995), the consensus grew that inflation is detrimental to economic growth, particularly when
inflation rates are high.
5
The deleterious effects of inflation on economic efficiency implies that the level of sustainable employment is
probably lower at higher rates of inflation. Thus, the goals of price stability and high employment are likely to be
complementary, rather than competing, and so there is no policy tradeoff between the goals of price stability and
maximum sustainable employment, the so-called dual mandate that the Federal Reserve has been given by Congress
(Mishkin, 2007b).

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The tradeoff suggested by Samuelson and Solow was hotly contested by Milton Friedman
(1968) and Edmund Phelps (1968), who independently argued that there was no long-run
tradeoff between unemployment and the inflation rate: Rather, the economy would gravitate to
some natural rate of unemployment in the long run no matter what the rate of inflation was. In
other words, the long-run Phillips curve would be vertical, and attempts to lower unemployment
below the natural rate would result only in higher inflation. The Friedman-Phelps natural rate
hypothesis was immediately influential and fairly quickly began to be incorporated in formal
econometric models.
Given the probable role that the attempt to exploit a long-run Phillips curve tradeoff had
in the ‘Great Inflation,” central bankers have been well served by adopting the natural rate, or
no-long-run-tradeoff, view. Of course, the earlier discussion of the benefits of price stability
suggests a long-run tradeoff--but not of the Phillips curve type. Rather, low inflation likely
contributes to improved efficiency and hence higher employment in the long run.

4. The Crucial Role of Expectations

A key aspect of the Friedman-Phelps natural rate hypothesis was that sustained inflation
may initially confuse firms and households, but in the long run sustained inflation would not
boost employment because expectations of inflation would adjust to any sustained rate of
increase in prices. Starting in the early 1970s, the rational expectations revolution, launched in a
series of papers by Robert Lucas (1972, 1973, and 1976), took this reasoning a step further and
demonstrated that the public and the markets’ expectations of policy actions have important

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effects on almost every sector of the economy.6 The theory of rational expectations emphasized
that economic agents should be driven by optimizing behavior, and therefore their expectations
of future variables should be optimal forecasts (the best guess of the future) using all available
information. Because the optimizing behavior posited by rational expectations indicates that
expectations should respond immediately to new information, rational expectations suggests that
the long run might be quite short, so that attempting to lower unemployment below the natural
rate could lead to higher inflation very quickly.
A fundamental insight of the rational expectations revolution is that expectations about
future monetary policy have an important impact on the evolution of economic activity. As a
result, the systematic component of policymakers’ actions--i.e., the component that can be
anticipated--plays a crucial role in the conduct of monetary policy. Indeed, the management of
expectations about future policy has become a central element of monetary theory, as
emphasized in the recent synthesis of Michael Woodford (2003).7 And this insight has
far-reaching implications, for example, with regard to the types of systematic behavior by
policymakers that are likely to be conducive to macroeconomic stability and growth.8

5. The Taylor Principle
6

The 1976 Lucas paper was already very influential in 1973, when it was first presented at the Carnegie-Rochester
Conference. Note that although Muth (1961) introduced the idea of rational expectations more than ten years
earlier, his work went largely unnoticed until resurrected by Lucas.
7
Indeed, one implication of rational expectations in a world of flexible wages and prices was the policy
ineffectiveness proposition, which indicated that if monetary policy was anticipated, it would have no real effect on
output; only unanticipated monetary policy could have a significant impact. Although evidence for the policy
ineffectiveness proposition turned out to be weak (Barro, 1977; Mishkin, 1982a,b, 1983), the rational expectation
revolution’s point that monetary policy’s impact on the economy is substantially influenced by whether it is
anticipated or not has become widely accepted.
8
Of course, the recognition that management of expectations is a central element in monetary policymaking raises
to the forefront the credibility of monetary policy authorities to do what they say they will do. It does not diminish,
however, the importance of actions by the monetary authorities because “actions speak louder than words”:
Monetary authorities will be believed only if they take the actions consistent with how they want expectations to be
managed.

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The recognition that economic outcomes depend on expectations of monetary policy
suggests that policy evaluation requires the comparison of economic performance under different
monetary policy rules.9 One type of rule that has received enormous attention in the literature is
the Taylor rule (Taylor, 1993a), which describes monetary policy as setting an overnight bank
rate (federal funds rate in the United States) in response to the deviation of inflation from its
desired level or target (the inflation gap) and the deviation of output from its natural rate level
(the output gap).10 Taylor (1993a) emphasized that a rule of this type had desirable properties
and in particular would stabilize inflation only if the coefficient on the inflation gap exceeded
unity. This conclusion came to be known as the “Taylor principle” (Woodford, 2001) and can be
described most simply by saying that stabilizing monetary policy must raise the nominal interest
rate by more than the rise in inflation. In other words, inflation will remain under control only if
real interest rates rise in response to a rise in inflation. Although, the Taylor principle now
seems pretty obvious, estimates of Taylor rules, such as those by Clarida, Gali, and Gertler
(1998), indicate that during the late 1960s and 1970s many central banks, including the Federal
Reserve, violated the Taylor principle, resulting in the “Great Inflation” that so many countries
experienced during this period.11 Indeed, as inflation rose in the United States, real interest rates
fell.12

9

Although Lucas (1976) was a critique of the then-current practice of using econometric models to evaluate specific
policy actions, it leads to the conclusion that monetary policy analysis should involve the comparison of economic
performance arising from different rules.
10
Variants of the Taylor rule also allow for interest rate smoothing, as in Taylor (1999).
11
In contrast, Orphanides (2003) argues that the Federal Reserve did abide by the Taylor principle but pursued
overly expansionary policies during this period because of large and persistent misperceptions of the level of
potential output and the natural unemployment rate.
12
E.g., the estimates in Mishkin (1981, 1992).

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6. The Time-Inconsistency Problem

Another important development in the science of monetary policy that emanated from the
rational expectations revolutions was the discovery of the importance of the time-inconsistency
problem in papers by Kydland and Prescott (1977), Calvo (1978), and Barro and Gordon (1983).
The time-inconsistency problem can arise if monetary policy conducted on a discretionary, dayby-day basis leads to worse long-run outcomes than could be achieved by committing to a policy
rule. In particular, policymakers may find it tempting to exploit a short-run Phillips curve
tradeoff between inflation and employment; but private agents, cognizant of this temptation, will
adjust expectations to anticipate the expansionary policy, so that it will result only in higher
inflation with no short-run increase in employment In other words, without a commitment
mechanism, monetary policy makers may find themselves unable to consistently follow an
optimal plan over time; the optimal plan can be time-inconsistent and so will soon be abandoned.
The notion of time-inconsistency has led to a number of important insights regarding central
bank behavior--such as the importance of reputation (formalized in the concept of reputational
equilibria) and institutional design.

7. Central Bank Independence

Indeed, the potential problem of time-inconsistency has led to a great deal of research
that examines the importance of institutional features that can give central bankers the
commitment mechanisms they need to pursue low inflation. Perhaps the most significant has
been research showing that central bank independence, at least along some dimensions, is likely
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very important to maintaining low inflation. Allowing central banks to be instrument
independent, i.e., to control the setting of monetary policy instruments, can help insulate them
from short-run pressures to exploit the Phillips-curve tradeoff between employment and inflation
and thus avoid the time-inconsistency problem.13
Evidence supports the conjecture that macroeconomic performance is improved when
central banks are more independent. When central banks in industrialized countries are ranked
from least legally independent to most legally independent, the inflation performance is found to
be the best for countries with the most independent central banks (Alesina and Summers, 1993;
Cukierman, 1993; Fischer, 1994; and the surveys in Forder, 2000, and Cukierman, 2006).
A particularly interesting example occurred with the granting of instrument independence
to the Bank of England in May of 1997 (Mishkin and Posen, 1997; Bernanke and others, 1999);
before that date, the Chancellor of the Exchequer (the finance minister) set the monetary policy
instrument, not the Bank of England. As figure 4 illustrates, during 1995-96 the U.K. retail
inflation rate (RPIX) was fairly close to 3 percent, but the spread between nominal and indexed
bond yields--referred to as 10-year breakeven inflation--was substantially higher, in the range of
4 percent to 5 percent, reflecting investors’ inflation expectations as well as compensation for
perceived inflation risk at a 10-year horizon. Notably, breakeven inflation declined markedly on
the day that the government announced the Bank of England’s independence and has remained
substantially lower ever since. This case study provides a striking example of the benefits of
instrument independence.

13

For an example of how the time-inconsistency problem can be modeled as resulting from political pressure, see
Mishkin and Westelius (forthcoming). Instrument independence also insulates the central bank from the myopia
that can be a feature of the political process. Instrument independence thus makes it more likely that the central
bank will be forward looking and adequately allow for the long lags from monetary policy actions to inflation in
setting their policy instruments.

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Although there is a strong case for instrument independence, the same is not true for goal
independence, the ability of the central bank to set its own goals for monetary policy.14
In a democracy, the public exercises control over government actions, and policymakers are
accountable, which requires that the goals of monetary policy be set by the elected government.
Although basic democratic principles argue for the government setting the goals of monetary
policy, the question of whether it should set goals for the short-run or intermediate-run is more
controversial. For example, an arrangement in which the government set a short-run inflation or
exchange rate target that was changed every month or every quarter could easily lead to a serious
time-inconsistency problem in which short-run objectives would dominate. In practice, however,
this problem does not appear to be severe because, for example, in many countries in which the
government sets the annual inflation target, the target is rarely changed once price stability is
achieved. Even though, in theory, governments could manipulate monetary policy goals to
pursue short-run objectives, they usually do not if the goal-setting process is highly transparent.
However, the length of the lags from monetary policy to inflation is a technical issue that
the central bank is well placed to determine. Thus, for example, deciding how long it should take
for inflation to return to a long-run goal necessarily requires judgment and expertise regarding the
nature of the inflation process and its interaction with real activity. That need for judgment and
expertise argues for having the central bank set medium-term goals because the speed with which it
can achieve them depends on the lags of monetary policy. Whether the central bank or the
government should set medium-term inflation targets is therefore an open question.

8. Commitment to a Nominal Anchor
14

The distinction between goal and instrument independence was first made by Debelle and Fischer (1994) and
Fischer (1994).

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The inability of monetary policy to boost employment in the long run, the importance of
expectations, the benefits of price stability, and the time-inconsistency problem are the reasons
that commitment to a nominal anchor--i.e., stabilization of a nominal variable such as the
inflation rate, the money supply, or an exchange rate--is crucial to successful monetary policy
outcomes.
An institutional commitment to price stability via establishing a nominal anchor provides
a counterbalance to the time-inconsistency problem because it makes it clear that the central
bank must focus on the long-run and thus resist the temptation to pursue short-run expansionary
policies that are inconsistent with the nominal anchor. Commitment to a nominal anchor can
also encourage the government to be more fiscally responsible, which also supports price
stability. For example, persistent fiscal imbalances have, in the absence of a strong nominal
anchor, led some governments, particularly in less-developed economies, to resort to the socalled inflation tax--the issuing/printing of money to pay for goods and services that leads to
more inflation and is thus inconsistent with price stability.
Commitment to a nominal anchor also leads to policy actions that promote price stability,
which helps promote economic efficiency and growth. The commitment to a nominal anchor
helps stabilize inflation expectations, which reduce the likelihood of “inflation scares,” in which
expected inflation and interest rates shoot up (Goodfriend, 1993). Inflation scares lead to bad
economic outcomes because the rise in inflation expectations leads not only to higher actual
inflation but also to monetary policy tightening to get inflation back under control that often
results in large declines in economic activity. Commitment to a nominal anchor is therefore a
crucial element in the successful management of expectations; and it is a key feature of recent
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theory on optimal monetary policy, referred to as the new-neoclassical (or new-Keynesian)
synthesis (Goodfriend and King, 1997; Clarida, Gali, and Gertler, 1999; Woodford, 2003). A
successful commitment to a nominal anchor has been found to produce not only more-stable
inflation but lower volatility of output fluctuations ( Fatás, Mihov, and Rose, 2007; Mishkin and
Schmidt-Hebbel, 2002, 2007).

9. Financial Frictions and the Business Cycle

Research that outlined how asymmetric information could impede the efficient
functioning of the financial system (Akerlof, 1970; Myers and Majluf, 1984; and Greenwald,
Stiglitz, and Weiss, 1984) suggests an important link between business cycle fluctuations and
financial frictions. When shocks to the financial system increase information asymmetry so that
financial frictions increase dramatically, financial instability results, and the financial system is
no longer able to channel funds to those with productive investment opportunities, with the result
that the economy can experience a severe economic downturn (Mishkin, 1997). The rediscovery
of Irving Fisher’s (1933) paper on the Great Depression led to the recognition that financial
instability played a central role in the collapse of economic activity during that period (Mishkin,
1978; Bernanke, 1983; and the survey in Calomiris, 1993), and it has spawned a large literature
on the role of financial frictions in business cycle fluctuations (e.g., Bernanke and Gertler, 1999,
2001; Bernanke, Gertler, and Gilchrist, 1999; Kashyap and Stein, 1994). Indeed, it is now well
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understood that the most severe business cycle downturns are always associated with financial
instability, not only in advanced countries but also in emerging-market countries (Mishkin, 1991,
1996). Minimizing output fluctuations thus requires that monetary policy factors in the impact
of financial frictions on economic activity.

II.
Advances in the Applied Science of Monetary Policy

Scientific principles are all well and good, but they have to be applied in a practical way
to produce good policies. The scientific principles from physics or biology provide important
guidance for real-world projects, but it is with the applied fields of engineering and medicine that
we build bridges and cure patients. Within economics, it is also important to delineate the use of
scientific principles in policymaking, as this type of categorization helps us understand where
progress has been made and where further progress is most needed. I will categorize the applied
science of monetary policy as those aspects that involve systematic, or algorithmic, methods
such as the development of econometric models. Other, more judgmental aspects of
policymaking are what I will call the “art” of policymaking.
So, how have the basic scientific principles outlined above been used algorithmically? I
focus particularly on the U.S. examples because they are the ones I am most familiar with given

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my experience as an American central banker, but similar developments have occurred
elsewhere.
Early Keynesian econometric models of the macroeconomy did not give monetary policy
a prominent role (for example, Tinbergen, 1939; Adelman and Adelman, 1959; Klein, 1968). In
contrast, the policy-oriented models developed in the 1960s--such as the MIT-Penn-SSRC
(MPS) model, developed by Franco Modigliani and collaborators and used as the workhorse
model for policy analysis at the Federal Reserve until 1996--incorporated a very important role
for monetary policy, broadly similar to the main channels of the monetary policy transmission
mechanism that are embedded in the current generation of models.15
In this sense, the notion that inflation is a monetary phenomenon has been embedded in formal
models for several decades.
Very early versions of the MPS model did display a long-run tradeoff between
unemployment and inflation, as the principle that there should be no long-run tradeoff took some
time to be accepted (e.g., Gramlich, 2004). By the early 1970s, the principle of no long-run
tradeoff was fully ensconced in the MPS model by the adoption of an accelerationist Phillips
curve (Pierce and Enzler, 1974; Brayton and others, 1997). The recognition in their models that
lower unemployment could not be bought by accepting higher inflation was a factor driving
central banks to adopt anti-inflationary policies by the 1980s.
Although accelerationist Phillips curves became standard in macroeconometric models
used at central banks like the MPS model through the 1970s, expectational elements were still
largely missing. The next generation of models emphasized the importance of expectations. For
example, the staff at the Board of Governors of the Federal Reserve System developed their
15

Brayton and Mauskopf (1985) describe the MPS model. As pointed out by Gramlich (2004), the researchers at
the Federal Reserve were instrumental in the building of this model and it might more accurately be described as the
Fed-MIT model or the Fed-MIT-Penn model.

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next-generation model, FRB/US (Brayton and Tinsley, 1995; Reifschneider, Stockton, and
Wilcox, 1997; Reifschneider, Tetlow, and Williams, 1999), to incorporate the importance of
expectations in the determination of real activity and inflation. The FRB/US model, and similar
models developed at other central banks such as the Bank of Canada’s QPM model (Coletti and
others, 1996) and the Reserve Bank of New Zealand’s FPS model (Hunt, Rose, and Scott, 2000)
were an outgrowth of the rational expectations revolution, and they allowed expectations to be
derived under many different assumptions, including rational expectations. Policy simulations to
help guide monetary policy decisions, such as those that are shown to the Federal Open Market
Committee (FOMC), explicitly emphasize assumptions about future expectations and how they
are formed. Policymakers have thus come to recognize that their decisions about policy involve
not only the current policy setting but also how they may be thinking about future policy
settings.
The focus on optimizing economic agents coming out of the rational expectations
revolution has led to modeling efforts at central banks that not only make use of rational
expectations, but that are also grounded on sounder microfoundations. Specifically, these
models build on two recent literatures, real business cycle theory (e.g., Prescott, 1986) and newKeynesian theory (e.g., Mankiw and Romer, 1991). In contrast to older Keynesian macro
modeling, new-Keynesian theory provides microfoundations for Keynesian concepts such as
nominal rigidities, the non-neutrality of money, and the inefficiency of business cycle
fluctuations by deriving them from optimizing behavior. The real business cycle approach
makes use of stochastic general equilibrium growth models with representative, optimizing
agents. The resulting new class of models, in which new-Keynesian features such as nominal
rigidities and monopolistic competition are added to the frictionless real business models, have
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become known as dynamic stochastic general equilibrium (DSGE) models. Simple versions of
such models have already provided a framework in which to think about key aspects of monetary
policy design--insights perhaps best illustrated in the Woodford (2003) discussion of policy
issues in the now-textbook, three-equation new-Keynesian model. Larger, more empiricallymotivated DSGE models are now in their early stages of development and are beginning to be
used for policy analysis at central banks (e.g., at the European Central Bank, Smets and Wouters,
2003, and Coenen, McAdam, and Straub, 2007; and at the Federal Reserve Board, Erceg,
Guerrieri, and Gust, 2006, and Edge, Kiley, and Laforte, 2007).
There are two very important implications from policy analysis with DSGE models, as
emphasized by Gali and Gertler (forthcoming): First, “monetary transmission depends critically
on private sector expectations of the future path of the central bank’s policy instrument.”
Second, “the natural (flexible price equilibrium) values of both output and the real interest rate
provide important reference points for monetary policy--and may fluctuate considerably.” I can
attest that both of these propositions indeed are now featured in the Bluebook (the staff’s main
document for analyzing policy options for the FOMC) .
The basic logic of the Taylor principle--that is, raising nominal interest rates more than
one-for-one in response to an increase in inflation--was developed in conjunction with the
analysis of Taylor’s multicountry model and other macroeconometric models (Taylor, 1993a,b;
Bryant, Hooper, and Mann, 1993). However, although the Taylor principle is
a necessary condition for good monetary policy outcomes, it is not sufficient. Central bankers
require knowledge about how much difference the Taylor principle makes to monetary policy
outcomes. They also require an understanding of how much greater than one the response of
nominal interest rates should be to increases in inflation and also need to know how the policy
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rate should respond to other variables. Studying the performance of different rules in
macroeconometric models has become a major enterprise at central banks, and the conclusion is
that the Taylor principle is indeed very important. Analysis of policy rules in macroeconometric
models that are not fully based on optimizing agents has been very extensive (e.g., Bryant,
Hooper, and Mann, 1993; Levin, Wieland, and Williams, 1999), and we are now seeing similar
analysis using DSGE models (e.g., Levin and others, 2006; Schmitt-Grohé and Uribe, 2006).
The second principle, and the sixth through the eighth principles -- which emphasize the
benefits of price stability and the importance of the time-inconsistency problem, central bank
independence and a commitment to a nominal anchor -- have important applications to the
design of monetary policy institutions.
The argument that independent central banks perform better and are better able to resist
the pressures for overly expansionary monetary policy arising from the time-inconsistency
problem has led to a remarkable trend toward increasing central bank independence. Before the
1990s, only a few central banks were highly independent, most notably the Bundesbank, the
Swiss National Bank, and, to a somewhat lesser extent, the Federal Reserve. Now almost all
central banks in advanced countries and many in emerging-market countries have central banks
with a level of independence on par with or exceeding that of the Federal Reserve. In the 1990s,
greater independence was granted to central banks in such diverse countries as Japan, New
Zealand, South Korea, Sweden, the United Kingdom, and those in the euro zone.
The increasing recognition of the time-inconsistency problem and the role of a nominal
anchor in producing better economic outcomes has been an important impetus behind increasing
central banks’ commitments to nominal anchors. One resulting dramatic development in recent
years has been a new monetary policy strategy, inflation targeting--the public announcement of
-18-

medium-term numerical targets for inflation with commitment and accountability to achieve this
target, along with increased transparency of the monetary policy strategy through communication
with the public (Bernanke and Mishkin, 1997). There has been a remarkable trend toward
inflation targeting, which was adopted first by New Zealand in March 1990, and has since been
adopted by an additional 23 countries (Rose, 2006). The evidence, is in general quite favorable
to inflation targeting, although countries that have adopted inflation targeting have not improved
their monetary policy performance beyond that of nontargeters in industrial countries that have
had successful monetary policy (e.g., Bernanke and others, 1999; Mishkin and Schmidt-Hebbel,
2002, 2007; Rose, 2006). And, in contrast to other monetary policy regimes, no country with its
own currency that has adopted inflation targeting has been forced to abandon it.16
The scientific principle that financial frictions matter to economic fluctuations has led to
increased attention at central banks to concerns about financial stability. Many central banks
now publish so-called Financial Stability reports, which examine vulnerabilities to the financial
system that could have negative consequences for economic activity in the future. Other central
banks are involved in prudential regulation and supervision of the financial system to reduce
excessive risk-taking that could lead to financial instability. Central banks also have designed
their lending facilities to improve their ability to function as a lender of last resort, so they can
provide liquidity quickly to the financial system in case of financial disruptions.

III.
The Art of Monetary Policy

16

Spain and Finland gave up inflation targeting when they entered the euro zone.

-19-

I have argued that there have been major advances in the science of monetary policy in
recent years, both in terms of basic scientific principles and applications of these principles to the
real world of monetary policymaking. Monetary policy has indeed become more of a science.
There are, however, serious limitations to the science of monetary policy. Thus, as former vicechairman of the Federal Reserve Board, Alan Blinder (1998, p.17), has emphasized, “central
banking in practice is as much art as science.” By “art,” I mean the use of judgment--judgment
that is informed by economic theory and data but in a manner that is less explicitly tied to formal
models or algorithms.
There are several reasons why judgment will always be an important element in the
conduct of monetary policy. First, models are able to make use of only a small fraction of the
potentially valuable information that tells us about the complexity of the economy. For example,
there are very high frequency data--monthly, weekly, and daily--that are not incorporated into
macroeconometric models, which are usually estimated on quarterly data. These high-frequency
data can often be very informative about the near-term dynamics of the economy and are used
judgmentally by central-bank forecasters (e.g., Reifschneider, Stockton, and Wilcox, 1997).
Second, information that can be very useful in forecasting the economy or deciding
whether a particular model makes sense is often anecdotal and is thus not easily quantifiable.
The Federal Reserve makes extensive use of anecdotal information in producing its forecasts.
The staff at the Board and the Federal Reserve Banks monitor a huge amount of anecdotal
information, and such information is discussed extensively in the publicly released Beige Book,
which reports information from contacts in the Federal Reserve Districts, and by the participants
in FOMC meetings.

-20-

Third, although monetary policy makers make extensive use of models in both
forecasting and evaluating different policies, they are never sure that one model is the correct
one. Active, and sometimes bitter, debates about which modeling approaches are the right ones
are ongoing in macroeconomics, and there often is not a consensus on the best model. As a
result, central banks must express some degree of humility regarding their knowledge of the
structural relationships that determine activity and prices. This humility is readily apparent in
the practice at central banks, which involves looking at many different models--structural,
reduced-form, general equilibrium and partial equilibrium, and continually using judgment to
decide which models are most informative.
Fourth, the economy does not stand still but, rather, changes over time.

Economic

relationships are thus unlikely to remain stable, and it is not always clear how these relationships
are changing.17 Therefore, policymakers must sometimes put less weight on econometrically
estimated equations and instead make informed guesses about how the economy will evolve.
Fifth, as part of managing expectations, monetary policy makers communicate with
economic agents who are not automatons but instead process information in complex ways.
Subtle changes can make a big difference in the effectiveness of communication strategies--i.e.,
details matter--and judgment is therefore always an important element of good communication.18
Although, for the reasons outlined above, judgment will always be a necessary element of
monetary policy, good decisions require that judgment be disciplined--not too ad hoc--and be
well informed by the science of monetary policy. As Blinder (1998, p. 17), has put it,
“Nonetheless, while practicing this dark art, I have always found the science quite useful.” Here
17

The housing channel is one example in which the monetary transmission mechanism has changed substantially
and is likely to continue to do so over time, e.g., Bernanke (2007) and Mishkin (2007c).
18
Because subtle details matter, there is an important rationale for the use of case studies to research best practice in
central bank communication strategies and this is why I have been drawn to case-study research (Bernanke and
Mishkin, 1992; Bernanke and others, 1999; Mishkin, 1999).

-21-

I will discuss two recent episodes in the United States--the financial-headwinds period in the
early 1990s and the new-economy, productivity burst of the late 1990s--to illustrate how
judgment informed by science was able to produce good economic outcomes.

Financial Headwinds in the Early 1990s

The last scientific principle discussed in the paper’s first section emphasizes the link
between financial frictions and the business cycle, but it is unfortunately quite hard to model the
role of these frictions in a general equilibrium, macroeconometric model. The late 1980s saw a
boom and then a major bust in the commercial real estate market leading to huge loan losses that
caused a substantial fall in capital at depository institutions (banks). At the same time, regulators
were raising bank capital requirements to ensure compliance with the Basel Accord. The
resulting capital shortfalls meant that banks had to either raise new capital or restrict their asset
growth by cutting back on lending. Because of their weak condition, banks could not raise much
new capital, so they chose the latter course. The resulting slowdown in the growth of credit was
unprecedented in the post-World War II era (Reifschneider, Stockton, and Wilcox, 1997).
Because banks have informational advantages in making certain loans (e.g., Mishkin, 2007a),
many bank-dependent borrowers could no longer get access to financing and thus had to cut back
on their spending.
Although the large-scale macromodel then in use at the Federal Reserve Board did not
explicitly have financial frictions in its equations, officials at the Federal Reserve were aware
that these frictions could be very important and were concerned that they might be playing a
critical role at that juncture. In part reflecting this concern, many Fed economists were actively
-22-

engaged in research on the impact of bank credit on economic activity. This research, together
with anecdotal reports that businesses were finding themselves credit constrained and survey
information indicating that bank credit standards were being tightened, gave rise to the view
among Federal Reserve policymakers that the capital crunch at banks was noticeably
constraining credit flows and hence spending by households and firms. Indeed, Federal Reserve
Chairman Alan Greenspan (1992) suggested that financial conditions in the early-1990s was
holding back activity like a “50-mile per hour headwind,” and in that period the FOMC reduced
the federal funds rate to levels well below that suggested by the Taylor rule (e.g., Rudebusch,
2006). Indeed, the recovery from the 1990-91 recession was very slow, and the Fed kept the
federal funds rate at 3 percent (which, with an inflation rate of around 3 percent, implied a real
rate of zero) until February of 1994--a very accommodative policy stance. The Fed’s
expansionary policy stance at the time has in hindsight been judged as very successful, with the
economy finally recovering and inflation remaining contained.

The New-Economy, Productivity Burst of the late 1990s

By the beginning of 1997, the unemployment rate had declined to 5.3 percent, and the
Board staff was forecasting that the unemployment rate would fall to 5 percent--an outcome that
followed by midyear. The forecast of a 5 percent unemployment rate was well below most
estimates of the NAIRU (nonaccelerating inflation rate of unemployment). As a result, the staff
forecast was for a rise in inflation (Svensson and Tetlow, 2005). The staff forecast and the
recommendation in the February Bluebook suggested that a period of monetary policy tightening
would be needed to “forestall a continuous rise in core inflation” (Federal Reserve Board, 1997,
-23-

p. 7). Although the FOMC did raise the federal funds rate in March 1997, it desisted from
raising rates further; in fact, the FOMC reduced the federal funds rate in the fall of 1998 after the
episode involving the Long-Term Capital Management hedge fund and the Russian-bond
meltdown. Despite an unemployment rate continually below estimates of the NAIRU, the
outcome was not the acceleration that the Board staff’s models predicted (Svensson and Tetlow,
2005; Tetlow and Ironside, 2006) but instead a decline in the inflation rate.
Why did the FOMC hold off and not raise rates in the face of economic growth that was
forecasted to be far in excess of potential growth--a decision that, ex post, appears to have
resulted in desirable outcomes for inflation and employment? The answer is that Fed Chairman
Greenspan guessed correctly that something unusual was going on with productivity. For
example, he was hearing from businesspeople that new information technologies were
transforming their businesses, making it easier for them to raise productivity. He was also a big
fan of the historical work by Paul David (1990), which suggested that new technological
innovations often took years to produce accelerations in productivity in the overall economy
(Meyer, 2004). Chairman Greenspan was led to the conclusion that the trend in productivity
growth was accelerating, a conclusion that the Board staff’s forecast did not come to fully accept
until late 1999 (Svensson and Tetlow, 2005). Moreover, he appeared to be convinced that the
acceleration in productivity would cap inflationary pressures, implying that inflation would not
accelerate even with rapid economic growth. His view prevailed in the FOMC (Meyer, 2004).19
The types of information used to foresee the effects of a productivity acceleration are
inherently difficult to incorporate into formal models. This is obvious with respect to the
anecdotes I have mentioned. But even the systematic data available at the time required the use
19

Chairman Greenspan’s successful use of judgment during this period is one reason why he was dubbed the
“maestro” by Woodward (2000).

-24-

of judgment. For example, part of the story of the late 1990s reflected the different signals being
sent by real-time measures of gross domestic product and gross domestic income--or at least the
component of the latter produced by nonfinancial corporations, which is perhaps better measured
(Corrado and Slifman, 1999) and provided some advance signal of the productivity acceleration.
Of course, these two measures--GDP and GDI--are the same in our formal models, and only a
judgmental filtering of the information content in each can be useful in real time.
Good judgment benefits not only from a good feel for the data and the successful
processing of anecdotal information but also from the use of scientific models, and the
late-1990s episode is no exception. At the July 1997 FOMC meeting, the Board staff presented
simulations using the FRB/US model examining what would happen if productivity were to
accelerate (Meyer, 2004; Tetlow and Ironside, 2006). Their simulations produced several results
that were consistent with what seemed to be happening. An acceleration of productivity would
raise profits and the value of equities, which would boost aggregate demand because higher
stock values would stimulate business investment and boost consumer spending through wealth
effects. The acceleration in productivity would also be disinflationary and could therefore
explain why inflation would fall despite a declining unemployment rate. An unexpected rise in
productivity growth would not be immediately reflected in higher wage rates, so unit labor costs
(wages adjusted for productivity growth) would fall, leading to a decline in inflation. Another
way of looking at this is through the NAIRU framework. For a given rate of unemployment, an
unexpected acceleration in productivity would produce an inflation rate lower than it otherwise
would be, so that the NAIRU at which the unemployment rate would not lead to an acceleration
of inflation would decline. As events unfolded in line with these simulation results, the FOMC

-25-

became more convinced that a productivity boom was under way and that there was less need for
a monetary tightening.
The two episodes discussed here illustrate several points about the art of central banking.
First, monetary policy is more likely to produce better outcomes when central bankers recognize
the limitations of their formal models. However, judgment cannot be undisciplined. The
accuracy of judgment is likely to be enhanced when it is informed by the science of monetary
policy, either through use of model simulations or applications of basic scientific principles.

IV.
Further Advances to Make Monetary Policy More of a Science

Although art will always be a feature of monetary policy, the science of monetary policy
will keep advancing, making monetary policy more of a science. In this section I will briefly
discuss where I think future advances in the science of monetary policy are likely to be made.
The push to build sound microfoundations into general equilibrium macroeconometric
models is ongoing as the expanding literature on DSGE models indicates (survey in Gali and
Gertler, forthcoming; and the discussions of model enhancements in Erceg, Gust, and Guerrieri,
2006, and in Edge, Kiley, and Laforte, 2007). However, these DSGE models are only now
starting to be brought to the data and are not nearly as rich in their coverage of features of the
economy as are older, more-Keynesian models such as FRB/US.20 Models like FRB/US do have
elements that are more ad hoc, but at the current juncture central bankers see them as more

20

To be fair, models like FRB/US do have much in common with DSGE models in that many of their equations, but
not all, are built on solid microfoundations.

-26-

realistic. Building macroeconometric models thoroughly grounded on solid microfoundations,
but with treatment of more sectors of the economy, will be one of the main challenges for the
science of monetary policy in the future.
Nominal rigidities are central to understanding quantitatively the impact of monetary
policy on the economy. The canonical DSGE model makes use of a simple new-Keynesian
Phillips curve framework because it makes the model very tractable.21 This framework is highly
stylized, however, and does not allow for endogenous changes in how often contracts are
renegotiated. Furthermore, there may be other reasons why prices are not reset too often, such as
rational inattention.22 Better explanations--and more empirical validation--regarding the source
of nominal rigidities may lead to important advances in the science of monetary policy.23
Tractability has led to models based on microfoundations, such as DSGE models, to rely
on representative agents, which is a serious drawback. I have a strong sense that what drives
many macroeconomic phenomena that are particularly interesting is heterogeneity of economic
agents. Building heterogeneous agents into macroeconometric models will by no means be easy,
but it has the potential to make these models much more realistic. Furthermore, it may allow us
to understand the link between aggregate economic fluctuations and income distribution, a hot
topic in political circles. Heterogeneity of economic agents is also crucial to understanding labor
market frictions. In some DSGE models, all fluctuations in employment are from variation in
hours per worker, and yet in the real world, changes in unemployment are a more important
source of employment fluctuations. Bringing the search and matching literature more directly

21

These models often use the Calvo (1983) staggering construct or the quadratic adjustment costs of Rotemberg
(1982); these specifications yield identical Phillips curve specifications.
22
Mankiw and Reis (2002) introduce this type of model; Kiley (2007) compares the ability of this type of model to
improve upon the fit of more familiar sticky-price models.
23
Microeconomic studies have begun to make interesting progress (e.g., Bils and Klenow, 2004; Nakamura and
Steinsson, 2006).

-27-

into microfounded macroeconometric models will make them more realistic and also allow better
welfare comparisons of different monetary policies.
Although, as discussed above, monetary policy makers understand the importance of
financial frictions to the business cycle, general equilibrium macroeconometric models, for the
most part, ignore financial market imperfections. Research has begun to incorporate financial
market imperfections into quantitative dynamic general equilibrium models (e.g., Bernanke,
Gertler, and Gilchrist, 1999), and some of this research has even begun to estimate these types of
DSGE models (e.g., Christiano, Motto, and Rostagno, 2007). But we need to know a lot more
about the how to scientifically incorporate financial frictions into policy deliberations. For the
time being, the role for art is this area is very important.
The new field of behavioral economics, which makes use of concepts from other social
sciences such as anthropology, sociology, and, particularly, psychology, suggests that economic
agents may not always be the rational, optimizing agents we assume in our models. Embedding
behavioral economics into macro models can make a major difference in the way these models
work (Akerlof, 2007). How important are deviations from rationality to our views on the
monetary transmission mechanism, and what are welfare-enhancing monetary policies? How
can systematic deviations from rationality be modeled in a serious way and built into
macroeconometric models? Answers to these questions may further enhance the realism of
macroeconometric models used for policy purposes.
One of the rationales for the use of judgment (art) in the conduct of monetary policy is
that the economy is not stationary, but rather is changing all the time. This means that economic
agents are continually learning about the state of the economy, so the rational expectations
assumption that depends on stationarity to derive expectations often may not be valid. Research
-28-

on the how agents learn and its implications for business cycles is an active area of research
(Bullard and Mitra, 2002; Evans and Honkapohja, 2003) that should have major payoff in
helping us to better understand the impact of monetary policy on the economy.
Another rationale for keeping art in monetary policymaking is that we can never be sure
what is the right model of the economy. As I mentioned earlier, this argues for humility at
central banks. It also argues for advances in scientific techniques to think about which monetary
policies are more robust in producing good economic outcomes. Research in this area is also
very active. One approach examines parametric uncertainties in which methods are examined to
ensure that a prescribed policy works well in an entire class of models (e.g., Levin, Wieland, and
Williams, 1999). Nonparametric approaches look at designing policies that protect against
model misspecifications that cannot be measured (e.g., Hansen and Sargent, forthcoming; Tetlow
and von zur Muehlen, 2001).
The list of areas here that will advance the science of monetary policy is necessarily
incomplete. Some of the most important advances in economic science are often very hard to
predict.

V.
Concluding Remarks

The science of monetary policy has come a long way over the past fifty years, and I
would argue that its advances are an important reason for the policy successes that so many
countries have been experiencing in recent years. Monetary policy will however never become
as boring as dentistry. Monetary policy will always have elements of art as well as science.
-29-

(That is good news because it will keep life interesting for monetary economists like me.)
However, the advances in the science of monetary policy that I have described here suggest that
monetary policy will become more of a science over time. Furthermore, even though art will
always be a key element in the conduct of monetary policy, the more it is informed by good
science, the more successful monetary policy will be.

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08-30-07

Figure 1: Headline Inflation
United States

United Kingdom*

12-month percent change

12-month percent change

28

28

24
20
16
12

1980

1985

1990

1995

2000

2005

16
12

8
4
0
1975

24
20

8
4
0

-4

1975

1980

1985

1990

1995

2000

2005

-4

*Inflation measured by RPI index.

Germany*

France*

12-month percent change

1975

1980

1985

1990

1995

2000

2005

28
24
20
16
12
8
4
0
-4

*Inflation measured by national consumer price index.

12-month percent change

1975

1980

1985

1985

1990

1995

2000

2005

28
24
20
16
12
8
4
0
-4

1975

1980

1985

1985

1990

1995

1990

1995

2000

2005

28
24
20
16
12
8
4
0
-4

Switzerland

12-month percent change

1980

2005

4-quarter percent change

Sweden

1975

2000

Australia

12-month percent change

1980

1995

*Inflation measured by national consumer price index.

Canada

1975

1990

28
24
20
16
12
8
4
0
-4

2000

2005

28
24
20
16
12
8
4
0
-4

-41-

12-month percent change

1975

1980

1985

1990

1995

2000

2005

28
24
20
16
12
8
4
0
-4

08-30-07

Figure 2: Standard Deviation of Headline Inflation
(5-year window)
United States

United Kingdom*
Standard deviation

Standard deviation

7

7

6

1995

2000

2005

2

1
1990

3

2

1985

4

3

1980

5

4

1975

6

5

1

0

1975

1980

1985

1990

1995

2000

2005

0

*Inflation measured by RPI index.

Germany*

France*
Standard deviation

Standard deviation

7

7

6

1995

2000

2005

2

1
1990

3

2

1985

4

3

1980

5

4

1975

6

5

1

0

1975

*Inflation measured by national consumer price index.

1980

1985

1990

1995

2000

2005

0

*Inflation measured by national consumer price index.

Canada

Australia
Standard deviation

Standard deviation

7

7

6

1995

2000

2005

2

1
1990

3

2

1985

4

3

1980

5

4

1975

6

5

1

0

1975

1980

1985

Sweden

1990

1995

2000

2005

0

Switzerland
Standard deviation

Standard deviation

7

7

6

1995

2000

2005

2

1
1990

3

2

1985

4

3

1980

5

4

1975

6

5

1

0

1975

-42-

1980

1985

1990

1995

2000

2005

0

08-30-07

Figure 3: Standard Deviation of Output Growth
(5-year window)
United States

United Kingdom
Standard deviation

Standard deviation

8

8

7
6
5
4

1980

1985

1990

1995

2000

2005

5
4

3
2
1
1975

7
6

3
2
1

0

1975

1980

1985

Germany

1980

1985

1990

1995

2000

2005

1985

1990

1975

1980

1985

1995

2000

2005

1985

1990

0

1995

2000

2005

8
7
6
5
4
3
2
1
0

1975

1980

1985

1990

1995

2000

2005

8
7
6
5
4
3
2
1
0

Switzerland
Standard deviation

1980

1990

Standard deviation

8
7
6
5
4
3
2
1
0

Sweden

1975

2005

Australia
Standard deviation

1980

2000

Standard deviation

8
7
6
5
4
3
2
1
0

Canada

1975

1995

France
Standard deviation

1975

1990

1995

2000

2005

Standard deviation

8
7
6
5
4
3
2
1
0

1975

-43-

1980

1985

1990

1995

2000

2005

8
7
6
5
4
3
2
1
0

09-06-07

Figure 4: Inflation Compensation 10 years ahead
12-month percent change

Bank of England Granted Independence

10-year Breakeven Inflation

6
5
4

RPIX Inflation

3
2

Old Target < 2.5%

1

New Point Target
1995

1996

1997

1998

Note: RPIX series is not seasonally adjusted; breakeven inflation uses a real bond indexed to RPI inflation.

-44-

1999

0