Full text of Economic Review (Federal Reserve Bank of Atlanta) : Fourth Quarter 2001, Volume 86, Number 4

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On Business Cycles and
Countercyclical Policies
M A R C O A . E S P I N O S A - V E G A
A N D J A N G - T I N G G U O
Espinosa-Vega is a senior economist in the macropolicy
section of the Atlanta Fed’s research department. Guo is
an associate professor at the University of California,
Riverside. They thank Ellis Tallman for comments on
an earlier version and are particularly grateful to
Steve Russell for insightful suggestions. EspinosaVega dedicates the article to Diana and Carl.

O

VER THE LAST TEN YEARS, THE

U.S. ECONOMY EXPERIENCED ONE OF THE LONGEST ECO-

NOMIC EXPANSIONS IN ITS HISTORY.
REAL GROSS DOMESTIC PRODUCT

HOWEVER, SINCE THE THIRD QUARTER OF 2000, ITS

(GDP) HAS EXPERIENCED SIGNIFICANTLY LOWER RATES OF

GROWTH, ON AN ANNUALIZED BASIS, THAN THOSE OBSERVED BETWEEN 1996 AND MID-2000.

Economists are still trying to assess the severity of
this slowdown, and this assessment has clearly become more difficult since the events of September 11.
It is worth emphasizing that this article is silent
about the economic implications of wars and similar
cataclysms and focuses instead on the analysis of
“typical” business cycles. The slowdown that began
prior to September 11 had already served as a
reminder that the business cycle is still alive—that
the U.S. economy is likely to continue to experience
both expansions and contractions. This situation
raises the following questions: What do we know
about the driving forces behind the business cycle?
What should policymakers do in the face of economic fluctuations?
Not surprisingly, there are a number of competing
explanations for business cycles, and there is no
shortage of policy recommendations. This article
focuses on only two of these explanations: the animal spirits theory and the real business cycle theory.
The former is closely connected with the Keynesian
economic tradition and identifies market participants’ mood swings as the key source of economic
fluctuations. The second explanation is rooted in the
classical economic tradition and views productivity
shocks as the driving force behind economic fluc-

tuations. These explanations are examined because
they are some of the better-known and most widely
quoted business cycle theories among academic
economists. Both theories meet modern academic
standards—one of them from its inception and the
other after a significant reformulation. Modern academic standards explicitly acknowledge the dynamic
nature of economic decisions—that macroeconomic
variables interact with each other in such a way that
the relevant economic relations must be considered
simultaneously—and the importance of microeconomic theory as a sound foundation for macroeconomic theory.
In addition to reviewing these two theories, the
article looks at what they suggest about countercyclical policies—policies aimed at trying to eliminate business cycle fluctuations or insulate market
participants from the effects of these fluctuations.
This article first presents the “everyday” adaptation of the original animal spirits explanation for
business cycles and then sketches the foundations
of the real business cycle and the reformulated animal spirits explanations. The article next reviews
the real business cycle and Neo-Keynesian views and,
finally, discusses the policy implications of these
two theories.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

1

The Keynesian (Nonfundamentals) Approach
ne popular explanation for the source of the
business cycle is that fluctuations in private
spending are induced by so-called animal
spirits.1 That is, economic fluctuations result from
waves of overpessimism or overoptimism, affecting
households and firms, that are not directly connected
to economic fundamentals but may nevertheless
become self-fulfilling.
A report on a popular Internet site earlier this year
reflects this belief. “The latest economic reports confirm what people on Wall Street and Main Street
already knew: the U.S. economy slowed sharply at the
end of last year. Consumers are largely to blame. They
reined in spending,
which accounts for
two-thirds of the U.S.
According to the real busieconomy; as their conness cycle theory, there is
fidence fell to a fournothing the government
year low, so did their
spending” (www.cnn.
can do to eliminate busicom, January 2001).
ness cycle fluctuations.
This statement
According to Farmer and
suggests that a fall in
consumer confidence,
Guo, in contrast, governinduced perhaps by
ments may be able to
animal spirits, has dridesign policies to moderate
ven down personal
consumption expeneconomic fluctuations.
ditures, in turn dragging down output.
This logic is a faithful reproduction of the so-called
spending hypothesis attributed to Keynes and found
in many macroeconomics textbooks. In a review of
the Great Depression, Mankiw (1992a) addresses
the question that originally motivated Keynes: What
caused the Great Depression? Mankiw describes
what he calls the spending hypothesis of what
caused the Depression. “The spending hypothesis
. . . places primary blame for the Depression on an
exogenous fall in spending on goods and services.
Economists have attempted to explain this decline
in spending in several ways. Some argue that a
downward shift in the consumption function caused
the contractionary shift. . . . The stock market crash
of 1929 may have been partly responsible for this
decline in consumption. By reducing wealth and
increasing uncertainty about future prospects for
the U.S. economy, the crash may have induced consumers to save more of their income [italics added]”
(1992a, 284–85).
In fairness to Keynes, his conjecture was that a
drop in consumption was part of the explanation
behind business cycles. The second part of the
explanation had to do with why the resources that

O

2

became available following a drop in aggregate consumption did not find their way into the investment
sector of the economy, thus preventing market participants’ mood swings from becoming self-fulfilling.
This question is a difficult one that many economists continue to struggle with, and therefore it is
not always reported in textbooks. Because many
commentators are exposed only to the first part of
Keynes’s explanation, it is referred to in this discussion as the everyday Keynesian explanation.
Of course, for this logic to apply, one would first
have to show that a fall in consumer confidence drags
down private consumption spending. Assuming for
the moment that this statement is true, the question
remains, Why would changes in private consumption
cause fluctuations in output or GDP? The explanation
according to the everyday Keynesian theory can be
illustrated as follows. Suppose a shoemaker’s customers suddenly, for whatever reason, become very
pessimistic about their future income, inducing them
to slash their consumption across the board. As a consequence, the shoemaker might see a significant drop
in shoe sales, forcing him to reduce his production.
Extrapolating from the shoemaker’s actions, commentators might conclude that there is likely to be a drop
in GDP if most producers of consumer goods and services experience a simultaneous decrease in sales.2 It
is clear, then, from this line of reasoning, that lower
private consumption spending causes lower output.
Despite its intuitive appeal, this analysis has been
the subject of criticism and qualifications by academic
economists for a number of decades. More recently,
these objections have stemmed from a theoretical
reexamination of the way households and firms make
their economic decisions at the microeconomic level
and economists beliefs about how these decisions
shape the evolution of the macroeconomy.
The next section presents a primer on the way
modern academic economists describe the economic
decisions of the two basic units that integrate the
macroeconomy: households and firms. These ideas
lay the foundations for the two business cycle theories this article examines.

The Economic Decisions of Households and Firms
acroeconomists build theoretical models
that are meant to provide a plausible representation of a number of features in the
economy. Modern macroeconomists analyze models
in which households make consumption, savings,
and labor supply decisions over their lifetimes,
which span a large number of years, and firms make
their input choices so as to maximize their profits.
Households. For a given stream of projected
income, a household’s key economic decision, which

M

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

takes place continuously and is based in part on a
decision about how many hours to work, is how
much to consume and how much to save. This decision is also based on the household’s degree of frugality. For a given level of income, the more frugal a
household is, the more it will save. Households also
prefer to avoid sharp swings in their consumption
patterns; for instance, most people would rather
have a moderately priced meal most of the time than
eat at La Tour d’Argent for a month and then starve
for the rest of the year. Finally, household savings
consist of increases in net acquisition of financial
assets in financial institutions. These institutions
lend most of their funds to the firms in the economy
to help them acquire capital.
Firms. Every production process, from fast food
restaurants to high-tech services, can be described
as the result of combining two basic inputs: capital
and labor. In most economic models, the goods that
households consume are produced by firms that use
labor and capital as inputs. The level of output
depends on the amount of labor supplied by the
households and on the amount of capital that has
been accumulated over time.
The Interaction of Households and Firms in
the Macroeconomy. The larger the number of
hours worked and the higher the level of accumulated capital, the greater the output level in an economy. At the same time, capital enhances workers’
productivity. In a competitive marketplace, higher
productivity is normally associated with higher pay.
Firms choose the right mix of labor hours and capital to maximize their profits.
The more frugal the households in an economy
are—that is, the lower their contemporaneous consumption expenditures—the more the households
will save and thus the more capital they will accumulate. The larger the amount of capital in an economy, the larger the amount of output produced,
consumed, and invested. Hence, according to this
sketch of how households and firms make economic
decisions at the micro level, it is possible that lower
consumption today will result in higher output

levels tomorrow. Similarly, if animal spirits led
households to slash their consumption, for a given
level of income this reduced consumption would
result in higher savings, additional capital accumulation, and higher output in the near future. Unlike
the everyday Keynesian explanation described earlier, in which a decrease in consumption leads to a
fall in output, in modern macroeconomic models a
drop in consumption produces an increase in savings that will provide the necessary capital to fuel
economic growth.
An example of this phenomenon is the boom
Singapore experienced between 1960 and the mid1980s. Singapore, one of the four “Asian tigers,” saw
a decrease in consumption in the late 1970s. This
drop was caused by a marked increase in household
frugality and was matched by a sharp increase in the
savings rate, which facilitated an investment expansion and an output boom.3 This example suggests
that, as modern macroeconomic analysis predicts,
lower consumption can cause higher GDP.
In sum, the everyday Keynesian analysis predicts that lower private consumption will always
lead to lower output. This prediction is inconsistent with the modern macroeconomic analyses
described above and with the sequence of events
that occurred in Singapore.
The factors that determine how much an economy produces, consumes, and invests are known as
the economy’s fundamentals. These fundamentals
include the total number of hours worked and the
amount of capital in the economy. Economists also
recognize that there are additional fundamental factors that can help explain the ultimate level of GDP.
These additional factors are included in so-called
multifactor or total factor productivity (TFP).4
Factors affecting TFP include a country’s legal
framework, its infrastructure, and its level of technological sophistication. For a given number of hours
worked and a given level of capital in an economy,
higher TFP means higher production capacity. Thus,
TFP reflects the fact that output can be increased
not only by working harder but also by working

1. Originally, the term animal spirits was coined in the context of explaining wild investment swings. In Keynes’s words, “Most,
probably, of our decisions to do something positive . . . can only be taken as a result of animal spirits—of a spontaneous urge
to action rather than inaction. . . . If the animal spirits are dimmed and the spontaneous optimism falters . . . enterprise will
fade and die” (Keynes 1973, 161–62).
2. According to the same logic, a wave of overoptimism would lead to a sharp increase in the sales of consumer goods, and one
would expect a production boom.
3. As reported by Barro (1992), the ratio of real gross investment to real GDP in Singapore was about 13 percent in the early
1960s, reached 21 percent between 1965 and 1969, and then climbed to an average of 37 percent from 1970 to 1985. Per capita
real GDP growth rates from 1960 to 1985 were around 5.8 percent, whereas for the 1960–85 period per capita real consumption grew by only 2.9 percent annually. Therefore, for the 1960–85 period, the relatively low growth in consumption was
matched by a sharp increase in the savings rate, which resulted in an investment and output boom.
4. Solow (1957) was the first economist to develop this idea.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

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CHART 1
U.S. Output, Consumption, Investment, and Labor Hours, 1954:Q1–2001:Q1

B illio n s o f 1996 D o lla r s

10,000

8,000
GDP

6,000
Consumption

4,000

2,000
Investment

1954

1959

1964

1969

1974

Hours

1979

1984

1989

1994

1999

Source: Computed by the authors from data from the Bureau of Economic Analysis and the Bureau of Labor Statistics.

smarter, that is, by combining the same amounts of
inputs in a more efficient fashion.
In a market economy, individuals are rewarded
according to the amount of goods and services they
help produce. A higher level of capital per worker
allows workers to generate more goods and services
per unit of labor input and thus helps raise workers’
compensation. However, efficiency changes
(changes in TFP) can also help explain changes in
workers’ compensation. Other things being equal,
above-average rates of TFP growth (possibly the
result of technological innovation) generate higher
rates of growth in real (inflation-adjusted) wages
because workers are compensated for helping produce more goods and services. Higher wages, in
turn, result in increases in household income, leading to higher consumption and saving. Similarly,
below-average rates of TFP growth reduce the rate
of growth in real wages. Lower wages result in
decreases in household income, leading to lower
consumption and saving. In sum, random shifts in
TFP could cause fluctuations in the total output of
an economy. The view that total factor productivity
has an important role in economic fluctuations has
slowly made its way into business economics and
policy-making circles. For example, in recent testimony (February 2001), Federal Reserve Chairman
Alan Greenspan stated that “crucial to the assessment of the outlook . . . is the role of technological
change . . . in shaping cyclical forces.”
The next section reviews the findings of a wellknown explanation for economic fluctuations: the
real business cycle (RBC) or fundamentals theory.
4

This theory relies on the foregoing analysis of the
two basic units that make up the macroeconomy—
households and firms.

Real Business Cycle Theory:
The Fundamentals Approach
ydland and Prescott (1982) and Long and
Plosser (1983) were the first economists to
recognize the possibility that business cycles
could be caused by random shocks to TFP (technology shocks).5 They started with the observation that
in the post–Korean War U.S. economy, output, consumption, investment, and labor hours are positively
correlated but differ in terms of their volatility over
the business cycle.
To illustrate this empirical fact, Chart 1 plots output, consumption, investment, and labor hours in
the United States from the first quarter of 1954 to
the first quarter of 2001.6 These time series can be
thought of as consisting of two components: the
trend or low-frequency component, which changes
slowly over time, and the cyclical or high-frequency
component—the deviation of the series from its
trend—which moves up and down over the business
cycle. The statistical mathematical procedure that
decomposes a time series into these two components is called detrending. The cyclical component
obtained after detrending is the object of business
cycle analysis. Chart 2 shows the cyclical components (percentage deviations from trend) of some
actual U.S. time series.7 They represents the yardstick against which to measure alternative business
cycle theories’ predictions.

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Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

CHART 2
Cyclical Components of U.S. Output, Consumption, Investment, and Labor Hours, 1954:Q1–2001:Q1

Pe r c e n t a g e D e via t io n fr o m T r e n d

10

5

Hours
Consumption

GDP

0

–5
Investment
–10

1954

1959

1964

1969

1974

1979

1984

1989

1994

1999

Source: Computed by the authors from data from the Bureau of Economic Analysis and the Bureau of Labor Statistics.

Chart 2 shows that consumption, investment, and
hours worked are all procyclical; that is, they all move
in the same direction as output over the business
cycle. Moreover, consumption displays a smoother
pattern than output, labor is about as volatile as output, and investment is more volatile than output over
the business cycle. Table 1 shows summary statistics
on relative volatility and contemporaneous correlation with output for key U.S. aggregates during the
sample period.8
Kydland and Prescott (1982) construct a model
that builds on the assumptions about the behavior
of households and firms that were sketched in the
preceding section. They assume that the prices of
different goods and services adjust readily in
response to changes in the economy’s fundamentals. Furthermore, economywide production of
goods and services is assumed to yield constant
returns to scale—that is, a proportional increase in
the quantity of capital and labor inputs is expected
to increase output by the same proportion.
Chart 3 reproduces Chart 2 along with the cyclical responses of output, consumption, investment,

and labor hours to technology shocks in a single simulation experiment conducted within an RBC model.
Although the model does a good job of matching the
relative volatility of the macroeconomic aggregates,
it does not capture the exact timing of the business
cycle. However, given the relative simplicity of the
model, it is remarkably successful in replicating the
cyclical behavior of key U.S. macroeconomic aggregates revealed in Chart 2.9
Another way of assessing the performance of the
RBC model is to contrast Table 1 with Table 2, which
presents sample means of relative volatility and contemporaneous correlation with output computed for
TA B L E 1
The U.S. Economy, 1954:Q1–2001:Q1

Variable

Relative
Volatility

Correlation
with Output

Output
Consumption
Investment
Labor Hours

1.00
0.50
2.57
0.95

1.00
0.83
0.91
0.87

5. For a very informative tour of the genesis of the shock-based business cycle theory, see Chatterjee (2000).
6. Output is defined as GDP, consumption is defined as private consumption of nondurables plus services, and investment is
defined as nonresidential fixed investment plus consumer durables. All these variables are measured in billions of 1996 dollars.
In addition, labor hours are defined as total manhours of the employed labor force in all industries from the household survey,
measured in billions of hours.
7. The detrending method used in Charts 2–4 is the Hodrick-Prescott filter, which fits a flexible trend through the time series.
The flexible trend reflects the assumption that each of the relevant variables exhibits a slowly changing growth rate over time.
8. Relative volatility is defined as the standard deviation of a variable divided by the standard deviation of output.
9. Subsequent extensions of the real business cycle approach, as labeled by Long and Plosser (1983), have improved the U.S.
data fit (see King and Rebelo 1999 for a survey and the references therein).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

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CHART 3
The Real Business Cycle Model
Out put

Co n su mp tio n

8
2
4

1
0

P e rc e n t a g e D e via t io n fr o m T r e n d

0

–4

–1

Data
RBC Model
55

60

65

70

75

80

85

90

95

–2
00

55

60

65

70

Investment

75

80

85

90

95

00

Ho u rs

20

6
4

10

2
0
0
–10
–2
–20

–4
55

60

65

70

75

80

85

90

95

00

55

60

65

70

75

80

85

90

95

00

Source: Computed by the authors from data from the Bureau of Economic Analysis and the Bureau of Labor Statistics.

fifty simulations of an RBC model. Each simulation
consists of 189 periods, the same number as the
U.S. data sample.
In the RBC artificial economy, the patterns of relative volatility are consistent with the U.S. data
reported in Table 1; that is, investment is the most
volatile, followed by output, labor hours, and then
consumption over the business cycle. Based on this
result, one can conclude that changes in total factor
productivity are a possible cause of fluctuations in
GDP, consumption, investment, and labor hours.
Moreover, all model-generated time series are procyclical in an RBC economy. In particular, output
and consumption are positively correlated over the
business cycle. As discussed earlier, a positive technology shock (or above-average TFP growth) leads
to higher labor hours and higher real wages; therefore, more output is produced, and households raise
their consumption expenditure accordingly. However,
from the RBC point of view, it would make no sense
to blame consumers for an economic slowdown, as
the everyday version of the animal spirits explanation for business cycles would suggest. According
to the RBC explanation, changes in households’
incomes brought about by an unanticipated change
in total factor productivity will induce changes in
both savings and consumption so that the causality
6

does not run from consumption to output but the
other way around.
So far, this article has identified some inconsistencies between the predictions of the everyday
adaptation of the original animal spirits theory and
predictions of modern macroeconomic models. It has
also presented an example that seems to support the
predictions of modern macroeconomic theories.
Finally, it has noted the success of the RBC theory in
matching the fluctuations of U.S. data. Under these
circumstances, one might wonder whether the nonfundamental or animal spirits explanation of the
business cycle should be considered obsolete.
According to a new generation of Keynesian economists, the answer to this question is no. These economists study the cyclical implications of the presence
of animal spirits in models that meet the modern academic standards sketched out in the last two sections. The next section presents a reformulation of
the nonfundamentals explanation that has been put
forward by some Neo-Keynesian economists.

The Neo-Keynesian (Nonfundamentals) Theory
ccording to Mankiw (1992b), a prominent
Keynesian economist, at least some new or
Neo-Keynesians agree with the RBC theorists that it is important for business cycle theory to

A

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

TA B L E 2
The Real Business Cycle Model of the
U.S. Economy, 1954:Q1–2001:Q1

Variable

Relative
Volatility

Correlation
with Output

Output
Consumption
Investment
Labor Hours

1.00
0.29
3.18
0.76

1.00
0.87
0.99
0.98

be consistent with the micro foundations of the macroeconomy. Mankiw states that “Keynesian economics
has been reincarnated into a body with firm microeconomic muscle. . . . Beyond the broad principles
. . . old and new Keynesians differ substantially. . . .
To some old Keynesians, new Keynesian economics
may be hard to recognize as Keynesian at all.
Indeed, new Keynesian economics may appear more
similar to the classical economics of David Hume”
(1992b, 560).
Mankiw makes it clear that he is not the
spokesperson for all Neo-Keynesian economists.
However, it is fair to say that he speaks for a large
body of academic economists who see the business
cycle as a type of economywide market failure, as
Keynes did, but who seek explanations that are
firmly anchored in the analysis of the behavior of
households and firms.
Rather than surveying all Neo-Keynesian studies
of the business cycle, this discussion focuses on a
recent Neo-Keynesian analysis by Farmer and Guo
(1994). This particular study was chosen because it
is consistent with the RBC and Mankiw’s view that
macro predictions should be the consequences of
assumptions made at the micro level.10 However,
Farmer and Guo’s analysis is also faithful to the
Keynesian tradition. They pursue a market failure
explanation for the business cycle, and they study
the possibility that animal spirits or nonfundamental
factors could be the driving force behind business
cycle fluctuations. In addition, they were the first
authors to conduct empirical tests of their theoreti-

cal arguments along the lines of the RBC approach,
thereby permitting a straightforward comparison of
their explanation and the RBC explanation.11
Farmer and Guo’s analysis features an important
departure from the RBC paradigm. Specifically, they
postulate constant returns to scale at the firm level
but economywide increasing returns to scale in production. The assumption of increasing returns to
scale means that a proportional change in labor and
capital inputs generates a more-than-proportional
change in output.12 To say that an economy experiences economywide increasing returns to scale
means that, although individual firms see themselves
as facing constant returns to scale, all of the firms
taken together experience increasing returns to
scale. Hence, Farmer and Guo assume that proportional additions of labor and capital by all individual
firms result in a more-than-proportional increase in
GDP. This possibility is also known as positive externalities in the aggregate production process.13
An example of positive externalities is the development and widespread use of the Internet. As individual firms continue to increase their use of the
Internet, they induce improvements in the distribution, utilization, and management of information at
the economywide level. Farmer and Guo believe
that the assumption that there are externalities in
the aggregate production process provides a better
description of the production technology in the
U.S. economy than the constant-return assumption
favored by RBC theorists.
Armed with modern analytical tools, some of
which were outlined earlier, Farmer and Guo envision an alternative sequence of events leading to
economic fluctuations that have nothing to do with
changes in TFP. Suppose, for whatever reason—say,
an unexpected increase in the ratio of total business
inventory to sales—households become pessimistic
about the future of the economy. Fearing that the
investment financed by their savings is not going to
pan out, households lower their savings today. For
a given level of income, this move would result in
higher consumption. But since households are happy

10. Of course, Farmer and Guo (1994) is not the only Neo-Keynesian work on business cycles. It is, however, one that incorporates most of the elements that academic economists have come to accept as standard in modern macroeconomic models.
For example, Mankiw (1985) presents a static, partial equilibrium analysis with no quantitative analysis of the U.S. business
cycle. On the other hand, Blanchard and Kiyotaki (1987) and Ball and Romer (1990) both examine general equilibrium
models, but these models are static and contain no quantitative business cycle analysis.
11. An alternative Neo-Keynesian analysis that emphasizes animal spirits as the source of business cycles is that of Gali (1994).
His analysis begins with a different source of economywide market failure: monopolistic competition. This characteristic
would make Gali’s model also an ideal Neo-Keynesian study to contrast against the RBC theory. Farmer and Guo’s model
was chosen instead because its theoretical setup and empirical methodology makes it a more transparent alternative to contrast against the RBC approach.
12. See, for example, Farmer and Guo (1994) for a careful justification of this assumption.
13. See, for example, Caballero and Lyons (1992) for empirical support of positive externalities in the U.S. economy.

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CHART 4
The Neo-Keynesian Model
Out put

Co n su mp tio n

6

3

4

2

2

1

0
P e rc e n t a g e D e via t io n fr o m T r e n d

0
–2
–4

Data

–6

N-K Model
55

60

65

70

75

80

85

90

–1
–2
95

00

55

60

65

70

I nvest ment

75

80

85

90

95

00

85

90

95

00

Ho u rs

40

6
4

20

2
0

0
–2
–4

–20

–6
55

60

65

70

75

80

85

90

95

00

55

60

65

70

75

80

Source: Computed by the authors from data from the Bureau of Economic Analysis and the Bureau of Labor Statistics.

with their initial choices of consumption (assuming
that households favor smooth consumption patterns), they will reduce their supply of labor just to
the point where they can earn enough to consume
what they were consuming prior to the outbreak of
pessimism. Therefore, employment will be predicted
to fall at this stage.
Since aggregate production in the Farmer-Guo
framework is subject to increasing returns, a
decrease in labor supply may reduce labor productivity. If so, this decline leads to a drop in the
demand for labor at every level of wages, in turn
leading to a downward shift of the labor demand
curve. Hence, the above outcome of lower employment in the economy is reinforced. The fall in
employment reduces households’ projected income
streams, thus decreasing their ability to consume
and save. In the end, households’ pessimism
becomes a self-fulfilling prophecy that causes output, investment, employment, consumption, and
labor productivity all to go down.
Notice that, as in the RBC theory, Farmer and Guo’s
theory predicts that the cause of cyclical declines in
output is not lower consumption. Lower consumption
results from a decrease in investment and output
caused by something else—in this case, a sudden burst
of pessimism about investment prospects.
8

The natural question at this point is, How well do
the simulated data generated by this model match
the actual data featured in Chart 2? Chart 4 shows
that, like the RBC model, the Farmer-Guo model is
able to reproduce the relative variances of U.S. output, consumption, investment, and labor hours over
the business cycle. Similarly, Table 3 shows that, like
the RBC model, the Farmer-Guo model provides a
plausible quantitative description of the cyclical
behavior of key post–Korean War U.S. macroeconomic aggregates in terms of relative volatility and
contemporaneous correlation with output.14
To sum up, the last two sections have described
the driving forces behind business cycles according
to two well-known theories: the real business cycle
theory and the Neo-Keynesian Farmer-Guo theory.
Neither theory supports the notion that fluctuations
in consumption cause the business cycle. Instead,
the theories predict that either random shocks to
total factor productivity or investors’ mood swings
can lead to fluctuations in GDP, consumption, investment, and labor hours.

Policy Implications

A

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

s this article has just reported, proponents of
these two explanations for the business cycle
have conducted empirical tests of their theo-

TA B L E 3
The Neo-Keynesian Model of the
U.S. Economy, 1954:Q1–2001:Q1

Variable

Relative
Volatility

Correlation
with Output

Output
Consumption
Investment
Labor Hours

1.00
0.24
5.14
0.83

1.00
0.78
0.99
0.98

retical arguments. Both theories seem to achieve a
reasonable fit to U.S. data. Since neither theory can
be written off on empirical grounds, it is interesting
to ask the following questions. What kind of economic policies according to these theories might
moderate the business cycle, or insulate households
and firms from aggregate fluctuations, and how
desirable are such policies?
Policy Prescriptions from the RBC Camp.
Imagine, as proponents of the RBC theory claim,
that there really are random and perhaps persistent
changes in total factor productivity. As discussed
earlier, these changes will induce fluctuations in
output, consumption, investment, and so on. Total
factor productivity movements will also induce
changes in the relative scarcity of resources. Under
the assumptions of the RBC theory, inflation-adjusted
wages and interest rates will adjust quickly to
reflect these scarcity changes. Households and firms
will modify their behavior so that they continue to
maximize their well-being and profits, respectively,
through time.
According to the RBC theory, business cycle fluctuations are the optimal responses of households
and firms to random shocks to TFP and hence are
“efficient” outcomes. In this scenario, Adam Smith’s
“invisible hand” will work in the sense that decisions
and actions of the private sector will achieve the
best possible economic outcomes. Accordingly,
under RBC assumptions there is no reason for the
government to implement any kind of “leaning
against the wind” policy—that is, there is no reason
for it to design policies that try to stimulate economic activity during a downturn or slow it down
during a boom. The marketplace of households and
firms will engineer adjustments in the opportunity
cost of investment, leisure, and so forth that induce
the optimal responses by its participants.
Consider the following simple example of cyclical
economic behavior in the U.S. economy: the construction industry. Construction booms in the sum-

mer and slows down in the winter. Is there a case for
taking policy actions such as raising interest rates
during the summer and lowering them during the
winter to stimulate borrowing and construction during winter (and vice versa) so as to even out the
level of construction throughout the year? Probably
not. No government policies can get rid of summers
or winters. There is a reason for the building booms
of the summer: building in the sun is a lot easier
than building under layers of snow. Why then distort
the market allocation of resources if it produces an
efficient outcome?
Critics of the RBC approach argue that its predictions cannot be fully tested because relatively few
observations of business cycles are available. They
also argue that the TFP-shock story is contrived in
that it does not admit any market failures and that its
explanation for unemployment as a natural market
response is hard to swallow. These critics also point
out that the RBC theory makes a number of assumptions that are at odds with reality. Some prices, for
example, do not adjust immediately to economic
conditions. And what if the U.S. economy experiences aggregate production externalities of the type
described in the Neo-Keynesian discussion? These
concerns raise some questions: How seriously should
one take the RBC theory’s claim about the uselessness of countercyclical policies? Is there room for
stabilization policies according to the competing animal spirits explanation of business cycles? And how
desirable are these policies?
Policy Prescriptions from the Animal Spirits
Camp. According to everyday Keynesian economics,
policies that boost private consumption can help
speed up a country’s recovery from an economic
slowdown. As noted earlier, however, this prescription is based on analytical methods that do not enjoy
widespread support among academic economists.
The Farmer-Guo model from the preceding section meets modern academic standards, but, unlike
the RBC theory, it assumes positive externalities in
the aggregate production process. From this perspective, if firms recognized that their individual
actions affected all the firms in the economy and if
they could coordinate their actions, then all could
reap the productivity benefits of increasing returns
to scale. However, by design, economic decisions in
a market economy take place in a decentralized way
and thus make this kind of coordination difficult. As
a result, it is possible for the decentralized decisions
of households and firms to be “inefficient” in the
sense that a central coordinating arrangement

14. As in Table 2, the statistics reported in Table 3 are sample means computed for fifty simulations, each of which consists of
189 periods.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

9

would produce a better economic outcome than
Adam Smith’s invisible hand.
The potential inefficiency of the free-market outcome creates an opportunity for stabilization policies designed to suppress fluctuations driven by
animal spirits to increase public welfare. For example, Guo and Lansing (1998) show that in a NeoKeynesian model with aggregate increasing returns,
a progressive income tax can prevent households
from reacting to bursts of optimism or pessimism.
When households experience a burst of optimism
and decide to work harder and invest more, they are
subject to a higher tax rate, preventing their optimism from becoming self-fulfilling. Conversely,
when households experience a burst of pessimism
and decide to work and invest less, they are subject
to a lower tax rate, preventing their pessimism from
becoming self-fulfilling.
But is it necessarily a good idea to eliminate economic fluctuations that are caused by animal spirits?
Suppose Farmer and Guo are right and the aggregate
production process in the United States does display
positive externalities. In this case, if all the firms in
the economy cooperated, they could obtain morethan-proportional increases in output by increasing
their inputs simultaneously. However, since there is
no central coordinating mechanism in a decentralized
market economy, firms cannot take advantage of this
situation under normal circumstances. One can think
of waves of overoptimism as an unintentional coordinating mechanism. For example, if most firms believe
that the “bad” times are over and decide to produce
more, more-than-proportional increases in output
may be observed. This possibility suggests that animal spirits–induced fluctuations may be a good thing
for the economy. Thus, it is possible that even if the
government can eliminate bursts of overoptimism, it
may not want to.
What is the potential advantage of moderating
economic fluctuations? First, if overoptimism alternates with overpessimism, then the average level of
output might fall more than proportionally to the
decreases in the amount of inputs, leading to undesirably low levels of consumption and investment.
Second, even if fluctuations caused by animal spirits
do not reduce the average level of output, they definitely increase the variability of consumption. Since
the economic theory outlined earlier predicts that
households prefer their consumption to be smooth
rather than variable, swings in consumption tend to
reduce public welfare.
As Christiano and Harrison (1999) point out, the
case for stabilizing the economy against fluctuations
driven by nonfundamental forces depends on the relative magnitude of two opposing factors. On the one
10

hand, households prefer smooth consumption, so
fluctuations in consumption reduce their well-being.
On the other hand, increasing returns in production
may allow nonfundamental fluctuations to increase
the average level of consumption. As a result, it cannot be determined a priori whether stabilization policies will improve the well-being of the economy.
According to the RBC theory, there is nothing the
government can do to eliminate business cycle fluctuations. According to Farmer and Guo, in contrast,
governments may be able to design policies to moderate economic fluctuations. However, Farmer and
Guo cannot recommend countercyclical intervention
unambiguously because it is possible for cyclical
fluctuations to be a net benefit for the economy.

Conclusion
his article has outlined two alternative explanations for business cycles: the real business
cycle theory and Keynesian theory. Although
neither theory is without detractors, each is worthy
of review because it exerts significant influence on
opinions about the business cycle inside the academic economic community.
This article has pointed out that the everyday
version of Keynesian theory predicted that fluctuations in output might be caused by fluctuations in
consumer spending. As a result, one of the reasons
economic commentators follow consumer confidence and spending so closely is that these behaviors are viewed as leading indicators of economic
fluctuations. Commentators think that identifying
leading indicators is important in alerting government about the stage of the business cycle the economy is in so that the appropriate countercyclical
policy can be implemented.
This article also reviewed the RBC theory’s
assumption that changes in total factor productivity
are the cause of economic fluctuations. One modern version of Keynesian theory, on the other hand,
suggests that animal spirits are the cause of economic fluctuations. However, this article makes two
points. First, the existence of a causal relationship
that runs from consumption spending to output is
far from well established. In two prominent business cycle theories, the real business cycle theory
and the Neo-Keynesian Farmer-Guo theory, causality
runs from output to consumption. Second, although
these theories differ diametrically in some key
assumptions regarding the functioning of the economy, both theories meet modern academic standards
and do a reasonably good job of matching key features of U.S. post–Korean War data. However, neither
theory makes an unambiguous case for countercyclical policies.

T

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

Should readers conclude from this discussion
that countercyclical policies are clearly a bad idea?
Not necessarily. This review has covered only a
small subset of the Keynesian literature, and it is
possible that other modern Keynesian analyses of
the business cycle may justify countercyclical policies more forcefully. Moreover, recent work using
the RBC approach, such as Cho and Cooley
(2000), suggests that it may permit more room for
countercyclical policy than RBC theorists have
previously believed.

This article makes clear, however, that two wellknown and widely cited business cycle theories
indicate that there may be no need for countercyclical government policies. This conclusion, no doubt,
will come as a surprise to a number of government
and business economists who have an ingrained
belief in the benefits of such policies. It is important
to remember, however, that attempts to understand
business cycles and the effects and desirability of
government policies that may (or may not) moderate
them are still at a very early stage.

REFERENCES
BALL, LAURENCE, AND DAVID ROMER. 1990. Real rigidities
and the nonneutrality of money. Review of Economic
Studies 57 (April): 183–203.

GUO, JANG-TING, AND KEVIN J. LANSING. 1998. Indeterminacy and stabilization policy. Journal of Economic
Theory 82 (October): 481–90.

BARRO, ROBERT J. 1992. Comment on “A tale of two cities”
by Alwyn Young. NBER Macroeconomics Annual 7.
Cambridge, Mass.: NBER.

KEYNES, JOHN M. 1973. The general theory of employment, interest, and money. Vol. 7 of The collected
writings of John Maynard Keynes. 1953. Reprint,
Cambridge: Macmillan Press Ltd.

BLANCHARD, OLIVIER JEAN, AND NOBUHIRO KIYOTAKI. 1987.
Monopolistic competition and the effects of aggregate
demand. American Economic Review 77 (September):
647–66.
CABALLERO, RICARDO J., AND RICHARD K. LYONS. 1992. External effects in U.S. procyclical productivity. Journal of
Monetary Economics 29 (April): 209–26.
CHATTERJEE, SATYAJIT. 2000. From cycles to shocks:
Progress in business cycle theory. Federal Reserve Bank
of Philadelphia Business Review (March/April): 1–11.
CHO, JANG-OK, AND THOMAS F. COOLEY. 2000. Business
cycle uncertainty and economic welfare. University of
Rochester. Unpublished manuscript.
CHRISTIANO, LAWRENCE J., AND SHARON G. HARRISON. 1999.
Chaos, sunspots, and automatic stabilizers in a business
cycle model. Journal of Monetary Economics 44
(August): 3–31.
FARMER, ROGER E.A., AND JANG-TING GUO. 1994. Real business cycles and the animal spirits hypothesis. Journal
of Economic Theory 63 (June): 42–72.
GALI, JORDI. 1994. Monopolistic competition, business
cycles, and the composition of aggregate demand.
Journal of Economic Theory 63 (June): 73-96.

KING, ROBERT G., AND SERGIO T. REBELO. 1999. Resuscitating real business cycles. In Handbook of macroeconomics 1B, edited by John B. Taylor and Michael
Woodford, 927–1007. Amsterdam: North-Holland.
KYDLAND, FINN E., AND EDWARD C. PRESCOTT. 1982. Time
to build and aggregate fluctuations. Econometrica
50:1345–70.
LONG, JOHN B., JR., AND CHARLES I. PLOSSER. 1983. Real
business cycles. Journal of Political Economy 91
(February): 39–69.
MANKIW, N. GREGORY. 1985. Small menu costs and large
business cycles: A macroeconomic model of monopoly.
Quarterly Journal of Economics 100 (May): 529–39.
———. 1992a. Macroeconomics. 3d ed. Cambridge,
Mass.: Harvard University Press.
———. 1992b. The reincarnation of Keynesian economics. European Economic Review 36 (April): 559–65.
SOLOW, ROBERT. 1957. Technical change and aggregate
production function. Review of Economics and Statistics 39 (August): 312–20.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

11

What Remains
of Monetarism?
R . W.

H A F E R

Hafer chairs the department of economics and finance
and is the director of the Office of Economic Education
and Business Research at Southern Illinois University
Edwardsville; he is also a visiting scholar at the Federal
Reserve Bank of Atlanta. He thanks Gerald Dwyer, Robert
Hetzel, Garett Jones, Mack Ott, and Anna Schwartz for
comments and criticisms on an earlier version of this
paper presented at the Western Economics Association
meetings in San Francisco in July 2001.

In economics as in other developing sciences, change erodes the value of
popular terminology. Monetarism is a name that has been given to a particular
set of propositions at a particular point of time. Like Keynesianism, fiscalism,
or the “Treasury view,” the particular set of propositions called monetarism
does not fully describe the body of thought accepted by a loosely knit group
of practicing economists any more than terms like Chicago, Cambridge or
Austrian School describe the thought of all to whom the terms are applied.
—Allan Meltzer, The Structure of Monetarism

OST CENTRAL BANKS CONDUCT MONETARY POLICY BY MANIPULATING SHORT-TERM INTEREST

M

RATES TO ACHIEVE CERTAIN POLICY OBJECTIVES, SUCH AS ECONOMIC GROWTH AND LOW

INFLATION.

BY

MOST ACCOUNTS THE

FEDERAL RESERVE

HAS BEEN REMARKABLY SUC-

CESSFUL DURING THE PAST TWO DECADES AT ACHIEVING THESE GOALS.

the economic expansion from 1982 to 1990 and from
1991 to 2001 is unprecedented in U.S. history. In
addition, inflation has fallen sharply since the 1970s,
averaging less than 3 percent during the past
decade. Looking back over this period, Taylor (1998)
calls it the “Great Boom” in U.S. economic history.
The Fed’s approach to policy was not always as
successful as recent experience suggests, however.
It was the Fed’s policy of controlling short-term
interest rates—more specifically, the federal funds
rate—that gave rise to the sustained inflation that
began in the early 1960s and ran through the early
1980s.1 Indeed, this dismal track record increased
interest in an alternative policy, one that focused
more on the growth rate of the money supply. The

THE LENGTH OF

basic idea behind this alternative policy, usually put
under the umbrella name of “monetarism,” was that,
by controlling the growth of the money supply and
not interest rates, the Fed could better control inflation and foster stable economic growth.
The power of monetarist arguments and the
building empirical evidence supporting them were
key factors leading up to the Fed’s October 1979
announcement that it would place more weight on
the monetary aggregates in policy deliberations.
The Fed’s apparent romance with an aggregatesbased policy was short-lived, however. Citing the
unusual behavior of money growth, in October 1982
the Fed abandoned monetary targets as operating
guides and returned to targeting the federal funds

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

13

rate. Indeed, today monetary growth targets play no
official role in the setting of U.S. monetary policy. The
fact that money plays no role is not new in the history
of U.S. policymaking.2 The question is whether such
disregard is justified by the data any more today than
it was in the past.
This article addresses that question by discussing
the development and apparent failure of monetarism
as a guide to policy. This overview is useful because it
puts today’s disregard of monetary aggregates as policy tools into a historical perspective. The article also
presents some empirical analysis using a sample of
fifteen countries to explore whether the basic monetarist propositions still hold true. Before delving into
these discussions and analysis, the article first provides a working definition of monetarism.

What Is Monetarism?
n its most generic form, monetarism is the term
often used to describe a view or a body of work
in which changes in the growth rates of the monetary aggregates play a central role in explaining
economic activity, including changes in income
(nominal and real) and prices. This view is directly
linked to the quantity theory of money. To see this
link, let

I
(1)

M = kY,

where M represents the nominal money stock, k is the
public’s desired ratio of money holdings to nominal
income, and Y is nominal income. The so-called k-ratio
is key to understanding the behavioral relationship
between the money stock, income, and prices. If this
ratio is constant, then M and Y move proportionally.
If M is viewed as the nominal stock of money balances demanded by the public, equation (1) is a simple money demand function, where money demand
depends largely on income.
The usefulness of equation (1) is demonstrated by
a scenario in which the economy is in equilibrium,
defined as a condition in which the quantity of
money balances demanded is equal to the quantity
supplied. If this condition holds, then any increase in
the nominal stock of money (M) leads to an increase
in either k or Y. If individuals do not initially alter
their desired money-to-income ratio (k), an increase
in the money stock leads directly to an increase in
nominal income. Writing equation (1) in growth rate
terms leads one to the following proposition: increasing the growth rate of the money stock leads to an
increase in the growth rate of nominal income.
This proposition is important to understanding the
nature of monetarism. First, the proposition suggests
that movements in the money stock lead to similar
14

movements in nominal income. If the money stock is
by and large influenced by the actions of the monetary authority—the Federal Reserve System in the
United States or the European Central Bank in
Europe—then policy actions have predictable effects
on the economy. Of course, how closely money and
nominal income move together is the subject of much
ongoing debate and empirical testing.
Second, equation (1) also suggests, as a matter of
arithmetic, that changes in money can affect both
real income and prices differently; nominal income
(Y) is the product of real income (y) and prices (P).
So equation (1) can be rewritten in the form
(2)

M = k(yP),

where yP = Y. If changes in the nominal money stock
are not associated with permanent changes in real
income and the k-ratio is stable, increases in inflation
are linked directly to increases in money growth.
This age-old proposition recognizes the fact that
increased money growth by itself cannot lead to an
increase in the production of real goods. This fact
can be illustrated by an example in which the money
stock doubles, making Jane’s checking account
today twice as big as it was yesterday. What does
Jane do? Of course, she might spend all the money,
save it all, or spend and save it in varying proportions. The impact of these events on the overall
economy is that demands for different goods are
likely to change. For goods for which demand has
increased, more of those goods are needed, so production increases. Real output (income) rises as
more goods are produced. However, there is an
upper limit to this production surge, a limit placed
by existing plants, equipment, production technologies, and the current labor force. As demands for
goods rise and the ability to produce more is constrained, profit-maximizing firms raise prices to
ration the scarce goods. Over time, increasing the
growth rate of the money supply is likely to be evidenced in rising inflation rates and not in increased
rates at which goods and services are produced.
This is the story that monetarists reinvigorated in
the 1960s, which reappeared as the New Keynesian
story of the 1980s and 1990s.3
This story provides a substantial foundation for
understanding what monetarism is. Of course, exactly
what constitutes monetarism varies as much as the
number of individuals attempting to define it.4 For
the purposes of this article, the definition of monetarism comprises three facets. First, it refers to a set
of testable propositions from which policy prescriptions are determined. For example, Milton Friedman’s
famous X percent rule for monetary policy is an

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

example of a policy prescription derived from empirical findings.5 Second, movements in the money supply are considered to be a major factor explaining
observed changes in income, prices, and, in the short
run, real output. This view suggests that money and
nominal income should be positively related, just as
money growth and inflation should be. While monetary impulses may have an impact on real economic
activity in the short run, money and real output are
not likely to be related over time. Finally, the monetary authority is believed to be accountable, over time,
for movements in the money stock. Even though
most central banks use short-term interest rates
as the policy tool, manipulating interest rates still
requires changes in the reserve structure of the banking system, and these changes produce changes in the
money stock.
This article uses these propositions to address
the question raised in the article’s title. The discussion focuses on the first two points, leaving the issue
of money stock control for another study. Before
turning to the empirical evidence on these points,
however, it is useful to examine a brief history of
monetarism’s rise and fall as a policy guide.

A Brief History of Monetarism
oney’s role in the macroeconomic theories
developed during the 1930–60 period was
negligible.6 Following the Great Depression
and World War II, the dominant view was that governments could successfully manage economies to
achieve full employment. The tool by which such
“demand management” could be conducted was fiscal
policy. Monetary policy was considered important only
in the sense that it would keep interest rates at levels
necessary to maintain economic growth. Inflation was
of little concern in the early postwar period.7

M

Against this mainstream view, some economists
emphasized the empirical relationship between
movements in the money stock, nominal income,
and inflation. The early studies of Warburton (1966)
stand out in this regard. Warburton tested the link
between money and inflation and money and
income, providing empirical support for the notion
that increases in the growth rate of the money stock
lead to similar increase in the inflation rate. He also
found that short-run fluctuations in real output are
related to similar changes in money growth. Both of
these empirical results became a hallmark of modern
monetarism. Unfortunately, Warburton’s evidence
and scholarly work received scant attention and did
little to alter mainstream perceptions regarding the
importance of money.
Although the history of monetarism in the postwar period contains many important and interesting
contributions, this article focuses on three: the early
work done by Milton Friedman and his associates,
the Andersen-Jordan model of income determination
and the subsequent St. Louis model, and the velocity
shift of the early 1980s.
Friedman and Associates. The 1950s witnessed
an increase in scholarly work on monetary theory
and policy. Notable in this regard is the work of
Milton Friedman and his students at the University
of Chicago. Friedman’s research agenda at the
National Bureau of Economic Research (NBER) in
the early 1950s began to focus on monetary economics.8 For example, an early analysis examined
the effects of money on the economy during
wartime (Friedman 1952). The mid-1950s saw the
publication of Studies in the Quantity Theory of
Money (1956), a collection of articles by Friedman
and his students in the monetary workshop at the
University of Chicago. His introductory essay, “The

1. See Mayer (1999), DeLong (1997), or Sargent (1999) for a discussion of what is referred to as the “Great Inflation.”
2. See Hafer (1999) and Meigs (1976) for a discussion of the early debates over the use of monetary targets. A review of policy
actions taken by the Federal Open Market Committee (FOMC) reveals that, during the period from 1950 through 1979 and
since 1982, monetary aggregates have been ignored more often than they have contributed to policy decisions.
3. See Mayer and Minford (1995), DeLong (2000), or Woodford (forthcoming).
4. Mayer (1978), for example, suggests more than a dozen attributes of what makes up monetarism, including notions about
governmental intervention.
5. The so-called Taylor rule, which relates changes in the federal funds rate to deviations in inflation and output from their
desired rates, is a recent policy rule derived from empirical findings. Its long-term viability, like Friedman’s rule, will be subject to the vagaries of the underlying data.
6. Portions of this discussion draw on Hafer and Wheelock (2001). Note that the discussion deals only with monetarism as it
developed in the United States, not elsewhere.
7. The notion that monetary policy actions, defined as changes in the growth rate of the money stock, are unrelated to economic
activity and should not be given much due is not an idea that remained the exclusive property of economists in the 1940s or
1950s. More recent evidence of such a view is found in B. Friedman (1984, 1997).
8. As Friedman recalls it, “In 1950, Arthur Burns, who had taken over from [Wesley Claire] Mitchell as director of research, asked
me whether I would take responsibility for the part of the study dealing with the role of money in business cycles. Both his
invitation and my acceptance of it demonstrates the interest that I had already developed in the role of money” (Friedman
and Friedman 1998, 227–28).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

15

Quantity Theory of Money—A Restatement,” is considered by some as the defining article that established modern monetarism.
Friedman posits in this essay that nominal income
is closely related to monetary developments: simply
put, the theory of money demand is really just a
theory of nominal income determination. Mayer and
Minford (1995) suggest that Friedman’s essay shifted
the debate from money’s long-run effects on prices
to its shorter-term influence on the business cycle.
As they state, “This meant that the quantity theory
could now explain changes in output as well as in
prices, and could no longer be dismissed as arbitrarily assuming full employment” (4). This view contrasted sharply with
the Keynesian orthodoxy, one in which
In October 1982 the Fed
money had little or no
role.9 Friedman’s own
abandoned monetary tarview is that the publigets as operating guides
cation of this book in
and returned to targeting
1956 was “the first
major step in a counthe federal funds rate.
terrevolution in monIndeed, today monetary
etary theory that sucgrowth targets play no
ceeded in restoring
the classical quantity
official role in the setting
theory to academic
of U.S. monetary policy.
respectability under
the unlovely label of
‘monetarism’ ” (Friedman and Friedman 1998, 228).10
Friedman’s work during the 1950s laid the foundation for later studies linking the behavior of the
economy to monetary policy actions. His early work
at the NBER with Anna J. Schwartz began to focus
more on the business cycle effects of money and
monetary policy.11 His testimony to the Joint Economic Committee in 1958 provides a glimpse into
this early counter-attack on Keynesian orthodoxy.
At that time policymakers within the Federal
Reserve System typically expressed little concern
over money’s cyclical effects. Minutes of the FOMC’s
policy meetings indicate that committee members
largely rejected the notion that movements in the
money supply could be controlled, much less that
changes in money growth affected economic activity
in any predictable manner. A few members of the
FOMC warned that significant shifts in money growth
could cause undesirable shifts in the real economy
and that the secular increase in money growth would
likely raise inflation rates. Unfortunately, these concerns went largely unheeded.12
A critical event in the early monetarist assault on
Keynesian policies occurred with the 1963 publica16

tion of Friedman and Meiselman’s “The Relative
Stability of Monetary Velocity and the Investment
Multiplier in the United States, 1897–1958.” The key
empirical finding reported in the article rejected a
core component in the Keynesian macro model—
namely, the relative stability of the expenditure
multiplier. Instead, Friedman and Meiselman demonstrated that the velocity of money, considered by
Keynesians to be highly erratic and thus obviating
any reliable money-income link, is relatively stable
over time.13 They argued that changes in the money
stock are more likely caused by changes in the money
supply—stemming directly from monetary policy
actions—than from changes in the public’s demand
for money. This finding supported an underlying
tenet of the quantity theory and the emerging monetarist argument: changes in nominal income are
largely determined by changes in the money supply.
Since movements in the money supply are related
directly to policy actions, fluctuations in economic
activity logically are tied to the Fed’s policy actions.
Friedman and Meiselman’s evidence and methodology were attacked and dismissed by mainstream
economists. The criticisms of Ando and Modigliani
(1965) and DePrano and Mayer (1965) were published in the American Economic Review along
with the Friedman-Meiselman article and the latter’s
rebuttal (1965). The debate reflected a fundamental
difference in views on the importance of money and
the role of monetary policy. Friedman and
Meiselman’s evidence came from simple, reducedform relations reminiscent of the quantity theory.
Their conclusions were based on observed long-run
relations in the data. Keynesian policies and viewpoints, represented by the Ando-Modigliani and
DePrano-Mayer papers, relied on the output of newly
developed, large-scale macroeconometric models.
These models focused more on short-run dynamics,
not long-run implications. Ando-Modigliani argued,
for example, that the Friedman-Meiselman analysis
used methods that were “inadequate” given the
advances in econometrics and evidenced in the construction of large-scale models. In later analysis,
Blinder and Solow (1974) suggested that the
reduced-form approach taken by FriedmanMeiselman was “far too primitive to represent any
theory” (cited in McCallum 1986, 11). The conventional view was that while different approaches generate different results, only the more sophisticated
approach produces a reliable outcome.14
Finding that velocity appeared more stable than
commonly thought heightened the debate over the
relative effectiveness of monetary and fiscal actions
as countercyclical policies. Most economists continued to support the use of fiscal actions as the only

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

effective policy tools available to manage real economic activity.15
The results of Friedman and Meiselman helped
spur the development of a nascent monetarist
research agenda. The publication in 1963 of
Friedman and Schwartz’s massive A Monetary
History of the United States: 1867–1960 provided
even greater ammunition to the monetarist movement. In their study, Friedman and Schwartz documented the long-term, empirical relation between
movements in the money supply, income, and
prices. A major point established in their meticulous
analysis of empirical relations and institutional
detail was that movements in the money supply
largely dictate observed changes in the economy.
Indeed, a major contribution of the study was its
description of policy blunders that led to the Great
Depression. In the end, much of the blame was laid
at the Fed’s doorstep. While Friedman and
Schwartz’s Monetary History helped to establish a
foundation for monetary policy emphasizing control
of the monetary aggregates, the nature of the analysis was decidedly long-run.
Andersen-Jordan and the St. Louis Model.
The heretofore long-run nature of the monetarist
position changed dramatically with the 1968 publication of Andersen and Jordan’s “Monetary and
Fiscal Actions: A Test of Their Relative Importance.”
Their controversial results were based on testing
the empirical relation between changes in nominal
income and various measures of money and fiscal
policy actions. The key equation can be written as
(3)

9.
10.
11.
12.

13.
14.

15.
16.
17.
18.

3

3

i =0

j =0

Yt = α + Σ βiMt–i + Σ λj Et–j + et,

where Y represents nominal GNP, M is the money
stock (M1 or the monetary base), and E is one of
several measures of fiscal policy actions.16 The form
of the equation explicitly recognizes the lagged
effects of policy actions and allows a more precise
estimation of the effects of changes in the policy
variables. Andersen and Jordan, like Friedman and
Meiselman, were interested in the role that money
plays in explaining movements in nominal income.17
But Andersen and Jordan extended the attack on
the conventional wisdom by directly comparing the
quantitative importance of the effect that monetary
and fiscal impulses have on nominal income.
Money’s role in explaining movements in nominal income was an important policy issue in 1968.
Jordan recalls that “the 1966 credit crunch and
subsequent ‘mini recession’ had demonstrated the
potential for a restrictive monetary policy, measured in terms of a deceleration of monetary growth,
to dominate an expansive fiscal impulse” (1986,
5).18 The Andersen-Jordan results provided support for a key element in the monetarist position:
namely, money is not only important in affecting
nominal income but has a more direct and manageable impact on the economy than fiscal policy
actions. In a significant way, the Andersen-Jordan
results pushed the long-run monetarist propositions further into the short end of the policy horizon. Andersen and Jordan demonstrated that, by
manipulating monetary aggregates, policymakers
could achieve the kind of demand-management
outcomes once thought possible only through fiscal
policy actions.
Andersen and Jordan’s results came under
immediate criticism. A number of the criticisms

This view is debatable, as the exchange in Hafer (1986) between McCallum, Brunner, Blinder, and Gordon indicates.
Although Friedman is often considered the “father” of monetarism, it was Karl Brunner (1968) who coined the term.
This research would later be published in three volumes. See Friedman and Schwartz (1963, 1970, 1982).
Some members of the FOMC favored policies that placed more weight on the behavior of the money supply over financial
market conditions. Of this small group, Delos Johns, president of the Federal Reserve Bank of St. Louis, and Malcolm Bryan,
president of the Federal Reserve Bank of Atlanta, stand out. They based their policy recommendations on recent monetarist
analyses. For a discussion of their contributions to the policy debate, see Meigs (1976) and Hafer (1999).
The velocity of money is simply the inverse of the k-ratio.
McCallum notes that “Most researchers in macroeconomics believed . . . that investigation of the issues under discussion could
be adequately carried out in the context of a full specified, simultaneous-equation, econometric model”(1986, 11). Brunner
rejected this notion, stating that “the use of a single equation with a single independent variable should now be clear. It was
the appropriate choice for an assessment of the core class [of hypotheses]. It did not represent a single-equation model or a
disposition to favor simple, as against sophistical, models” (1986, 41).
For a discussion of the issues surrounding the debate, see the articles in Hafer (1986).
Their analysis used three fiscal policy measures: the high-employment budget surplus, high-employment expenditures, and
high-employment receipts.
Andersen-Jordan’s intellectual link to earlier work by Karl Brunner is obvious. For example, Brunner and Balbach (1959)
tested the relative role of money and fiscal policy actions and found that money played an important role.
The importance of the events surrounding the 1968 decline is revealed in Maisel’s appraisal: “Monetarists’ forecasts have
had a fair record. The fact that they did well in 1968 when most others did poorly was a major cause of their initial popularity. . . . But I, at least, do not believe their record has been good enough to prove their simplified theory” (1973, 274).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

17

were technical in nature.19 It is interesting to note
that some of the earliest and harshest criticisms
came from within the Federal Reserve System
itself. For example, DeLeeuw and Kalchbrenner
(1969), both associated with the Board of
Governors, argued that the monetary aggregate
favored by Andersen and Jordan (the monetary
base) was not exogenous with respect to movements in nominal income.20 They also argued that
Andersen and Jordan’s results were inconsistent
(and therefore suspect) with those generated by
the Board of Governor’s large-scale econometric
model. Davis, an economist at the New York Fed,
took up this argument, noting that the St. Louis
equation “portrays a
world [that is] in several respects sharply
The Andersen-Jordan results
at variance with the
expectations of most
provided support for a key
of us” (1968, 121). He
element in the monetarist
suggested that moneposition: namely, money is
tarists build a structural model (like the
not only important in affectFRB/MIT model) and
ing nominal income but has
reject the reduceda more direct and manageform approach that
began with Friedmanable impact on the economy
Meiselman and was
than fiscal policy actions.
refined by Andersen
and Jordan.21
DeLong (2000)
argues that the next significant development in the
monetarist counter-revolution came with the 1970
publication of Andersen and Carlson’s “A Monetarist
Model for Economic Stabilization.” Usually referred
to as the St. Louis model, this study and subsequent
refinement of the model put monetarism on similar
footing with Keynesian models. The St. Louis model
was “monetarist” in the sense that, even though
money appears only in the total spending equation,
its effects percolate throughout. For example, the
effects from an increase in the growth rate of the
money supply could be traced through its impact on
nominal spending, changes in the price level, real
output, long-term interest rates, and unemployment.22 Since price level changes came about
through a simple Phillips-curve relation embedded
in the model, no claim was made that that relationship was being ignored. In fact, Andersen and
Carlson explicitly state that their analysis was used
“to estimate the response of output and prices to
monetary and fiscal actions, not to test a hypothesized structure” (1970, 10–11).
The St. Louis model strengthened monetarism’s
place in policy discussions in several ways. First,
18

monetarist analysis moved into the realm of shortrun policy dynamics. Second, the estimated relationships provided support for the theoretical
findings of Friedman (1968) and Phelps (1967) that
there does not exist a long-run, exploitable tradeoff between inflation and unemployment, as many
Keynesian economists believed. Estimates of the
St. Louis model also demonstrated that expansionary
monetary policy can produce a short-run increase in
real economic growth (a reduction in the unemployment rate) but that it will vanish over time as inflation
picks up and the economy returns to its potential rate
of growth. Such actions, taken repeatedly, impart an
inflationary bias to the economy.23
Third, the Andersen-Carlson results showed that
monetary policy, not fiscal policy, is a more potent
tool for economic stabilization. Now monetary policy was defined in concrete terms. Instead of terms
like “money market pressure” or “tone and feel,” a
vocabulary that popularized monetary policy analysis in the 1950s and 1960s (and has resurfaced in
recent times), changes in the growth of the monetary aggregates could be calculated and their effects
analyzed. The St. Louis model helped push the monetarist agenda to the forefront of the short-run stabilization debate more forcefully than previous work
had. Dewald argues that “monetarism was [now]
widely interpreted as providing an alternative to
short run Keynesian model forecasts” (1988, 6).
The Rise and Fall of Monetarism as a Policy
Guide. As the success of monetarist predictions
mounted, monetarists began to shift from testing
rival policies to arguing for the use of monetary
aggregates as a short-run stabilization tool. Ongoing
development of the St. Louis model and its variants,
along with its use for policy analysis, pushed monetarism away from its roots in the long-term relations
embodied in the quantity theory. By the mid-1970s,
monetarism had elbowed its way squarely into the
arena of short-run stabilization issues.24 Unlike the
large-scale macroeconometric models that contained hundreds of variables and equations, the
archetypal monetarist model allowed one to analyze
stabilization issues using a handful of equations.25
The increase in inflation rates throughout the
1970s led many to reconsider monetarist calls for a
policy of steady money growth. Even though the
inflation spikes of the 1970s were related directly to
oil price shocks, the rising trend rate of inflation
since the mid-1960s shadowed a similar increase in
the average rate of money growth.26 The Fed began,
reluctantly, to adopt parts of the monetarist platform. In the mid-1970s, monetary targets were
being used in official policy analysis; there is substantial evidence, however, that these targets were

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

more window-dressing than strict policy guidelines
(Friedman 1982).
The most dramatic shift toward a monetarist-like
policy occurred in October 1979. At that time the
Fed announced that it would henceforth emphasize
policy procedures aimed more at controlling nonborrowed reserves than at the federal funds rate.27 This
shift was made to reduce inflation rates, which were
then running in double digits. The restrictive policies
enacted served to help lower inflation (and inflationary expectations), but they also sent the economy
into the deepest postwar recession on record.
Monetarist theory predicted the outcome: A
swift, sharp reduction in money growth (and the
attendant spike in interest rates) initially affected
real economic activity and then, over time, lowered
inflation. Although monetarists predicted the outcome, they neither favored the policy nor claimed
credit for it.28 While monetarists attempted to disassociate themselves from the Fed’s policies and to
provide alternative procedures to achieve the
desired money growth,29 public perception and professional opinion quickly rejected the so-called

monetarist policies being followed by the Fed.
Attacks on monetarism surged not only in academic
journals but in the popular press as well.30
Whether the Fed truly embraced a monetarist policy agenda in 1979 remains debatable, but the Fed’s
policies dealt a severe blow to monetarism. In addition, sweeping deregulation associated with the
Depository Institutions Deregulation and Monetary
Control Act of 1980 gave rise to increased volatility in
the empirical links between the existing monetary
aggregates and the economy. The spread of interestbearing checking accounts severely altered the relationship between narrow measures of money and
income from their historical norms. The most visible
effect was the unexpected and large shift in velocity in
the early 1980s, which severely reduced the accuracy
of monetarist model predictions of nominal income
growth and inflation.31 As the 1980s progressed,
deregulation of the banking system, largely a response
to the disintermediation that occurred in response to
the inflation of the 1970s, and the quickened pace of
financial innovations altered the historical empirical
relationships between money, income, and prices.

19. For reference to previous studies, see Hafer and Wheelock (2001).
20. Deleeuw and Kalchbrenner (1969) decomposed the monetary base into what they argued were the most exogenous components: reserves less bank borrowings—the adjusted base—and the adjusted base less currency. With this change they
found that when the adjusted base was paired with real high-employment receipts, the sum of the estimated coefficients on
lagged money—a measure of the monetary multiplier—was less than that found by Andersen and Jordan. Even so, the
results were striking enough to show that monetary policy appeared “to exert a powerful influence.”
21. Brunner (1986) notes in his survey that this criticism confused competing economic theory with testing a core class of
hypotheses that are derived from theory. Even so, the stigma attached to reduced-form results dogged the debate. Regardless
of the amount of empirical support for the finding that money influenced nominal income, monetary policy continues to this
day to focus on the behavior of interest rates as the mechanism by which policy actions are transmitted to the economy.
22. Output is determined as the difference between total spending and the price level. As Andersen and Carlson note, “This
method of determining the change in total spending and its division between output change and price change differs from
most econometric models. A standard practice in econometric model building is to determine output and prices separately,
then combine them to determine total spending” (1970, 10).
23. For a discussion about the role of monetary policy in generating the spiraling inflation that began in the 1960s, see the interviews in Mayer (1999).
24. Hafer and Wheelock (2001) detail the difficulties that this focus placed on the popularity of monetarism in policy discussions.
Tavlas (personal correspondence) suggests that the movement to a shorter-term focus occurred much earlier, evidenced by
the publication of Friedman (1972).
25. The 1970 version of the St. Louis model, for example, contained eight equations and eleven economic variables.
26. Fed Governor Gramley is quoted in Grieder (1987) as saying, “When you look back over the past fifteen years, you find that
inflation kept getting worse. It got worse for a whole variety of reasons, but certainly one of them was that the course of
monetary policy over this long period had permitted a rapid increase in money and credit” (emphasis added) (1987,
94). For discussions of the “Great Inflation,” see DeLong (1997), Mayer (1999), and Sargent (1999).
27. For a timely overview of the so-called monetarist experiment, see Brunner (1983).
28. See the debate between M. Friedman (1984) and B. Friedman (1984).
29. For example, a number of studies demonstrated that the money multiplier was easily forecast, thus allowing the Fed to
achieve monetary growth targets. See, among others, Johannes and Rasche (1979) and Hafer and Hein (1984) for examples
of such analyses. Of course, the vacuous argument made against such evidence was that, once the Fed began to target the
money stock, the ability to forecast the multiplier would be impaired.
30. Batten and Stone (1983) provide a partial listing of the articles taking a negative view of the monetarist experiment. (The
author’s personal favorite is Kaldor 1982.)
31. A key ingredient of the earlier success of short-run, empirical monetarist models had been the relative stability of velocity over
much of the postwar period, even though this point was recognized early in the debate. See, among others, Rasche (1972).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

19

In light of these events, policymakers quickly
rejected monetary aggregates as a policy tool. In lieu
of money, they once again returned to the manipulation of the federal funds rate to achieve policy
objectives. Since the early 1980s, monetary aggregates have played a minimal role in the conduct of
U.S. monetary policy. In the early 1990s, Taylor
(1993) showed that U.S. monetary policy could be
described accurately by relating movements in the
federal funds rate to deviations in inflation from a
posited target rate and deviations in real output
growth from potential growth. The so-called Taylor
rule has dominated much of the research on monetary policy during the past decade, both as a model
of Fed behavior and
as a model to guide
policy decisions. 32
What is notable in this
Unlike the large-scale
monetary policy rule
macroeconometric models
is that money does
not appear.
that contained hundreds of
The failure of monvariables and equations, the
etarism to survive as a
archetypal monetarist model
policy guide has been
noted by Fed Goverallowed one to analyze stanor Meyer (2001),
bilization issues using a
who stated, “Monetarhandful of equations.
ism is about money,
but money plays no
explicit role in today’s
consensus macro
model, and it plays virtually no role in the conduct of
monetary policy, at least in the United States.” 33 The
consensus macro model to which Meyer refers is
described in McCallum (1999) and Rudebusch and
Svensson (2000), among others. In this model,
money’s purpose is only to assist the central bank in
determining the interest rate. The consensus macro
model determines the inflation rate, the level of output, and the interest rate without any direct reference
to the behavior of the money stock. As McCallum
notes, “This is the basic point that has led many
researchers to ignore money and, indeed, that has led
the staff of the Fed’s Board of Governors to construct
a large, sophisticated, and expensive new macroeconometric model that does not recognize money in
any capacity” (1999, 7). Meltzer echoes this in his
observation that “Most working economists, most
central bank staffs, and market practitioners do not
use money growth to predict inflation” (1999, 25).
It would be incorrect, however, to conclude that
monetarism failed. In fact, several of its key tenets
have become characteristics of current economic
thinking. DeLong (2000) and Woodford (forthcoming), for example, argue that the general acceptance
20

of policy rules is a direct descendant of the monetarist agenda. In addition, the very fact that monetary
policy, not fiscal policy, is considered the major
weapon to combat economic fluctuation is a clear victory for the monetarist view. Still, interest rate manipulation once again dominates controlling growth in
the monetary aggregates as a means of achieving
stable economic growth and low inflation. The policy
role of money is back to where it was almost forty
years ago, and policy discussion today is similar to
that found in the FOMC minutes from the 1960s.34
Monetarism is based on an empirical relation
between movements in the money supply and
income and prices. Thus, is there any informational
content in the monetary aggregates that could help
determine the direction and thrust of policy
actions? Answering this question occupies the
remainder of this article.

Empirical Evidence
his section provides some empirical evidence
aimed at answering the question raised in the
article’s title as well as Meyer’s (2001) corollary question: Does money matter? The analysis
approaches this task in three interrelated parts.
First, data from a sample of diverse countries is
examined to determine whether money growth and
nominal income growth are positively and significantly related. Next, the link between money and
inflation is investigated. Finally, the effect of money
on short-term fluctuations in real output is tested.
Overall, the evidence indicates that movements in
the money supply still help explain movements in
nominal income, prices, and real output.
Data. The analysis uses annual post–World War II
data from a diverse sample of countries. The data
include two measures of money (M1 and M2), the
price level (measured using the consumer price
index [CPI]), nominal income (gross domestic product [GDP]), and real income (real GDP). The choice
of countries is based on no specific criteria beyond
data availability, attempting to provide a wide range
in economic experience, and keeping the discussion
tractable. The attempt is not to achieve total coverage but to test the general applicability of several
key monetarist propositions. The sample of countries, the period covered, and summary statistics are
provided in Table 1.35
Fifteen countries, including developed and developing countries, make up the sample. This sample
covers a wide variety of economic experiences. For
example, the average annual inflation rate averages
a little over 9 percent, ranging from Malta’s 3.3 percent to Indonesia’s 23.3 percent. Similarly, average
annual nominal GDP growth spreads across a wide

T

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

TA B L E 1
Summary Statistics
Average Rates of Growth (Percent)
Country
Canada
Chile
Colombia
Denmark
Egypt
Iceland
Indonesia
Japan
Korea
Malta
Pakistan
Philippines
South Africa
Thailand
United States
Averages

Sample

M1

M2

Price Level

Nominal GDP

Real GDP

1950–99
1960–99
1955–99
1950–99
1952–99
1951–98
1965–99
1953–99
1966–99
1957–99
1956–99
1950–99
1965–99
1953–99
1959–99

7.9
23.7
20.0
8.3
10.8
20.5
31.4
10.5
19.0
7.8
11.8
11.9
15.2
10.5
5.6
14.5

8.7
26.4
22.9
8.2
13.5
21.4
36.4
11.0
23.2
9.6
12.8
14.6
13.4
14.5
6.9
16.2

3.5
16.5
16.6
5.3
7.6
16.1
23.3
4.0
9.2
3.3
7.4
8.1
9.8
4.7
4.4
9.3

8.0
21.7
21.2
8.2
12.4
21.4
31.5
9.2
18.7
8.7
12.3
12.4
13.7
10.7
7.3
14.5

3.8
5.2
4.6
3.0
4.9
5.3
8.2
5.3
9.4
5.4
5.0
4.3
3.8
5.9
2.9
5.1

Source: International Monetary Fund, International Financial Statistics CD, December 2000.

range, from 7.3 percent in the United States to 31.5
percent in Indonesia. One aspect worth noting is
that average nominal GDP growth across the sample
is closer to money growth than is inflation or real
GDP growth. As Table 1 shows, the average growth
rate of the money supply—14.5 percent for M1 and
16.2 percent for M2—is closer to nominal GDP
growth (14.5 percent) than to average inflation (9.3
percent). It should also be noted that average real
GDP growth (5.1 percent) is noticeably less than
money growth. Finally, the range of growth rates for
real GDP—2.9 percent to 9.4 percent—is less than
the range recorded for nominal GDP growth and

inflation.36 As a first approximation, these data suggest a closer relation between money and nominal
income than between money and inflation or money
and real output.
Correlations. If money matters for policy, there
should be a correlation between money growth,
nominal income growth, and inflation. In addition, if
money growth has little impact on real output in the
long run, then a smaller correlation between money
growth and real output growth should be found in
the data. It is useful to compare correlations across
three time horizons, using annual observations of
each variable, to assess the link between money and

32. For a critical analysis of the Taylor rule and its applicability, see Hetzel (2000) and the works cited therein. Arguably, the
Taylor rule suffers from the same problems as the monetarist rule—namely, reliance on short-term empirical relationships
in the data to drive policy implications. As Hetzel demonstrates, policies derived from the rule change over time, thus yielding questionable guidance.
33. There is an inconsistency to recent discussions concerning the role of money in monetary policy and the ultimate policy
objective of price stability. For instance, Meyer (2001) states that money “plays virtually no explicit role in the conduct of
monetary policy” in the United States and that “money matters—indeed it is just about all that matters—for inflation in the
long run.” While price stability is widely acknowledged as the appropriate long-run objective of monetary policy, many economists argue that policymakers should respond to fluctuations in real output or employment as part of their strategy to
achieve price stability and, ultimately, to support maximum sustainable economic growth. This position is taken in Mishkin
(2000), for example.
34. Consider Estrella and Mishkin’s argument that “the inability of monetary aggregates to perform well as straightforward information variables in recent periods has the implication that they cannot be used to signal the stance of monetary policy, an
important requirement if money growth targets are to be used as part of a strategy to increase the transparency of monetary policy to the public and the markets” (1996, 29).
35. All data are from the December 2000 International Financial Statistics CD.
36. This correlation between money growth, income growth, and inflation using a cross-section of countries has been documented previously. For recent examples, see Dwyer and Hafer (1988, 1999) and the references cited therein.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

21

the economy. The analysis uses rolling averages of
growth rates over one- three- and five-year intervals. This approach, similar to that of Dewald (1998)
and Dwyer (1998), smoothes short-run fluctuations
in the series that may mask the underlying, longterm relationship.37 The correlations are reported
in Table 2.
The results based on annual observations indicate
a wide range of correlation for the money-price link.
The correlation between M1 growth and inflation for
the United States is 0.21. Using M2, the annual correlation is zero. This finding seems to support the contention that there is little informational content in the
money growth numbers that policymakers can
exploit. Looking across
countries, the range of
the annual correlation
is from –0.04 for the
Philippines to 0.97 for
Policies that increase
Indonesia. Considering
the money-inflation
money growth are more
relationship across
likely, over time, to generate
countries, the average
increased inflation, not
correlation between
M1 growth and inflafaster growth in the production is about 0.25 pertion of goods and services.
cent. Using M2 growth,
the average correlation increases slightly
to 0.40 percent. In
either case, these correlations suggest a fairly loose relationship at an
annual horizon. Indeed, this evidence suggests that
the money-inflation link is rather weak over a period
as short as one year.
When the growth rates are averaged over time, the
correlation between money and inflation generally
increases. In Thailand, for example, the M1-inflation
correlation is essentially zero with annual data but
increases to 0.27 using the three-year average data
and to 0.42 for the five-year averaged data. If M2 is
used, the five-year correlation jumps to 0.63. This
increase is also found in most other countries for
which the annual correlations are rather low. For
instance, the M2-inflation correlation using annual
data for the United States is 0.21 but is 0.56 using the
five-year interval. In one instance—Canada—there is
no noticeable increase in the correlation between M1
growth and inflation even as longer averages are used.
However, the money-inflation correlation in Canada is
noticeably larger using the broader M2 measure: the

correlation increases from 0.57 percent with annual
data to 0.74 percent using five-year averages.
The results in Table 2 indicate that the link
between money and inflation improves as the time
horizon increases. The cross-country average correlation between M1 growth and inflation is 0.25 at an
annual frequency but 0.60 percent when five-year
averages are used. Similarly, the correlation
between M2 growth and inflation jumps from a sample average of 0.40 using annual data to 0.70 with
five-year averages. These results are consistent with
the proposition that money growth and inflation are
related more closely in the long run.
Table 2 reveals, in all instances but one, a positive
correlation between annual money growth and nominal GDP growth, and in most cases this correlation
increases as the time interval expands. For example,
for the United States the correlation between annual
M2 and GDP growth rates is 0.49, increasing to 0.85
when the data are averaged over five years. A similar
increase in correlation is reported for most other
countries although the magnitude of the increase
varies. As with inflation, comparing the averages
across countries is useful. For instance, the sample
average money-GDP correlation using annual data is
0.40 percent using M1 and 0.57 percent using M2.
When five-year averages are used, the correlation
increases to 0.65 and 0.83, respectively. This evidence
indicates not only that there is a positive correlation
between money growth and nominal income growth
but that this correlation increases as the time interval increases. This outcome also is consistent with the
proposition that income growth and money growth
are positively related.
Finally, monetarists often claim that the correlation
between money growth and real income growth weakens over time relative to money-inflation and moneynominal income. The results in Table 2 bear this out.
The correlation between money growth and real GDP
growth using the five-year averages is considerably
smaller than the corresponding correlations between
money growth, inflation, and nominal income growth.
Even though there are instances in which the correlation appears relatively large (for example, Malta [0.84]
and Japan [0.82]), on average the money-real income
correlations are smaller. This general view again is supported by measuring the average correlations across
countries. The average M1–real income correlation is
0.26 percent at an annual horizon and only 0.19 using
the five-year averages. If M2 is used, the correlations
are 0.27 and 0.17, respectively. This evidence suggests

37. Dwyer (1998, n. 3) notes that a drawback of using rolling averages is that it induces serial correlation. Because each observation uses overlapping data, the usual tests for zero correlation are invalid. Even so, such averaging does not preclude comparing correlations as the time interval changes.

22

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

TA B L E 2
Correlations
M1
Time Interval
Pair a

1-Year

3-Year

5-Year

1-Year

3-Year

5-Year

Canada

M, P
M, GDP
M, RGDP

.00
–.09
–.10

.05
.14
.14

.16
.17
.03

.57
.61
.17

.71
.81
.24

.74
.81
.15

Chile

M, P
M, GDP
M, RGDP

.66
.78
.72

.69
.77
.57

.75
.75
.37

.60
.51
.14

.86
.62
–.12

.87
.72
–.21

Colombia

M, P
M, GDP
M, RGDP

.40
.50
.12

.77
.53
.06

.87
.46
–.12

.57
.70
.18

.84
.90
.06

.90
.93
–.10

Denmark

M, P
M.GDP
M, RGDP

.21
.26
.07

.55
.41
.11

.62
.41
.13

.33
.38
.08

.50
.72
.05

.49
.74
.07

Egypt

M, P
M, GDP
M, RGDP

.44
.62
.33

.32
.76
.50

.28
.81
.49

.62
.76
.29

.71
.87
.38

.77
.92
.33

Iceland

M, P
M, GDP
M, RGDP

.78
.81
–.02

.92
.93
–.06

.95
.94
–.17

.82
.82
–.08

.93
.92
–.12

.96
.94
–.20

Indonesia

M, P
M, GDP
M, RGDP

.35
.56
.38

.74
.86
.43

.94
.97
.58

.57
.70
.27

.72
.79
.30

.95
.96
.50

Japan

M, P
M, GDP
M, RGDP

.30
.59
.50

.46
.77
.62

.52
.84
.68

.41
.78
.65

.49
.91
.79

.55
.96
.82

Korea

M, P
M, GDP
M, RGDP

.19
.55
.50

.58
.86
.71

.74
.92
.75

.44
.53
.21

.78
.82
.35

.77
.82
.51

Malta

M, P
M, GDP
M, RGDP

.37
.56
.39

.64
.79
.54

.70
.82
.59

.06
.59
.70

.36
.83
.81

.52
.89
.84

Pakistan

M, P
M, GDP
M, RGDP

.02
.16
.25

.31
.32
.02

.61
.64
.06

.04
.19
.26

.34
.40
.11

.61
.66
.10

Philippines

M, P
M, GDP
M, RGDP

–.04
.08
.18

.51
.59
–.10

.70
.70
–.41

.14
.30
.17

.47
.53
–.11

.74
.66
–.58

South Africa

M, P
M, GDP
M, RGDP

.04
.34
.33

.28
.40
.13

.45
.40
–.27

.17
.57
.49

.33
.70
.42

.61
.73
–.13

Thailand

M, P
M, GDP
M, RGDP

–.01
.17
.21

.27
.51
.33

.42
.63
.34

.40
.63
.32

.51
.77
.39

.63
.84
.37

United States

M, P
M, GDP
M, RGDP

.00
.10
.11

.14
.22
.03

.28
.25
–.16

.21
.49
.21

.43
.77
.19

.56
.85
.13

Country

a

M2
Time Interval

The variables are money growth (M); the inflation rate (P), measured using the CPI; nominal GDP growth (GDP); and real GDP growth
(RGDP). All variables are measured as logarithmic first differences.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

23

that, in the long run, changes in money growth are
more likely to affect changes in nominal income and
inflation than changes in real output.
Why should money be related more closely with
changes in prices and nominal GDP than changes in
real output? If real output growth is, over time, determined by real factors, such as population growth or
technology, then changes in money growth should be
reflected in prices and nominal income (see equation
[2]). The correlations do not reject the notion that, in
the long run, changes in the growth rates of money
have less effect on the path of real economic activity
than on nominal income growth and inflation.38 This
finding is in line with monetarist propositions and has
an important policy
implication: Policies
that increase money
growth are more likely,
over time, to generate
It appears that changes in
increased inflation,
the real money stock may
not faster growth in
significantly affect shortthe production of
goods and services.
term economic activity
Money and Nomieven after the impact of
nal Income. Equachanges in the real rate of
tion (1) suggests that
changes in the stock
interest is accounted for.
of money are directly
and positively associated with movements
in nominal income,
given the k-ratio. This hypothesized relationship is
used to examine an important monetarist proposition—namely, that there exists a positive connection between changes in the stock of money and the
level of nominal income. The correlations in Table 2
generally do not reject this notion. To further check
whether money growth can serve as an indicator of
nominal income growth, this analysis employs socalled Granger causality tests. The idea is to determine whether there is information in money growth
that, once estimates have been conditioned on past
income growth, significantly improves the prediction of income. Even though such test results should
be viewed with some caution, they are instructive.
Table 3 reports the outcome of these pairwise
causality tests between money growth (M1 and M2)
and nominal income growth.39
The first two columns of Table 3 report statistics
associated with testing the hypothesis that M1
growth does not cause GDP growth. A statistically
significant test statistic allows one to reject that
hypothesis. The hypothesis is rejected in ten
instances at a 10 percent level of significance (eleven
if one permits the 12 percent significance level found
24

for Pakistan). These results suggest that in twothirds of the countries examined there is evidence
that changes in money growth have a significant
impact on nominal income growth. The second column tests the companion hypothesis, whether GDP
growth does not cause M1 growth. The results of
that test indicate that this hypothesis is rejected in
five cases, again using a 10 percent level of significance. The results suggest that GDP growth does not
cause money growth in nine out of fifteen instances.
The third and fourth columns of Table 3 report
the results using the broader M2 measure of money.
Overall, the results are comparable to those using
M1. The hypothesis that M2 growth does not cause
GDP growth is rejected in eight instances, and in
seven cases the hypothesis that GDP growth does
not cause M2 growth is rejected. These results suggest that the choice of the monetary aggregate has
some effect on the test outcome. Overall, the results
in Table 3 indicate that there is a causal link from
money growth to nominal income in many countries.
An even more restrictive hypothesis can be tested:
Does money have a unidirectional effect on nominal
GDP? This hypothesis is important to establish the
usefulness of monetary aggregates in conducting
monetary policy. If changes in money do not stem
from changes in income, money could serve as a useful measure of the thrust of policy actions.40 The
results for M1 found in Table 3 indicate that one would
answer this question in the affirmative for seven countries: Canada, Colombia, Egypt, Japan, Malta, the
Philippines, and Thailand. Unidirectional causation
from M2 to income cannot be rejected for Canada,
Colombia, Egypt, Korea, and the Philippines. Note
that changing the definition of money affects the outcome for Malta, Korea, and Thailand. The array of
economic experiences captured by this subsample of
countries suggests that the money-income relation
does not hold only for certain types of economies.
Conversely, is evidence of unidirectional causation
running from GDP growth to money growth? Such a
finding is most damaging to the idea that monetary
aggregates are useful in setting policy because it signals that money growth is not exogenous to changes
in income growth. The hypothesis that GDP unidirectionally causes M1 is not rejected in only two countries—Iceland and Pakistan. In the remaining
countries, there is evidence of bidirectional causation
(Chile, Denmark, and Korea) or no discernable relation (Indonesia, South Africa, and the United States).
Replacing M1 with M2 leads to the following outcomes: the hypothesis that GDP growth unidirectionally causes M2 is not rejected for Iceland, Malta,
Pakistan, and South Africa at the 10 percent level.
Bidirectional causation is not ruled out for Denmark,

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

TA B L E 3
Pairwise Causality Tests
F-statistics (Probability)

Country
Canada
Chile
Colombia
Denmark
Egypt
Iceland
Indonesia
Japan
Korea
Malta
Pakistan
Philippines
South Africa
Thailand
United States

M1 Does Not
Cause GDP
2.45
3.41
18.88
4.43
11.74
0.78
0.88
5.06
5.05
4.16
2.26
13.13
0.40
4.04
0.68

(0.09)
(0.05)
(0.00)
(0.02)
(0.00)
(0.46)
(0.43)
(0.02)
(0.02)
(0.02)
(0.12)
(0.00)
(0.67)
(0.02)
(0.51)

GDP Does Not
Cause M1
0.33
5.68
1.28
7.07
0.41
9.24
0.32
1.76
3.73
1.58
3.06
0.78
0.61
0.82
1.16

Japan, and the United States. The remaining countries indicate no reliable statistical relation. The fact
that the GDP-to-money causation is relatively weak
across most countries suggests that money may possess potentially useful policy information.
Money and Inflation. The view often stated by
policymakers is that the objective of monetary policy is to keep inflation at bay. Some central banks
announce explicit inflation targets although Mishkin
(2000) points out that the Federal Reserve has been
reluctant to do so. As Meyer (2001) notes, “Given
the widespread commitment to price stability, monetarists believe that central banks should therefore
give appropriate attention to money growth in the
conduct of monetary policy.” Is there evidence to
support this belief?
A number of recent studies find that movements
in the nominal money stock and the price level are
positively related. Two approaches are used in these
studies. Dewald (1998), for example, averages money

(0.72)
(0.01)
(0.29)
(0.00)
(0.67)
(0.00)
(0.73)
(0.18)
(0.04)
(0.22)
(0.06)
(0.46)
(0.55)
(0.45)
(0.33)

M2 Does Not
Cause GDP
3.21
0.51
5.67
2.94
18.41
1.49
0.41
7.78
3.66
1.36
1.23
11.99
2.02
1.09
8.18

(0.05)
(0.61)
(0.01)
(0.06)
(0.00)
(0.24)
(0.67)
(0.00)
(0.04)
(0.27)
(0.30)
(0.00)
(0.15)
(0.35)
(0.00)

GDP Does Not
Cause M2
1.22
1.88
2.23
6.32
0.36
9.92
0.52
2.89
1.79
8.54
5.18
0.09
2.57
1.98
3.18

(0.30)
(0.19)
(0.12)
(0.00)
(0.70)
(0.00)
(0.60)
(0.07)
(0.19)
(0.00)
(0.01)
(0.91)
(0.10)
(0.15)
(0.05)

growth and inflation data over time, sometimes for
periods as long as a decade. The other approach,
used in Dwyer and Hafer (1988, 1999), for example,
averages data over shorter time spans but across a
large number of countries. The analysis in this article
examines the temporal relationship between money
and prices on a country-by-country basis to gauge the
generality of the connection and to illustrate the idiosyncratic nature of the relationships.
To better illuminate the link between money and
prices, equation (2) can be solved for the price level
to yield
(4)

P = k–1(M/y).

Equation (4) states that, given the k-ratio, changes
in the ratio of money to real output are reflected in
the price level. If the k-ratio is relatively stable over
time,41 the price level and money per unit of output
should move together over time.

38. In a similar vein, Barro (1996) finds that there is no significant relation between inflation and economic growth for a large
sample of countries. If inflation is, in the long run, determined by money growth, then Barro’s results imply that money
growth and real economic growth also are not related over time.
39. For each test, two regressions are estimated, one with nominal income growth as the dependent variable, another with
money growth as the dependent variable. To conserve on degrees of freedom, the explanatory variables in each regression
consist of two lags of money growth and nominal GDP growth. In essence, then, the causality tests conducted here simply
ask whether there is any information in the variables that, after the estimates are conditioned with lags of the dependent
variable, improve the explanatory power of the equation.
40. A classic treatment of the instrument-indicator issue is Brunner (1969).
41. This point has been the subject of intense and long-lasting debate, whether the issue revolved around the k-ratio or the
demand for real money balances. Although there is evidence that the demand for money is somewhat volatile in the short
run, there is compelling evidence to suggest that the economic relationship is stable over time. See, for example, Hoffman
and Rasche (1996) and the articles cited therein for evidence on this point.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

25

Real output (y) plays an important role in this
story. The range of average output growth for the
countries studied here is considerably less than the
range for inflation and nominal income growth (see
Table 1). This result suggests that real output growth
may be determined less by nominal factors, such as
money growth, and more by real factors, such as population growth, changes in technology, and changes
in the capital stock. If one takes output in the long
run as being determined exogenously to monetary
policy, then the only impact of changes in the growth
rate of money is on the price level. In other words,
equation (4) shows that if the k-ratio is stable and
real output is determined independently of money,
there is a one-to-one
connection between
changes in money and
The data indicate that
the price level.
To investigate the
money growth is directly
link
between money
related to nominal income
and prices within the
growth and inflation.
context of equation (4),
the ratio of money to
Moreover, the evidence
real income is plotted
suggests a weaker relation
along with the price
between money growth
level for each country.
The resulting graphs
and real output growth
are shown in the chart
in the long run.
on page 27. The scale
for each graph is logarithmic—that is, the
slope of the money-output ratio and the price level
lines represent rates of change. Similar slopes thus
indicate that the growth rates of the underlying
series are positively related.
The chart shows that for every country there is a
positive, long-run relationship between the money and
price series. It is informative that the recent deviation
in the United States, which occurred during the early
1990s, is not unique. Such deviations occur, sometimes
even frequently, but the two series persistently move
together over time. Whether for a high-inflation country such as Indonesia or Chile or a low-inflation
country like Japan, the plots in the chart indicate that
increases in the growth of money, given output growth,
are associated with higher rates of inflation.
Correlations between the two series (not reported)
indicate that there is nearly a one-to-one relation
between money and prices.42 This evidence corroborates the results in Table 2, where the correlation

between money growth alone and inflation increases
over lengthening time intervals. The upshot is that an
increase in the growth rate of money relative to real
output is likely to impart upward pressure on the
price level. Failure by central bankers to heed this signal may create inflationary increases that, as occurred
in the past, necessitate restrictive measures.43
Money and Real Output. Current U.S. monetary
policy, according to Meltzer (1998, 1999), McCallum
(1999), and Meyer (2001), utilizes several economic
models in which money generally plays no direct role.
This view is based on a popular macroeconomic
model in which movements in real output are a function solely of changes in the real rate of interest. In
this model, monetary policy affects real economic
activity only indirectly through its impact on the real
rate of interest. Movements in the money supply,
therefore, are viewed as having no independent
effect on output.44
The policy implications of this so-called consensus
model have been criticized by a number of economists,
such as Meltzer (1999) and Nelson (2000). The popular view is that policy actions taken by a central bank
first produce changes in a number of financial returns.
The transmission mechanism—the route by which policy actions affect the real economy—thus works primarily through an interest rate channel. A change in
policy—that is, a change in the target federal funds
rate—leads to a series of changes in other interest
rates that induce individuals to reallocate portfolios
of financial and real assets, thus producing a change
in economic activity. Taking such a narrow focus usually means that one considers only one real interest
rate as reflective of policy actions—thus, the focus
on the federal funds rate as the sole policy indicator.
This narrow view ignores the potential effects
that arise through other avenues, such as changes in
real long-term rates or in the return on real assets.
Meltzer (1999) tests for the impact of monetary policy actions on aggregate demand by estimating a
consumption function in which both short-term real
interest rates and real money balances appear.
Arguing that prices are relatively sticky in the short
run, Meltzer finds that, even after accounting for the
effect of the short-term real rate, movements in the
real monetary base exert a statistically significant,
independent effect on consumption.
Nelson (2000) also tests for the effect of changes
in real money balances on aggregate demand using
data from the United States and the United Kingdom.

42. The lone exception is Malta, where the correlation is 0.84. For all other countries, the correlation exceeds 0.90.
43. For a useful discussion of such policies and the inflation they engendered in the United States, see Mayer (1999).
44. Examples of recent studies employing such models include Rudebusch and Svensson (1999, 2000) and McCallum and
Nelson (1999), among others.

26

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

The Correlation between Money and Prices

10

Canada

10

Prices

1

Chile

Colombia

10
1

1

0.1
0.1
0.01
Money

1955 1965 1975 1985 1995
Denmark

10

1979 1983 1987 1991 1995

1960

Egypt

10

1970

1980

1990

Iceland

10
1

1

1

0.1
0.01

1955 1965 1975 1985 1995
Indonesia

Lo ga rith mic Sc a le

10

1957

1967

1977

1987

1997

Japan

10

1956

1966

1976

1986 1996

Korea

10

1
1
0.1

1
0.1

0.01

1970

1980
Malta

10

1955

1990

1965

1975

1985

1995

Pakistan

10

1971

1981

1991

Philippines

10

1
1

1
0.1

1962

1972

1982

1992

South Africa

10

1

1961

1971

1981

1991

1955 1965 1975 1985 1995

Thailand

10

1

1970

1980

1990

United States

10

1

1955

1965

1975

1985

1995

1964

1974

1984

1994

Source: International Monetary Fund, International Financial Statistics CD, December 2000.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

27

TA B L E 4
Detrended Output Regressions: M1
Country
Canada

Chile

Denmark

Japan

South Africa

Pakistan

y t–1

y t–2

1.084
(7.76)

–0.297
(1.86)

1.194
(7.36)

–0.387
(2.46)

1.285
(8.54)

–0.659
(4.43)

0.969
(6.05)

–.0387
(2.43)

1.112
(8.31)

–.0267
(2.70)

1.231
(7.55)

–0.418
(2.32)

1.259
(9.23)

–0.390
(3.19)

1.027
(6.34)

–0.256
(1.99)

1.049
(6.84)

–0.450
(3.32)

0.898
(5.01)

–0.327
(2.06)

–0.007
(3.67)

realt–2

0.004
(2.54)

0.001
(0.30)

moneyt–1

0.210
(2.15)

–0.002
(1.20)

0.107
(1.85)

–0.001
(0.70)

0.282
(4.29)

–0.001
(0.70)

0.103
(1.97)

0.002
(1.63)

1.155
(8.31)

–0.442
(3.29)

1.030
(7.62)

–0.250
(1.25)

moneyt–2

0.059
(1.23)

0.884
(10.92)
0.856
(9.56)

United States

realt–1

–0.114
(1.84)

0.001
(0.42)

0.107
(1.10)

–0.113
(2.01)

adjR 2

DW

0.710

1.85

0.785

1.86

0.799

2.53

0.821

2.43

0.760

1.94

0.787

1.91

0.847

1.93

0.877

1.76

0.588

1.86

0.599

1.86

0.768

1.64

0.774

1.52

0.685

1.96

0.686

1.81

Notes: All equations include a constant term. Figures in parentheses are absolute values of t-statistics based on White heteroskedasticityconsistent standard errors.

In contrast to previous findings (such as Rudebusch
and Svensson 2000), Nelson reports that deviations
in real output from its trend (or potential) are
explained by real short-term interest rates and real
monetary base growth. Nelson’s finding is important
because it demonstrates a direct, independent effect
of changes in monetary aggregates on aggregate
demand.45 Movements in the monetary base—an
aggregate over which the monetary authority arguably
28

has some control—thus affect the real economy in
the short run. Nelson argues that “when yields
besides the short-term rate enter both the IS and LM
relations, it is possible that real money growth might
be a valuable summary statistic for these yields and
might therefore contain information about GDP not
present in short-term interest rates” (2000, 18).46
This article tests for the independent effects
of real money balances on real output once the

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

effects of a real short-term interest rate have been
accounted for. Using Nelson’s approach, the following equation is estimated:
(5)

yt = a + bi yt–i + cirt–i + dimt–i + et ,

where y is deviations of real output from trend, r
represents the real rate of interest, m is real money
balances, e is an error term, and the terms a–d are
coefficients to be estimated. Nelson (2000) provides
a discussion of the underlying theory, which predicts that the expected sign on the real rate of interest should be negative: An increase in the real rate,
if all other factors remain the same, should lower
aggregate demand. The expected sign on real
money balances is positive, suggesting that expansionary monetary policy leads to a (temporary)
increase in real output growth above trend.
The paucity of data on short-term rates reduces
the sample to seven countries: Canada, Chile,
Denmark, Japan, Pakistan, South Africa, and the
United States.47 As a first approximation, real interest rates are calculated as the observed nominal
interest rate minus the actual rate of inflation. To
calculate the growth of real money balances, nominal money balances are deflated by the CPI, and the
logarithmic first difference of the series is calculated.
Finally, recent work focuses on the impact of real
rates and real balances on deviations of real GDP
from potential or trend values. Since measuring
potential GDP is difficult under the best of circumstances, this analysis measures the output gap as
deviations of real GDP from a quadratic trend.48
Table 4 reports the outcome from estimating
equation (5) for the seven countries when M1 is the
monetary aggregate used. Two regressions are
reported for each country. The first is a regression
of the output gap on its own lagged values. The lag
length reported is based on experimentation with
longer lag lengths, using only the last lag that
achieves statistical significance. In most instances
the significant lags are limited to the first two. The

second equation adds to this equation lagged values
of the real interest rate and lagged values of real
money growth.
The results generally indicate that lagged real
interest rates do not achieve statistical significance. For example, the real rate is significant only
for Canada, and even there the cumulative effect is
quite small. Similarly, Nelson (2000) also reports
that the real rate is insignificant (and positive) for
the United Kingdom. These results do not support
the hypothesis that changes in the real rate of
interest explain movements in detrended output
growth. The results for lagged real M1 growth are
more positive, though not overwhelming. Across
the countries tested, this study finds that money
generally exerts a positive effect on detrended real
output for Chile, Japan, and South Africa. In two
instances, Denmark and Pakistan, the estimated
coefficients are counter to the theoretically expected
positive sign.49
Table 5 reports the results when M2 is used to
estimate equation (5). Switching to the broader
measure leads to money’s insignificance for Chile, in
contrast to the outcome found using M1. However,
switching to the broader measure produces a significant result for the United States. Overall, using M2
yields a significant monetary effect on the output
gap in Canada, Denmark, Japan, South Africa, and
the United States. In these five cases, an increase in
real M2 growth, all other things being equal, is associated with an increase in the output gap.
These results are supportive of Meltzer (1999)
and Nelson (2000). It appears that changes in the
real money stock may significantly affect shortterm economic activity even after the impact of
changes in the real rate of interest is accounted for.
Moreover, it should be noted that the importance of
the real rate of interest is by no means supported in
these results. Though tentative, the results reported
here, especially using M2, do not support the widely
held opinion that money should play no role in monetary policy.

45. There is literature that addresses the unresolved issue of whether real output is affected in the short run by changes in
money growth independently of changes in short-term interest rates. For recent studies of this issue and evidence suggesting that there is a significant money-output link, see Hafer and Kutan (1997) or Swanson (1998) and the articles
cited therein.
46. Nelson (2000) provides a theoretical model in which the appearance of real money balances is justified as an explanatory
variable in the model. As he suggests ( p. 28), real money balances act as a proxy for the effects of policy actions on the multitude of yields that in all likelihood enter the aggregate demand and money demand functions.
47. The rates used for each country are the T-bill rate (Canada and South Africa), market lending rate (Chile), discount rate
(Denmark), call money rate (Japan and Pakistan), and the federal funds rate (the United States). All rates are from the
International Monetary Fund’s International Financial Statistics database.
48. This series is generated as the residual from a regression of log real GDP on time and time squared.
49. Although not reported, this analysis also tested for temporal stability in the extended equations. In all cases except Canada,
the calculated test statistics do not permit rejection of the hypothesis of stability. The break point tested is 1980.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

29

TA B L E 5
Detrended Output Regressions: M2
Country
Canada

Chile

Denmark

Japan

South Africa

Pakistan

y t–1

y t–2

1.084
(7.76)

–0.297
(1.86)

1.171
(6.34)

–0.396
(2.20)

1.285
(8.54)

–0.659
(4.43)

1.235
(5.76)

0.640
(3.89)

1.112
(8.31)

–0.0267
(2.70)

1.113
(7.26)

–0.278
(1.56)

1.259
(9.23)

–0.390
(3.19)

1.241
(10.07)

–0.373
(3.20)

1.049
(6.84)

–0.450
(3.32)

0.736
(5.31)

–0.185
(1.43)

–0.008
(3.32)

realt–2

0.004
(2.79)

0.001
(0.11)

moneyt–1

0.073
(0.28)

–0.002
(1.22)

0.144
(2.12)

–0.002
(0.84)

0.409
(4.57)

–0.001
(0.83)

0.361
(3.39)

0.002
(1.42)

1.155
(8.31)

–0.442
(3.29)

0.728
(5.24)

0.155
(0.77)

moneyt–2

0.034
(0.32)

0.884
(10.92)
0.855
(8.98)

United States

realt–1

–0.084
(1.26)

0.001
(1.13)

0.435
(4.79)

–0.238
(2.12)

adjR 2

DW

0.710

1.85

0.776

1.86

0.799

2.53

0.780

2.42

0.760

1.94

0.779

1.81

0.847

1.93

0.886

1.95

0.588

1.86

0.660

1.79

0.768

1.64

0.776

1.56

0.685

1.96

0.796

1.69

Notes: All equations include a constant term. Figures in parentheses are absolute values of t-statistics based on White heteroskedasticityconsistent standard errors.

Conclusion
o what does remain of monetarism? Does
money matter? The evidence presented in
this article suggests that a blanket dismissal
of monetary aggregates as uninformative for policy
decisions is premature. The data from a variety of
economies indicate that money growth is directly
related to nominal income growth and inflation.
Moreover, the evidence suggests a weaker relation

S
30

between money growth and real output growth in
the long run. These findings change as the time
horizon moves from annual to multiyear averages.
But the pattern is what monetarism suggests
should occur, in keeping with its foundation in the
quantity theory. While these results do not support
a version of monetarism in which short-term
manipulation of the monetary aggregates delivers
direct and precise control over movements in

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

income and prices, they also do not reject the
notion that changes in money growth have important effects on the economy. Failure to acknowledge
this empirical fact could give rise to undesirable
policy consequences, as evidenced by the inflation
of the 1970s or the dramatic and deep recession of
the early 1980s.
If one is skeptical about the role of money and
prefers interest rates as the key policy tool, the
results presented here do not provide overwhelming
support for that position. It appears that there is
more likely to be a short-run response of real output
to a change in money growth than a change in real

interest rates. Of course, these estimates are based
on only one measure of the real rate, but the outcome is similar to Nelson’s (2000) more rigorous
analysis. Together, his results and those in this
study do not provide much empirical support for the
use of interest rates as key policy variables to
achieve stable economic growth.
The results presented here signal a call for continued research into the links between money and
the economy, the assessment of existing and new
measures of the money aggregates, and the role that
money should play in policy. In the end, it appears
that quite a bit of monetarism remains.

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33

Assessing Simple Policy Rules:
A View from a Complete
Macroeconomic Model
ERIC

M.

LEEPER

AND

TAO

ZHA

Leeper is a professor in the department of economics at
Indiana University, Bloomington. Zha is an assistant
vice president in the research department of the Atlanta
Fed. The authors thank Dan Waggoner for helpful discussions. This paper was presented at the Twenty-Fifth
Annual Economic Policy Conference of the Federal
Reserve Bank of St. Louis, October 19–20, 2000, and
published in the St. Louis Fed’s Review (July/August
2001); it is published here with minor changes.

P

OLICY ANALYSTS MUST MAKE TOUGH CHOICES: SHOULD THEY USE A MODEL IN WHICH THE
ECONOMIC BEHAVIOR IS STRIPPED DOWN AND EASY TO UNDERSTAND BUT WHOSE FIT TO DATA
IS CRUDE, OR SHOULD THEY USE A MODEL WHOSE FIT AND FORECAST PERFORMANCE ARE
GOOD BUT WITH ECONOMIC BEHAVIOR THAT IS NOT VERY DETAILED?

stories frequently dominates the desire to fit data.
This is not a choice between “simple” and “complex”
though it is sometimes couched as such. A model
must be simple if it’s to be understood. It must be
understood if it’s to inform policy debates.
Unfortunately, we understand models on a qualitative level, while we use them for policy analysis on a
quantitative level. Tensions arise in moving from
qualitative discussion to quantitative prediction.
The tensions are well illustrated by two popular
approaches to empirical analysis of monetary policy:
the New Keynesian (NK) and the identified vector
autoregression (VAR) approaches. Stylized models
of private behavior coupled with simple rules
describing policy behavior characterize NK work.
VARs consist of minimally identified dynamic
descriptions of private behavior coupled with a
detailed rule for policy behavior.1
The choice between the two approaches would
not matter if they offered the same interpretations
of policy behavior and the same predictions for the
impacts of changes in policy. But they do not.

THE NEED TO TELL TIDY

Much of the appeal of NK models derives from
their simplicity.2 Implications of the model are easy to
communicate and have rapidly become a standard
framework for discussing monetary policy. Simple
models often produce stark conclusions. NK models
deliver the stark conclusion that good monetary policy calls for the central bank to adjust the nominal
interest rate more than one-for-one with inflation.
Some authors argue that Federal Reserve behavior
under Alan Greenspan is superior—nearly optimal,
by some calculations—to Fed behavior before Paul
Volcker became chair in 1979.3 NK researchers base
their case that policy has improved on estimates of
the parameter that determines how much the Fed
adjusts the federal funds rate when inflation changes.
Estimates of a stronger response to inflation after
1979 than before 1979 underlie the NK case. An
unstable policy rule is the linchpin in the NK case
that monetary policy has improved. VARs, in contrast, tend to find little evidence of either important
instability in policy parameters or instability in the
dynamic impacts of exogenous shifts in policy.4

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13

Simplicity also makes NK models vulnerable. In
simple models, behavioral relationships are tightly
circumscribed and sparsely parameterized. As a consequence, each parameter carries a hefty share of the
model’s implications: the value of a single parameter
can mean the difference between inferring that policy
was stabilizing or destabilizing. Because dynamics
are carefully pruned, there is a great deal of simultaneous behavior. It is no surprise that this environment breeds identification problems.5 Taken together,
simplicity and simultaneity make it very difficult
to nail down estimates of critical parameters.
Despite this difficulty, single-equation estimation
techniques constitute the bulk of the empirical work
in the NK literature.
Ironically, Taylor’s
(1993b) econometric
The choice between the
modeling is the genesis for much of the
New Keynesian and VAR
recent NK work with
approaches would not
simple policy rules.
matter if they offered the
Identified VARs share
with Taylor’s analyses
same interpretations of
an emphasis on the
policy behavior and the
economic system
same predictions for the
rather than on single
behavioral relationimpacts of policy changes.
ships. In VARs, behavBut they do not.
ioral relationships are
loosely consistent
with theory. Dynamics
are intricate, typically unrestricted, and difficult to
interpret. These characteristics make the output of
VARs hard to communicate, and the models often
get treated as black boxes. Simultaneity is kept to a
minimum: some of the most widely cited VAR models
contain none at all. These blunt identifying assumptions, though controversial, can produce robust
results. Rarely does instability of a single VAR parameter carry important qualitative implications.
The two approaches share the objective of
explaining post–World War II U.S. data. Identifying
assumptions, which are what link economic behavior to economic data, sharply distinguish NK and
VAR approaches. We pursue that distinction to
explore the identification problems that plague any
attempt to tease policy behavior out of the tangle of
dynamic correlations in macro time series. We take
the view that NK models are restricted VARs.
Dynamic optimizing behavior generates both linear
and cross-equation restrictions. The latter group
typically arises to ensure that expectations are
rational and consistent with the model’s predictions.
In the first section of the article we use an offthe-shelf NK model to obtain identifying restrictions
14

in a three-variable model. We argue that identification problems pervade the model. Calibration offers
one solution to these problems. For example, calibrating key private parameters or policy parameters
can deliver economically sensible system estimates.
In such models it can be misleading to base
inferences about the effects of policy solely on
estimated policy parameters. The article’s next
section displays models that are stable despite the
fact that policy parameters do not satisfy the NK
criterion for “stabilizing” policy.
Some of the NK models’ simplicity stems from
their position on money: it’s irrelevant. Money plays
no role in the transmission of monetary policy, in the
setting of monetary policy, or in the formation of
expectations about policy. The monetary sector is a
sideshow. The third section introduces money.
Although this creates some new identification challenges, we argue that interpretations of historical
policy behavior can change dramatically once
money is reintroduced into the analysis. Estimates
in that section rely on identifying assumptions that
separate the behavior of money demanders from
that of the monetary authority. Our results underscore, however, that understandings of behavior can
change drastically when one moves away from relying on reduced-form correlations.
The fourth section puts a sharp point on the
trade-off between simplicity and robustness of
inferences. The identified VARs we report in that
section display remarkable stability across subperiods in the postwar data. The stability implies
there may not have been important changes in the
dynamic responses of the economy to exogenous
shifts in policy, raising doubts about the premise of
the NK conclusion of superior policy performance
in the past twenty years. Policy may, in fact, have
improved over time. But New Keynesians do not
make the case.
Some authors argue that because the behavioral
equations in NK models emerge from optimization,
it is reasonable to treat them as invariant to policy
(Rotemberg and Woodford 1997 and Woodford
1999b). Similar claims cannot be made for equations in identified VARs. Instead, building on Sims
(1987), Leeper and Zha (2001) contend that VARs
are linear approximations to an underlying nonlinear model and that, for many practical policy
questions, the linear approximations may be quite
accurate. We shall not pursue this topic further
here; rather, we accept that, for the class of policy
interventions we think best characterizes routine
Federal Open Market Committee (FOMC) analysis,
both approaches estimate private behavioral equations that are virtually invariant.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

A Canonical New Keynesian Model
n this section we lay out a slight variant of the
stylized NK model that forms the basis of the
monetary policy analyses in Clarida, Gali, and
Gertler (1999, 2000), Rotemberg and Woodford
(1997, 1999), Woodford (1999a, 1999b), and elsewhere. Under certain parameterizations, the model
specializes to Taylor’s (1999b) reduced-form model.
The empirical results in this section relate to Taylor’s
version of the model estimated with U.S. data over
the period from 1959:Q1 to 2000:Q2.
The Theoretical Model. Because the microfoundations of the model are well known, we shall simply
write down the relevant log-linearized equations.
The generalized IS (investment demand and saving) equation is 6

I

(1) IS:

xt = − (1 / σ ) [ it − Et ( pt +1 − pt ) − r ]
+ κ θ xt −1 + κ(1 − θ )Et xt +1 + ε tIS ;

and the aggregate supply (AS) or price setting
equation is
(2) AS:

pt − pt −1 = λ 0 xt + λ1 xt −1
+ ψ( pt −1 − pt −2 )

ral elasticity of substitution; r is the steady state real
interest rate; κ is an indicator function equal to 0 or
1; θ and ψ lie on the unit interval; ε AS is an exogenous process reflecting deviations from the condition that real marginal cost and the output gap are
proportional; and β is the discount factor. The expectation Et is taken with respect to an information set
that contains all variables dated t and earlier.
As written in equations (1) and (2), our
approach allows for the possibility of both forwardand backward-looking behavior in the IS and AS
relationships. The parameters θ and ψ determine
the extent to which behavior looks forward and
backward. We are less concerned with whether
backward-looking behavior can be sensibly rationalized in an optimizing framework than we are with
extracting the model’s implications for empirical
work. To that end, it is desirable to work with a flexibly parameterized model.
Complete the model with the monetary policy
(MP) rule
(3) MP:

it = γ 0 + γ π1[( pt − pt −1 ) − π]
+ γ π 2[( pt −1 − pt −2 ) − π] + γ x1 xt + γ x 2 xt −1
+ γ m1[( Mt − Mt −1 ) − µ ] + ω it −1 + ε tMP,

+ (1 − ψ )βEt ( pt +1 − pt ) + ε tAS,
where x is the “output gap,” defined as actual output minus potential output:
xt = yt – ytp.
Here, i is the nominal interest rate, which is set by
the monetary authority; p is the aggregate price
level and pt – pt –1 is the inflation rate at t;7 ε IS is an
exogenous process reflecting nonmonetary policy
sources of aggregate demand; 1/σ is the intertempo-

where ω is a parameter that allows for partial adjustment to the target interest rate and determines the
degree of interest rate smoothing and ε MP is a policy
disturbance; γπ1 describes how policy responds to
inflation and is the parameter that receives the most
– and µ
– are target
attention in the NK literature. π
levels for inflation and money growth. This rule represents a substantial generalization of the class of
rules typically considered in NK research as it
allows policy choice to depend on the lagged inflation rate and output gap as well as on current and
past money growth.8 The rule that Taylor (1993a,

1. NK work is associated with Rotemberg and Woodford (1997, 1999), Clarida, Gali, and Gertler (1999, 2000), and McCallum
and Nelson (1999); identified VARs are associated with Leeper, Sims, and Zha (1996), Christiano, Eichenbaum, and Evans
(1999), and Bernanke and Mihov (1998). A third approach combines simple Taylor-type rules with large econometric models
of the economy as in Bryant, Hooper, and Mann (1993), Taylor (1993b), and Levin, Wieland, and Williams (1999).
2. At the conference, a semantic debate took place concerning whether the class of models we have in mind are “New
Keynesian.” Nothing substantive rests on the terminology. We adopted the term from Clarida, Gali, and Gertler (1999), who
labeled the literature “New Keynesian.”
3. See, for example, Rotemberg and Woodford (1999) or Gali, Lopez-Salido, and Valles (2000).
4. See, for example, Bernanke and Mihov (1998), Sims (1999), Leeper and Zha (2001), or Hanson (2000b).
5. NK models are not unique in this regard. Virtually all dynamic stochastic general equilibrium models suffer from the kind of
identification problems that concern us (see Canova and Pina 2000).
6. Although, strictly speaking, IS involves output rather than the output gap, in equation (1) we follow the convention in the
NK literature.
7. In the empirical work below, we convert this to the annual rate 4(p t – pt–1 ). To avoid notational clutter, we leave the conversion out of the theoretical expressions.
8. Papers by Clarida, Gali, and Gertler (2000) and Bernanke and Woodford (1997) also include policy responses to expected
inflation and output. This makes little difference for our purposes.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

15

1999a, 1999b) employs, and is now nearly standard
equipment in an NK model, sets i solely as a function of the current inflation rate and output gap:
(4) MP (Taylor):

it = γ 0 + γ π1[( pt − pt −1 ) − π]
+ γ x1 xt + ε tMP.

The two exogenous processes associated with private behavior are ε tIS and ε tAS, and ε tMP is the exogenous part of policy behavior.
Potential Identification Problems. Nearly all
the NK papers assume certain values for the private
parameters in equations (1) and (2). They then estimate the policy parameters using ordinary
least squares (OLS)
or instrumental variables methods or they
Even if particular policy
impose particular
policy parameters.
parameters are unstable,
Suppose instead that
when the dynamics of
the reduced form—or
behavior are well modeled,
solved-out version—
of equations (1), (2),
the equilibrium effects of
and (3) were to be
policy are quite stable.
estimated simultaneously. Although the
reduced form confounds private parameters and policy
parameters, we may work with it as long as we do
not intend to change policy parameters while holding fixed the reduced-form parameters in the nonpolicy equations. We can even solve the model
numerically, noting, where possible, the linear
restrictions the model implies and then imposing
those restrictions on our estimation. In this procedure we concentrate on restrictions on contemporaneous interactions among variables, which are the
most common identifying restrictions used in empirical work. Because cross-equation restrictions are
often at odds with data, we limit ourselves to the
linear restrictions that theory implies.
Inspection of the three equations of the model
suggests the potential for several identification
problems to arise. First, both IS and policy link the
current nominal rate to inflation and the output gap.
If inflation is close to a random walk, then both
equations involve (xt, πt, it ), and without additional
restrictions they cannot be distinguished. This problem is critical as it potentially confounds the impact
of monetary policy with other sources of disturbance to aggregate demand, causing misleading
interpretations of the role of monetary policy.
16

Taylor (1999b) resolves the identification problem by considering the model that emerges when
κ = 0, λ 0 = 0, and ψ = 1. In that case, the reducedform expression for IS makes xt = a(i t – π t – r) + ξ t ,
for some coefficient a, producing an additional
restriction that separates IS from monetary policy.9
If, in contrast to Taylor’s specification, the IS curve
is dynamic (κ = 1, θ ∈ [0, 1]), then Taylor’s additional
restriction does not hold generally, and nothing distinguishes the reduced forms for IS and policy.
One way to separate IS and MP is to adopt the
approach taken in some of the identified VAR literature and advocated by McCallum (1999) in NK models: an operational rule cannot make policy choice
depend on variables the Fed does not observe contemporaneously. Because the Fed does not observe
inflation and output contemporaneously, we might
posit the rule
(5) MP (Taylor lagged): it = γ 0 + γ π 2[( pt −1 − pt −2 ) − π]
+ γ x 2 xt −1 + ε tMP.
This rule equates the surprise in the federal funds
rate, given past inflation and output, to the exogenous disturbance in monetary policy. Unfortunately,
it is well documented that this identification can generate empirical anomalies; a prominent anomaly is
that an exogenous monetary contraction raises the
funds rate, lowers output, and raises the inflation
rate (see, for example, Gordon and Leeper 1994).
Although it is no longer fashionable to include
money in models of monetary policy, the Fed does
observe growth rates of various monetary aggregates contemporaneously. And for much of the postwar period the Fed established target growth rates
for aggregates. These targets have been pursued
with varying degrees of vigilance over the years
because, when velocity is fairly predictable, money
growth can be informative about future inflation.
Adding current money growth to the policy rule in
equation (5) produces
(6) MP (with money): it = γ 0 + γ m1[( Mt − Mt −1 ) − µ ]
+ γ π 2[( pt −1 − pt −2 ) − π]
+ γ x 2 xt −1 + ε tMP.
This specification is close to the rule that Ireland
(2000) estimates in an NK model. A policy rule like
equation (6) carries two important implications. First,
money is no longer an appendage to the NK model.
Now interaction of supply and demand in the money
market determines the money stock and the nominal
interest rate simultaneously. This raises the tricky
problem of separating money demand and monetary

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

policy. Second, the dynamic IS and AS relationships
imply that current inflation and output depend on the
entire expected future path of policy. If money enters
the policy equation, then it plays a role in forming
expectations of policy. The reduced forms for IS and
AS now must include the current money stock.
The presence of money in the IS equation raises
a new identification problem. Now both IS and
money demand include output, the price level, the
nominal interest rate, and the money stock. Without
further restrictions, IS and money demand are indistinguishable. Homogeneity restrictions play a
prominent role in money demand regressions; in
fact, the money demand relationship is usually written in terms of the demand for real money balances,
reflecting one homogeneity restriction. Another
restriction, which many general equilibrium models
of money demand imply, is unitary income elasticity.
Some VAR work has found it necessary to impose
both homogeneity restrictions to model the money
market (for example, Cushman and Zha 1997).
Estimated Models. We now illustrate some of
these identification problems with estimated models. The reduced form for the NK model with three
endogenous variables can be written as
(7)

rate, and y p is the Congressional Budget Office’s
measure of potential GDP (chained 1996 dollars).
We choose to estimate data in terms of levels, rather
than growth rates, in order to connect the work
more closely to the identified VAR literature. We
impose all the linear restrictions implied by the NK
model and execute maximum likelihood estimation
(MLE).11 All variables are logged except the funds
rate, which is a percentage.
In the models reported below we display both
the estimated parameters and the impulse response
functions computed from

where B(L)=(A0′ – A1′L – A2′L2 )–1.
Because many of our points are logical, illustrating the nature of identification problems rather than
statistical problems, we do not report standard errors
or error bands for most of the estimated models.
Taylor’s (1999b) Model. Taylor’s model is
described by
(9)

X t′ A0 = C + X t′−1 A1 + X t′−2 A2 + ε t′ ,

where Xt = ( yt, pt, it, y tp )′, C is a vector of intercept
terms, and εt =(ε tIS, ε tAS, ε tMP, ε typ)′.10 We take the exogenous disturbances to be independent and identically
distributed with ε t ~ N, (0, I ). In the estimation, we
follow tradition and treat potential output, y p, as
exogenous and estimate an AR(1) process for it.
Adding y p to the model in an exogenous block alters
the order condition substantially: it buys us three
zero restrictions while adding only one free parameter. Most NK work, however, includes the gap,
rather than y and y p separately. To keep in the spirit
of that work, we assess the order condition as if we
estimated the model in terms of (x, p, i ).
All data are quarterly and all but the interest rate
are seasonally adjusted. The estimation period in
this section runs from 1959:Q1 to 2000:Q2; y is real
gross domestic product (GDP) (chained 1996 dollars), p is the personal consumption expenditures
deflator (chained 1996 dollars), i is the federal funds

X t = B( L )ε t ,

(8)

IS:

xt = −(1 / σ )( it − π t − r ) + ε tIS

AS:

π t = λ1 xt −1 + π t −1 + ε tAS

MP:

it = γ 0 + γ π1 π t + γ x1 xt + ε tMP ,

where πt = pt – pt –1 is the inflation rate. In this
model, πt is inertial or predetermined while xt and
it are determined simultaneously. Based on restrictions on A0 alone, the model is not identified unless
some additional restriction is imposed. Taylor
imposes the restriction that the coefficients on the
nominal rate and inflation in the IS equation are
equal and of opposite sign.12 Imposing that restriction yields the estimates13
(10)

[

]

xt = 0.795 it − ( pt − pt −1 ) − 0.024

pt − pt −1 = 0.077 xt −1 + ( pt −1 − pt −2 )

it = 0.820 ( pt − pt −1 ) − 0.597 xt + 0.032.

With the exception of the AS relationship, none of
these parameters is reasonable. IS and MP relationships are confounded: the pattern of coefficients in
IS makes more sense as a policy rule, and the pattern

In this case, πt = λ1xt–1 + πt–1, so the IS relationship implies a = –1/σ (1 – λ1).
We impose restrictions to express equations in terms of inflation or the output gap.
First we obtain maximum likelihood estimates of A0; then we obtain estimates of (A1, A2, C ), conditional on the MLE of A0.
In Taylor’s model, the current output gap is excluded from AS. This exclusion restriction is necessary for identification from
restrictions on A0 alone. Without it, the model is underidentified for two reasons: because it adds the coefficient on xt in AS
and because, if xt enters AS, then the restriction on IS that the coefficients on i and π be equal and of opposite sign no longer
holds. See footnote 9.
13. The process for potential output is estimated to be ytp = 0.0297 + 0.998 ytp–1.

9.
10.
11.
12.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

17

CHART 1
Confounding IS and MP in Taylor’s Model
AS

IS

MP

1.3679

0

y

–2.1003
0.3752
0

p

–4.4527
1.7207

i
0

–1.033
0

4

8

12

0

4

of coefficients in the policy equation makes more
sense as an IS curve. Chart 1 confirms this interpretation.14 The shock identified as IS raises the funds
rate and lowers output (although only slightly),
while the shock identified as MP raises output, the
price level, and the funds rate. The latter is reasonable when interpreted as an endogenous response
of policy to higher output and inflation; it is unreasonable when interpreted as an exogenous monetary policy contraction.
Without further restrictions, there appears to be
no way to separate the two components of aggregate demand in the model. We turn now to two alternative solutions to this problem.
Calibration as Identification. Perhaps the most
popular solution to identification problems is to
impose parameter values obtained from other data
sets or previous research. While this approach
gained popularity initially in the real business cycle
literature, its popularity has carried over to research
using NK models. We show that transporting parameters from other studies certainly can solve the
18

8

12

0

4

8

12

identification problems inherent in separating IS
from MP. First we impose the intertemporal elasticity of substitution, 1/σ= 2, which is within the range
of values used in the literature, and freely estimate
the remaining parameters.15 Next we impose γ π1 =1.5
and γx = 0.5 in the Taylor rule and estimate the rest
of the model. Both approaches produce sensible
results, with monetary policy shocks having important effects on output. Inflation, however, appears
to be entirely an aggregate supply phenomenon.
Imposing 1/σ = 2 and not imposing Taylor’s
restriction on IS leads to the estimates
(11)

xt = − 2.0 it + 1.658 (pt − pt −1) + 0.063

pt − pt −1 = 0.077 xt −1 + (pt −1 − pt −2)

it = 1.027 (pt − pt −1) + 1.535 xt + 0.030,

where underlining indicates an imposed parameter value.
All the estimated parameters are reasonable. The
IS elasticity with respect to inflation is positive, as

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

CHART 2
Dynamic Responses in Taylor’s Model
IS

AS

MP

1.1857

0
y

–2.2082
0.3252
0

p

–4.4527
1.8204

i
0

–1.033
0

4

8

12

0

4

one would expect if output depends on the real
interest rate.
Most striking are the estimated policy parameters. The Fed raises the funds rate more than onefor-one with the inflation rate. It raises the funds
rate about 150 basis points in response to a 1 percent
increase in the output gap. A coefficient on inflation
that exceeds 1 implies stabilizing policy, according
to the standard interpretations of the policy rule
(for example, Taylor 1999b or Clarida, Gali, and
Gertler 1999). The system estimates in equation (11)
are in sharp contrast to OLS estimates of the policy
rule over this period:
(12)

OLS:

it = 0.86 ( pt − pt −1 ) − 0.14 xt + 0.03,

(0.07)

(0.07) (0.003)

8

12

0

4

8

12

which would seem to suggest that policy has not
been stabilizing on average since 1959. The substantive difference in estimates underscores the
importance of estimating policy behavior and private behavior simultaneously. Inferences about policy behavior based on the system estimates in
equation (10) are qualitatively different from those
based on single-equation estimates in equation (12).
Chart 2 displays the system’s responses to exogenous disturbances over four years. The third column
shows that MP has important effects on output: a
100 basis point exogenous contraction reduces output by 2 percent (as the calibrated value for σ
implies), though the effects die out immediately.
Policy disturbances matter for output, accounting
for over a third of its variability. Exogenous shifts in
policy, however, have little impact on inflation.

14. All charts depict impulse responses that have been multiplied by 100.
15. Rotemberg and Woodford (1997) estimate σ = 0.16, producing an IS interest elasticity of –6.25, while McCallum and Nelson
(1999) estimate σ = 4.93, making the interest elasticity –0.20. Clarida, Gali, and Gertler (2000) and Gali, Lopez-Salido, and
Valles (2000) calibrate their models to log preferences, so σ = 1.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

19

CHART 3
Implied Structural Shocks in Taylor’s Model
4
2

ε IS

0
–2
–4
1960

1965

1970

1975

1980

1985

1990

1995

2000

1960

1965

1970

1975

1980

1985

1990

1995

2000

1960

1965

1970

1975

1980

1985

1990

1995

2000

2

εAS

0

–2

4
2

εMP

0
–2
–4

Policy is strongly endogenous. Policy disturbances account for 20 percent of the variation in the
funds rate over the first year and for 10 percent over
longer horizons. Endogeneity of policy shows up in
the responses of the funds rate to IS and AS disturbances. An IS shock that increases the output gap
and gradually raises the price level brings forth a
higher funds rate. An outward shift in AS persistently raises the output gap, permanently lowers the
price level, and induces the Fed to lower the funds
rate. Only very gradually does the Fed return the
rate to its initial level.
Monetary policy shocks are the dominant source
of output variation (75 percent over the four-year
horizon), and AS disturbances are the sole source
of price level movements (more than 98 percent over
the horizon). AS shocks also account for threequarters of funds rate variability at four-year horizons.
Chart 3 displays the time paths of structural shocks
implied by the estimated model. With the exception of
the AS shock, the estimated disturbances exhibit
20

strong patterns of serial correlation, which arise from
the absence of dynamics in the behavioral equations.16
The time path of the monetary policy disturbances in
the bottom panel of the chart resembles the “policy
mistakes” that Taylor (1999a) reports in his historical
analysis of policy rules. Taylor computes the gap
between the actual funds rate and value of the rate
implied by two policy rules. He concludes that when
the gap was positive the funds rate was “too high” and
when it was negative the funds rate was “too low.” In
Chart 3, a positive value of ε MP is an exogenous tightening of policy and a negative value is an exogenous
loosening. Unlike Taylor’s work, the chart does not
report that policy in the early 1960s was “too loose.” It
is consistent with Taylor’s findings that in the second
half of the 1960s and the 1970s policy was “loose,”
while in the early to mid-1980s policy was “tight.” The
chart is also generally consistent with Taylor in finding
that through the 1990s “policy mistakes” were small.
We are unwilling, however, to draw the normative
conclusions Taylor does. In the model, as Chart 2

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

attests, exogenous shifts in policy have unrealistically
large impacts on output and essentially no impacts on
inflation. We prefer to link normative statements
about policy to the impacts policy has on variables
that affect private agents’ welfare. If those estimated
impacts are implausible, it seems premature to
deduce how well policy has performed from the estimated pattern of residuals in the policy equation.
Not surprisingly, the data strongly reject equation (11), a model with severely pruned dynamics.
Letting ξ denote twice the difference of the log likelihoods of the unrestricted and restricted models,
ξ = 2[log(LU) – log(LR )], we find ξ = 775.06, which
has a p-value of 0.00. Critical values for the Schwarz
and Akaike criteria are 122.4 and 48.
Imposing that policy be set according to the parameters Taylor (1993a, 1999a) employs (γπ1 = 1.5,
γx1 = 0.5) leads to the estimates
(13)

xt = 0.732 it + 0.545 ( pt − pt −1 ) + 0.024

(

pt − pt −1 = 0.077 xt −1 + pt −1 − pt − 2

)

it = 1.5 ( pt − pt −1 ) + 0.5 xt + 0.009,
where underlining indicates imposed parameter
values. The qualitative impacts of the three disturbances are much like those depicted in Chart 2 and
are not reported. Because the model’s parameters
are different, however, the quantitative implications
differ somewhat. The estimated interest elasticity of
IS is lower than in system (11), so the output effects
of policy disturbances in this model are smaller,
accounting for no more than 35 percent of output
variability. AS shocks explain one-quarter of output
forecast error variance and 80 percent of funds rate
variability over four-year horizons.
It is difficult to distinguish the reduced-form
expressions for IS and MP that emerge from the NK
model without introducing additional restrictions.
We showed that imposing an interest elasticity of IS
of –2.0, which is in the ballpark for calibrated NK
models, can solve the identification problem.
Estimates of policy behavior from the 1959:Q1 to
2000:Q2 period are consistent with the interpretation that the Fed has, on average, been stabilizing: it
raised the federal funds rate more than one-for-one
with inflation. This result comes from system estimates; OLS estimates of policy behavior produce a
response to inflation that is substantially below 1.0.
Although we solved the identification problem, the

estimated models imply little role for monetary policy in influencing inflation.

Inferences about Stability Based on
Policy Parameters
ne piece of conventional wisdom to emerge
from the NK work on MP is that policy is stabilizing when it raises the nominal interest
rate more than one-for-one with the inflation rate.
This increases the real interest rate, the argument
goes, reduces aggregate demand, and counteracts
the incipient inflation. In Taylor’s policy rule, equation (4), this requires that γπ1 > 1. Several authors
have drawn inferences about how policy impacts the
economy based on estimates of γπ1 (for example,
Clarida, Gali, and Gertler 1999, 2000; Rotemberg
and Woodford 1997, 1999; and Taylor 1999a).
Stability is a characteristic of an equilibrium and,
as such, is an implication of a model. Much of the
recent work on simple rules may give the impression
that one can deduce this model implication merely
by estimating a policy rule. Implicitly, many authors
are conditioning their assertions about the magnitude of a particular policy parameter on the structure and parameter values of an entire model.
Two different but related interpretations of
U.S. monetary policy behavior before the VolckerGreenspan era stem from inferences about stability
drawn from estimated policy rules. Taylor (1999b)
argues in the following way that policy is “stabilizing” when γπ1 > 1. Modify the AS relationship in his
model, equation (9), to be

O

(14)

π t = λ1 xt −1 + δπ t −1 + ε tAS.

AS:

Substituting MP into IS, and the resulting expression
for x into AS, yields a first-order difference equation
describing the evolution of equilibrium inflation:

(15)

 λ (1 − γ π1 ) 
πt =  1
+ δ π t −1
 σ + γ x1

−

λ1
λ1σ IS
ε tMP
ε t −1 + ε tAS .
−1 +
σ + γ x1
σ + γ x1

Taylor imposes δ =1. In that case, if λ1, σ, γx1 > 0,
which are reasonable assumptions, then γπ1 > 1 is
necessary and sufficient for equation (15) to be a
stable difference equation. Suppose the economy is
hit by an adverse AS shock that increases inflation. A

16. Clearly these errors are not independent and identically distributed as assumed in the estimation. Rather than estimate patterns of serial correlation for the shocks to render some residuals as white noise, we prefer to account for the data’s persistence through behavioral relationships. Allowing serially correlated errors, as is common in the literature, would improve
the fit but would not contribute to the economic interpretation of the data.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

21

CHART 4
An Unstable Equilibrium in Taylor’s Model (1959:Q1–1979:Q3)
AS

IS

MP

0

y

–1.9642
0

p

–6.2477
0.5497
0

i

–1.7457
0

4

8

12

0

4

sufficiently strong policy response to the initial
increase in inflation would raise the real interest rate,
reduce output, and stabilize inflation. In the absence
of a strong policy response, output might rise and,
through the AS relationship, raise inflation still more
in the future. The process can be explosive.
A second interpretation of the implications of
γπ1 < 1 comes from Rotemberg and Woodford (1997)
and Clarida, Gali, and Gertler (2000). In a maximizing
model with typical assumed values for private parameters, γπ1 < 1 implies that sunspot equilibria cannot
be ruled out. Expectations of higher inflation that
arise for unexplained reasons can be self-fulfilling.
Sunspot fluctuations may arise because economic
agents rationally believe that the Fed will accommodate higher expected inflation by letting short-term
real interest rates fall, stimulating aggregate demand,
and raising inflation further. We do not pursue this
interpretation in the present paper.
Both interpretations rely on estimates of γπ1 that
are substantially below 1.0 in the United States
before 1979.
22

8

12

0

4

8

12

We can see precisely the phenomenon that Taylor
discusses when we reestimate the model in equation
(10) over the sample 1959:Q1–1979:Q3. The estimates are
(16)

[

]

xt = − 2.621 it − ( pt − pt −1 ) + 0.041

pt − pt −1 = 0.105 xt −1 + ( pt −1 − pt −2 )

it = 0.671( pt − pt −1 ) + 0.571 xt + 0.023.

The critical policy parameter, the response of the
funds rate to inflation, is substantially less than 1 at
0.671. According to conventional wisdom, policy was
not stabilizing. Impulse response functions in Chart 4
bear out the conventional wisdom. Although the shortrun patterns make economic sense, the responses to
AS shocks are explosive, with output, the price level,
and the funds rate shooting off to negative infinity.
Explosiveness stems from the source Taylor highlights: the eigenvalue of the model’s difference equation in inflation in equation (15) exceeds 1.0.17
It may be surprising that an important ingredient
in generating instability is the restriction that δ =1 in

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

the AS specification in equation (14). We turn to the
NK model laid out in the first section, but move away
from the specific parameterization Taylor used.
Instead, we consider an environment in which behavior in both the IS and the AS equations is dynamic.
To obtain restrictions motivated by the theoretical
NK model, we calibrate and solve the model, deriving
the theory’s analogs to the (A0, A1, A2, C) matrices in
the estimated model, equation (7). We then apply the
pattern of linear restrictions implied by the theory to
our empirical model.18 Let Xt = (xt, pt, it )′ and order the
equations (IS, AS, MP). Let column j of Ai represent
equation j, j = IS, AS, MP. As an example, the pattern
matrices for Taylor’s (1999b) model, specified in equation (9) and estimated in equation (10), are
(17)
 × 0 ×
 0 × 0
0 0 0






A0 = − ×1 × 2 × 3  , A1 = − ×1 2 × 2 × 3  , A2 = 0 − × 2 0
 ×1 0 × 
 0 0 0 
0 0 0
where × denotes a freely estimated coefficient, 0
denotes a coefficient that is excluded, and × i denotes
a coefficient estimated subject to a dynamic linear
restriction within an equation. After estimation, setting the diagonal terms in A0 to 1 normalizes the
matrices. There are six freely estimated parameters
in A0, so the model is just identified.19
We now consider other versions of the NK model.
IS and AS relationships are dynamic with both
forward- and backward-looking behavior; policy follows the Taylor rule it = 2+1.5πt + 0.5xt.20 The reduced
form for this model implies the pattern matrices

(18)

× × × 
× 0 0 
0 0 0






A0 = × × ×1  , A1 = × × ×1  , A2 = 0 × 0.
× 0 × 
0 0 0 
0 0 0

Now the model determines all three variables simultaneously. With eight free parameters in A0, the
model is not identified. We follow Taylor in forcing
the effect of the output gap on inflation to occur with a
one-period lag (making A0 [1, 2] = 0 and A1[1, 2] = ×),
and we add the restriction that the nominal interest
elasticity of IS is –2.0: σ = A0 (3, 1) = 1/2. The estimated model for the period 1959:Q1–1979:Q3 is

(19)
xt = − 2.0 it + 1.126 pt − pt −1 + 1.495 xt −1 + 0.060

(

)

pt = 0.025 xt −1 + 1.9615 pt −1 − 0.9618 pt −2

(

)

it = 0.690 pt − pt −1 + 0.517 xt + 0.023.
Although we did not impose that the AS function can
be written in terms of the inflation rate, the estimates
are very close to πt = 0.025xt –1 + 0.962π t –1. As in
system (16), we estimate that the policy response
to inflation is well below one-for-one. This model,
however, does not display the instability following
AS shocks that appears in the previous model.
Chart 5 displays the impulse response functions over
a four-year horizon. All the responses look reasonable and converge after about ten years. The absolute
values of the largest eigenvalues of the estimated
system are 0.996 and 0.997. Evidently, γπ1 > 1 isn’t
necessary for stability.
Although the estimated response of policy to
inflation is weak, policy behavior is strongly endogenous. Over forecast horizons of one to four years,
over 20 percent of the fluctuations in the funds rate
are due to IS shocks and 45 percent are due to AS
shocks. Inflation is again estimated to be primarily an
aggregate supply phenomenon, with 85 to 100 percent of price level variation due to AS shocks. Policy
disturbances simply do not move the price level very
much, though they are more important than IS
shocks in accounting for output fluctuations in the
short run (60 percent versus 40 percent).
In spite of the widespread belief that the Fed
raised the funds rate less than one-for-one with inflation in the period from 1959:Q1 to 1979:Q3, it is
worthwhile estimating the same model with identification achieved by imposing the policy rule
it = γ0 +1.5πt + 0.5xt. With these two restrictions on
A0, we now freely estimate the interest elasticity of
IS (A0 [3, 1]) and the contemporaneous effect of output on price setting behavior (A0 [1, 2]). The model
determines (xt, it ) simultaneously. The estimates are
(20)
xt = − 0.555 it + 0.069 ( pt − pt −1 ) + 1.090 xt −1 + 0.027
pt = 0.053 xt −1 + 1.979 pt −1 − 0.979 pt −2
it = 1.5 ( pt − pt −1 ) + 0.5 xt + 0.11.

17. The largest eigenvalue is estimated to be 1.036.
18. Because we do not impose the cross-equation restrictions that the theory implies, the empirical model may be underidentified even when the theoretical model is not.
19. As equation (17) makes clear, there are additional restrictions among coefficients across the A i matrices. When we evaluate the order condition, we do not count these and focus exclusively on restrictions on A 0. One could instead investigate
achieving identification through the dynamic restrictions.
20. We set r = 2, σ = 1, θ = 0.2, κ = 1, λ0 = 0.3, β = 0.99, ψ = 0.2, γ0 = 2, γp 1 = 1.5, γx1 = 0.5, and the remaining parameters to zero.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

23

CHART 5
A Stable Equilibrium in Taylor’s Model (1959:Q1–1979:Q3)
AS

IS

MP

1.0433

0

y

–1.2772
0.7904
0

p

–2.7598
0.6386

i

0

–0.7346
0

4

8

12

0

4

Once again the model is stable, with largest eigenvalues equal to 0.997 and 0.999. Dynamic responses to
exogenous disturbances look reasonable, as shown
in Chart 6. The most notable quantitative difference
between this model and the previous one, equation
(19), is that now IS and MP disturbances are more
important sources of inflation variation, accounting
for 25 percent each over horizons of four years. AS
shocks continue to be the dominant source of inflation in the short run, but over longer periods, aggregate demand is as important as aggregate supply.
We also estimated two versions of the NK model
where AS behavior is forward looking only. IS continues to be forward and backward looking. Eliminating
backward-looking price-setting behavior excludes
current inflation from the IS equation. The resulting
pattern matrices are

(21)

24

× × × 
× 0 0 
0 0 0






A0 = 0 ×1 × 2  , A1 = 0 ×1 × 2  , A2 = 0 0 0 .
× 0 × 
0 0 0 
0 0 0

8

12

0

4

8

12

One more restriction on A0 is needed to identify the
model. We considered (i) excluding xt from AS and
(ii) imposing an interest elasticity of –2.0 on IS. In
both cases the estimated models were stable in spite
of the policy’s less than one-for-one response of the
funds rate to inflation.
Whether or not monetary policy is stabilizing
depends on policy and private behavior. We found
that over the pre-Volcker-Greenspan era (1959:Q1–
1979:Q3), Fed behavior appears not to be stabilizing
when we impose the Taylor (1999b) restrictions on
aggregate supply. In contrast, when we impose AS
restrictions implied by the dynamic NK model, policy
over the period appears to be stabilizing. In both
cases we estimate that the Fed adjusted the funds
rate less than one-for-one with inflation.

The Disappearance of Money from
Monetary Policy Analyses
oney plays no role in NK models of monetary policy. To some observers this may
seem odd. This section reviews and dis-

M

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

CHART 6
Another Stable Equilibrium in Taylor’s Model (1959:Q1–1979:Q3)
IS

AS

MP

0.6793

0

y

–1.229
0.7022

p
0

–0.4062
1.301

i
0

–0.6531
0

4

8

12

0

4

cusses the reasons for money’s disappearance. The
section then turns to some empirical implications of
reintroducing money.
Why Money Disappeared. Money disappeared
for both practical and theoretical reasons.
Throughout the 1980s, the Federal Reserve paid
fairly close attention to the growth of various monetary aggregates in setting its target for the federal
funds rate. Target growth rates for aggregates were
established and taken seriously by observers of
monetary policy. A decade ago researchers at the
Federal Reserve Board developed the “P-Star”
model, which relied on stable long-run values of
velocity and output growth, to use M2 growth to
predict inflation (Hallman, Porter, and Small 1991).
Although doubts were raised at the time, any hope
of exploiting M2 growth to forecast inflation evaporated when M2 velocity began to behave erratically
in the early 1990s.21 Since then, as a practical move,
the Fed has deemphasized growth rates of aggre-

8

12

0

4

8

12

gates as indicators of inflation. In 2000, the FOMC
formalized this deemphasis, as the minutes from the
June 27–28, 2000, meeting indicate:
In contrast to its earlier practice, the Committee at this meeting did not establish
ranges for growth of money and debt in 2000
and 2001. The legal requirement to set and
announce such ranges recently had expired,
and the members did not view the ranges as
currently serving a useful role in the formulation of monetary policy. Owing to uncertainties about the behavior of the velocities of
money and debt, these ranges had not provided reliable benchmarks for the conduct of
monetary policy for some years. Nevertheless,
the Committee believed that the behavior of
these aggregates retained value for gauging
economic and financial conditions and that
such behavior should continue to be monitored.

21. Christiano (1989) raised some doubts.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

25

Moreover, Committee members emphasized
that they would continue to consider periodically issues related to their long-run strategy for monetary policy, even if they were no
longer setting ranges for the money and debt
aggregates.
Theoretical developments in the past decade reinforce the Fed’s pragmatic response to unstable M2
velocity. Several authors showed that a nominal
anchor need not come from control of a monetary
aggregate: a policy rule that sets the nominal interest
rate can uniquely determine the price level even in a
rational expectations model.22 This contradicted
Sargent and Wallace’s
(1975) famous result
that interest rate rules
cannot determine the
Even if the Fed now
price level.23 These
ignores money, it certainly
developments initiated a literature about
has not always ignored it.
interest rate rules that
Historical interpretations
continues to flourish.24
of policy behavior that
Several considerations
arise from the
ignore money run the risk
absence of money in
of seriously misinterpreting
the analytical framepast policy actions.
work. First, even if the
Fed ignores money
when it sets the funds
rate, this does not
imply that money plays no role in the transmission of
monetary policy or in individuals’ and firms’ consumption, investment, employment, and pricing
decisions. In terms of the NK model, absence of
money from the policy rule does not justify its
absence from the IS and AS relationships. Interest
rates need not be the only channel through which
monetary policy affects economic activity.
Second, the fact that the Fed can ignore money
without losing a nominal anchor does not imply the
Fed does ignore it. The FOMC minutes leave open
the possibility that the Fed may again choose to pay
more attention to monetary aggregates. For example, it is hard to imagine that if M2 growth were to
exceed 20 percent for four consecutive quarters
that there would be no tendency for the FOMC to
adjust its funds rate target in response.
Third, even if the Fed now ignores money, it certainly has not always ignored it. Historical interpretations of policy behavior that ignore money run the
risk of seriously misinterpreting past policy actions.
Finally, if money plays any role at all in the
FOMC’s settings for the funds rate, then money is
likely to enter private sector expectations of future
26

funds rates. Money, therefore, will enter dynamic IS
or AS relationships through the expectations terms,
once expectations are solved out.
Adding Money. We add to the NK model a function that makes the demand for real money balances
(MD) depend on the current nominal interest rate
and current income. To focus on the marginal contribution of adding money to a model with simple
policy rules, we adopt an agnostic view of the
dynamics associated with IS, AS, and MD behavior.
We posit the money demand function
(22)

MD: Mt – pt= α0 + α i i t + α y yt + lags + ε tMD,

where M is a broad monetary aggregate, y is output
(or income), and ε MD is an exogenous disturbance to
the demand for money. We exclude potential output
entirely from MD. Money enters the econometric
models in logged form.
Money is taken to be M2. Clearly, the federal funds
rate is not the opportunity cost of M2. Based on the
large models estimated in Leeper, Sims, and Zha
(1996), modeling the details of the links between the
markets for reserves and broad money complicates
but does not substantively change the analysis. In
addition, Gordon and Leeper (1994) found that correctly accounting for the own rate of return on M2 in
computing the opportunity cost does not appreciably
alter the conclusions that concern us here.
As discussed in the first section, there is the potential for confounding behavior described by IS with that
described by either MD or MP. For the present purposes, we seek to minimize those identification problems by treating ( yt, pt ) as being determined in an
“inertial” sector of the economy. This assumption
treats output and inflation as predetermined for
monetary variables: disturbances to MD and MP
behavior affect y and p with a one-period lag. By
lumping output and price determination into a single
sector, we can no longer claim to have identified
behavioral IS and AS equations; instead, we now have
“x” and “p” equations.
The empirical work in this section contrasts two
assumptions about policy behavior: the conventional
Taylor rule, as given by equation (4), and an even
simpler rule in which the Fed’s choice for the funds
rate depends only on current money growth:25
– ] + ε MP.
(23) MP (M rule): it = γ0 + γm1[(Mt – Mt–1) – µ
t
We have chosen to normalize this rule on the nominal interest rate, but it is equally consistent to imagine this as a rule that determines the supply of
money, where that supply choice is sensitive to the
nominal interest rate.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

We order the equations “x,” “p,” MD, and MP and
the variables x, p, M, and i. Common to both assumptions about policy behavior are the pattern matrices
(24)
× × ×

0 × ×1
A0 = 
0 0 ×1

0 0 ×

×x 
× × × 0 
× × × 0





×p 
×
×
×
×
× × × 0
p
, A1 = 
, A2 = 
,
× × × × M 
× × × 0
×M 





× 
× × × 0 
× × × 0

where either ×x and × p are nonzero with ×M = 0 (conventional Taylor rule) or ×M is nonzero with
×x = ×p = 0 (M rule). Coefficients denoted ×1 reflect
the homogeneity restriction making money demand
the demand for real balances. The specification
removes any dynamics from policy behavior.
We estimate the models from 1959:Q1 to
2000:Q2. For the model with the Taylor rule, estimates of the coefficients in A0 imply
(25)

( Mt − pt )d = − 0.242 it + 0.204 yt
it = 0.873 ( pt − pt −1 ) − 0.045 xt ,

where we suppress the lagged coefficients in
money demand, all constants, and the coefficient
on the output gap in the price equation.26 It is
clear that when the demand for money is appended
to this model with a Taylor rule for policy, the variables can be solved recursively: the first equation
yields xt, which from the second equation implies
pt; together these yield it from the policy rule in
equation (25), and Mt comes from the money
demand equation in equation (25), given the value
p
for exogenous potential output, yt . Because M and
i are not determined simultaneously, estimates of
money demand have no effect on estimates of policy behavior.
The estimated parameters in equation (25)
seem reasonable. The short-run semielasticity of
money demand is negative, and the short-run

income elasticity is positive. In contrast to what
we found when estimating a model with severely
restricted dynamics (see equation [11]), policy
appears to adjust the funds rate less than one-forone with inflation. This difference underscores the
importance of all the model’s identifying assumptions when drawing inferences about policy behavior from estimated policy rules. Model (11)
determines x and i simultaneously through IS
behavior; model (25) determines them recursively
due to inertial behavior. In spite of the estimated
policy behavior, the model is stable.
Chart 7 shows the Taylor rule applied to response
functions over a four-year horizon. Responses to MP
shocks are depicted in the fourth column: a policy
contraction raises the funds rate substantially and
reduces the money stock, generating a liquidity
effect. Output has a strange positive blip in the
quarter after the shock but then declines, following
a hump-shaped path. There is no effect on the price
level. Policy disturbances explain, at most, 13 percent of output, 41 percent of M2, and, in the initial
period, over 80 percent of the funds rate. After four
years, only 40 percent of funds rate variability is due
to MP disturbances.
The endogeneity of policy appears in the first
three panels of the bottom row of the chart. An “x”
shock, which reduces output and the price level,
produces a modest response from policy, while a
“p” shock, which moves output and inflation in
opposite directions, engenders a stronger offsetting reaction. Over 40 percent of funds rate fluctuations at four-year horizons arise from reactions
to “p” shocks.
Policy also responds to money demand disturbances. An MD shock lowers M2 on impact. This is
followed by falling prices and initially lower output;
after about two years, output rises above its initial
level.27 Policy raises the funds rate smoothly, gradually returning it to its preshock level. The Taylor rule
prevents the funds rate from jumping when MD
shocks strike.

22. Authors include McCallum (1981, 1983) and Leeper (1991). Related work falls under the rubric of the “fiscal theory of the
price level” advocated by Sims (1994) and Woodford (1998).
23. Sargent states the result as follows: “There is no interest rate rule that is associated with a determinate price level” (1979,
362). Predecessors to Sargent and Wallace that do not impose rational expectations include Patinkin (1949) and Gurley and
Shaw (1960).
24. Analyses of the price level, inflation, and monetary policy without money are creeping into principles textbooks (see Romer
2000 and Stiglitz and Walsh 2000).
25. In estimation, we annualize the growth rate of money, so 4(Mt – Mt –1) appears in equation (23).
26. All current and lagged coefficients in the output and price equations are identical between the two models with two different policy rules.
27. Textbook analyses typically have positive money-demand shocks lowering the price level. In simulations of the NK model,
however, the pattern depicted in Chart 7 is common.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

27

CHART 7
Model with Money and a Taylor Rule (1959:Q1–2000:Q2)
MD

“p”

“x”

MP

0.1691
0
y

–0.7164
2.0739

p

0
–1.1014
1.5531

M

0

–1.6873
2.1262

i

0
–0.3781
0

4

8

12

0

4

8

12

Estimates of the model with the alternative M
rule in equation (23) yield:28
(26)

( Mt − pt )d = − 1.571 it + 0.554 yt
it = 2.913 ( Mt − Mt −1 ).

The large estimated coefficient on money growth, by
conventional wisdom, implies that policy was stabilizing.29 The model is no longer recursive, as the equations in equation (26) simultaneously determine M
and i. Note that the negative correlation between
money and interest rates, which equation (25) attributes entirely to the interest elasticity of money
demand, now gets split into a stronger negative
demand elasticity and a positive supply elasticity.
Dynamic responses to the shocks in the “inertial”
sector, shown in Chart 8, are similar to those in the
model with a Taylor rule. From the standpoint of
endogenous reactions to the disturbances that have
occupied much of the attention of NK authors, the
two policy rules are nearly indistinguishable. Some
differences show up in the effects of exogenous
28

0

4

8

12

0

4

8

12

shifts in policy: with an M rule a contraction generates a hump-shaped decrease in output and a
smooth decline in the price level.
Three differences between the models are
worth noting. First, comparing Chart 7 and Chart 8,
a monetary contraction under a Taylor rule only
temporarily changes the level of the money stock,
while under an M rule it does so permanently. This
implies that under a Taylor rule, the open market
operation that initially raises the funds rate must
be reversed to bring the money stock back to its
original level. Second, the money stock appears to be
more endogenous under an M rule: at most, 20 percent of the variation in M is attributed to exogenous
MD shocks. With a Taylor rule, over 60 percent of
M fluctuations are due to MD, providing a substantial role to exogenous factors in determining the
money supply.
Finally, we formally test the overidentifying
restrictions in the two models. The model with a
Taylor rule imposes one less restriction than does
the model with an M rule. We obtain

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

CHART 8
Model with Money and a Money Rule (1959:Q1–2000:Q2)
“x”

“p”

MD

MP

0.1865
0
y

–0.7507
1.8317

p
0
–0.6535
0.706

M

0

–0.8796
0.7983

i
0
–0.3895
0

4

8

12

0

4

8

12

Taylor rule
M rule
ξ = 418.3
ξ = 207.4
SC = 112.2
SC = 117.3
AC = 44
AC = 46
p = 0.00
p = 0.00
where SC is the Schwarz criterion and AC is the
Akaike criterion. By any criterion the data reject both
models. The test statistic in the M rule model is substantially less than in the Taylor rule model. These
results suggest that a rule that makes the nominal
interest rate respond to money growth—and nothing
else—certainly fits no worse than a Taylor rule.
Estimates of identical models under two qualitatively different policy rules yield fairly similar
results when judged by system properties like
impulse response functions and stability. Based
solely on estimated policy rules, however, the two
models look very different.

0

4

8

12

0

4

8

12

The Recent Period. Much current research on
Federal Reserve behavior draws sharp distinctions
between the pre-1979 and the post-1979 periods.
Rotemberg and Woodford (1997) focus on the
1980–95 period, Fuhrer and Estrella (1999) consider
breaks in policy occurring in 1979:Q3, 1982:Q3, and
1987:Q2, while Taylor (1999a) estimates his rule
from 1987:Q1 to 1997:Q3. We reestimate the two
models in equations (25) and (26) over the period
1982:Q1–2000:Q2. The NK literature has concluded
that during this period the Fed stabilized the economy
by adjusting the funds rate strongly in response to
inflation; it is also a period in which many authors
believe no harm is done by ignoring money.
Estimates from the model with a Taylor rule are
(27)

( Mt − pt )d = − 0.182 it + 0.303 yt
it = 0.311( pt − pt −1 ) + 0.269 xt .

28. Separate coefficients on Mt and Mt –1 are estimated to be 2.913 and –2.902, so imposing equal and opposite coefficients does
not move the estimates far from the peak of the likelihood function.
29. In the NK model, this coefficient also eliminates indeterminacy of equilibria.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

29

System estimates do not recover a policy response
to inflation that is even close to exceeding 1.0. In
contrast, OLS estimates of the policy rule yield
(28) OLS:

(

)

it = 1.03 pt − pt −1 − 0.13 xt + 0.035,

(0.19)

(0.10) (0.006)

which is consistent with conclusions of earlier authors
that policy was stabilizing. Impulse response functions for the model estimate a small anomalous price
response following an exogenous monetary policy
contraction (not reported).
Estimates of the model with an M rule over the
1982:Q1–2000:Q2 period offer a different interpretation of policy behavior:
(29)

( Mt − pt )d = −4.503 it + 1.442 yt
it = 1.272 ( Mt − Mt −1 ).

With the response of the funds rate to money
growth exceeding 1.0, policy appears to be stabilizing. OLS estimates of the policy rule tell a different tale:
it = 0.344 ( Mt − Mt −1 ) + 0.049.

(0.072)

(0.005)

Responses to MP shocks in this model are not
reported because they look very similar to those in
Chart 8.
This section has presented evidence that the
exclusion of money from NK empirical analyses is
not innocuous. Substantive conclusions about the
role of monetary policy and the behavior of the
Federal Reserve can change when money is reintroduced in a way that generates interactions between
MD and MP behavior. We also demonstrated that in
practice it is difficult to distinguish a monetary policy that adjusts the nominal interest rate in
response to inflation and output from a policy that
adjusts the rate in response to the growth rate of the
money supply. This raises some doubts that either
specification of monetary policy—equation (27) or
equation (29)—identifies policy decision rules.
Instead, they may merely be alternative characterizations of equilibrium policy behavior.
In our results, the model with an M rule looks
more reasonable than the model in which money is
irrelevant to policy choice. These results seem to be
at odds with Ireland (2000) and McCallum (2000).
They show that money plays a quantitatively unimportant role in the class of general equilibrium models that includes NK models. The inconsistency
between their quantitative-theoretic results and our
empirical findings deserves further study.
30

Stability in an Identified VAR Framework
he first section estimated tightly parameterized behavioral relationships with simple policy rules. The third section loosened the
restrictions on dynamics in equations, describing
private behavior while it maintained simple static
policy rules. To complete the progression, this section allows also for freely estimated dynamics in policy behavior, leading to specifications in line with
the approach taken in the identified VAR literature.
We show that when dynamics are left unrestricted,
the models exhibit remarkable stability across subperiods. With the loss of parsimony come increased
sampling error and less precisely estimated parameters. To reduce sampling error we adopt the Bayesian methods developed by Sims and Zha (1998)
and employed by Leeper, Sims, and Zha (1996) and
Leeper and Zha (2001).
This section reports estimates from two weakly
identified VARs. First we revisit the three-variable
system consisting of output, the price level, and the
federal funds rate. Although the system is fairly stable over time, it exhibits the price puzzle that has
received attention in VAR work: an exogenous easing of policy lowers the funds rate, raises output,
and lowers inflation.30 The second model adds money
to the system with two important effects: the price
puzzle disappears and the responses to exogenous
shifts in policy become more stable.
In choosing subperiods we face the usual problem that some “interesting” episodes may be too
short to be informative. With brief time series, sampling error alone can dominate the estimates and
produce misleading inferences. We check stability
by estimating the system over three subperiods that
coincide with ones frequently studied in work on
Fed behavior: 1959:Q1–2000:Q2, 1959: Q1–1979:Q3,
and 1959:Q1–2000:Q2 with 1979: Q4–1982:Q4
excluded. The models are estimated with four unrestricted lags and a constant term in each equation.31
We found that adding potential GDP contributes
little to the interpretation of results in this section,
so we have dispensed with that variable.
Three-Variable Model. As in the third section,
we treat output and inflation as determined in an
inertial sector of the economy. This implies policy
disturbances affect y and p with a one-period lag.
We also take seriously the argument that an operational policy rule must make policy choice depend
on observables. In the three-variable economy,
where y and p are not observed contemporaneously,
an operational rule sets the funds rate as a function
of lagged values of all three series.
Because the VAR coefficients in this model are
not interpretable, we move directly to the impulse

T

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

CHART 9
Three-Variable Identified VAR over Various Subperiods
y

p

MP

1.8739

y
0

–1.4498
2.4039

p

0

–1.9283

0.4112

0

i

–0.9513
0

4

8

12

1959:Q1–2000:Q2

0

4

1959:Q1–1979:Q3

8

12

0

4

8

12

1959:Q1–2000:Q2 excluding 1979:Q4–1982:Q4

Note: The shaded region is a 68 percent error band associated with the model estimated from 1959:Q1 to 1979:Q3.

response functions displayed in Chart 9. In each
panel we report the three point estimates that correspond to the three subperiods and show a shaded
region, which is a 68 percent error band associated
with the model estimated from 1959:Q1 to
1979:Q3.32 When, for the three subperiods, the
responses to a shock fall within the error bands, the
model makes stable predictions of the effects of that
shock. For many policy purposes, this is sufficient
evidence of stability.
Most responses over a four-year horizon fall within
the error bands. Notable exceptions are the response
of output and prices to a policy disturbance: the

impacts of policy appear to weaken as more recent
data are included in the sample. Another difference is
that over the full sample, the funds rate responds
more strongly to a “p” shock. This pattern is consistent with the view that, in the Volcker-Greenspan era,
the Federal Reserve has placed increased emphasis
on stabilizing inflation. Because in the model, both “p”
and “y” shocks move output and prices in the same
direction, they are both consistent with disturbances
that shift aggregate demand. No shock in the model
looks like aggregate supply.
Exogenous monetary expansions have strong and
persistent effects on output. Even after four years,

30. Sims (1992) and Eichenbaum (1992) discuss the price puzzle.
31. In Sims and Zha’s (1998) notation, the tightness of the prior is set as λ0 = 0.5, λ1 = 0.4, λ2 = λ3 = 1.0, λ4 = 0.2, µ5 = 1.0, and µ6 = 5.
32. The error bands are computed from 50,000 draws using procedures developed by Sims and Zha (1999) and Waggoner and
Zha (2000).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

31

output remains well above its preshock level. The
perverse response of the price level, though less
pronounced in recent data, is consistent across subperiods. The stronger price puzzle exhibited in data
up to 1979 conforms to Hanson’s (2000a) findings in
a different system of variables.
Chart 9 exhibits anomalies and enough instability
that we are not comfortable with the identification
of policy in the model. To address our concerns, we
turn to a model with money.
Four-Variable Model. To identify policy behavior in the four-variable model with money, it is crucial
to separate money demand from policy. We estimate
the pattern matrix on contemporaneous variables

(30)

× × × 0 


0 × × 0
A0 = 
,
0 0 × × 


0 0 × × 

where the equations appear in columns in the order
“y,” “p,” MD, MP, and variables appear in rows
ordered y, p, M, i. We impose no additional restrictions on this matrix and no restrictions on lagged
variables. Over the three sample periods, the estimates of A0 are
(31)
1 0.02 − 0.18 0 
1 0.02 − 0.43 0 




0 1 − 1.17 0  59 −00 0 1 − 0.59 0 
A059 −79= 
, A0
=
0 0
0 0
1 − 8.71
1 − 9.93




0 0 1.49 1 
0 0 1.44 1 
1 0.03 − 0.43 0 


0 1 − 1.89 0 
A0no 79 −82 = 
.
0 0
1 13.29


0 0 3.47 1 
In all periods, money demand is estimated to have a
negative interest elasticity and positive price and
income elasticities. Over the entire sample and over
the period up to 1979, the Fed raised the funds rate
in response to higher money growth. When the
1979–82 period is excluded, the policy response to
current money growth changes from positive to negative. It may be tempting to infer that policy behavior changed in important ways. This parameter is
one of many that describes policy behavior in the
VAR. The implications of changes in that parameter
must be gleaned from the entire model.
Chart 10 illustrates the pitfalls of inferring policy
behavior from a single parameter in the policy rule.
Dynamic responses to policy disturbances are
32

remarkably stable. Point estimates from the three
subperiods lie within the 68 percent error bands
derived from the 1959–79 period. There is some evidence in recent years that the output effects of policy
shocks have weakened and the price effects have
strengthened. In addition, much of the variance of
the policy shocks over the entire sample derives from
the 1979–82 period (note the smaller shock when
that period is excluded). There is no evidence of a
price puzzle: point estimates of price responses to a
monetary contraction never rise, though the error
band places some probability on a higher price path.
In contrast to the three-variable system, where
both “p” and “y” shocks look like they shift aggregate demand, we now see distinct AS and AD disturbances. A “y” shock moves output and prices in
the same direction, as one would expect from AD,
while a “p” shock moves them in opposite directions, as would an AD disturbance. By separating
the two kinds of aggregate shocks, the model
allows a richer interpretation of policy behavior
than can be gleaned from the model without
money. When output and prices move together,
policy responds to counteract the output effects.
When output and prices move in opposite directions, policy tries to counteract the price effects.
This pattern of policy responses is consistent with
those found in the simple models with a Taylor rule
(for example, Chart 2), but they appear under very
different identifying assumptions.
Some instability does show up in the model with
money. The price effects of MD disturbances appear
to be much weaker in models using recent data. And
as in the three-variable system, the response of policy to a “p” shock is stronger in recent years, though
now we can interpret the “p” shock as AS.
In contrast to the previous models, the fourvariable VAR has only one overidentifying restriction. When estimated from 1959–79, the data do not
reject the model by any criterion: the test statistic is
ξ = 1.04; SC = 4.43, AC = 2.0, p = .69. Over the full
sample there is more evidence against the model:
ξ = 4.92; SC = 5.12, AC = 2.0, p = .03.
Adding money alters many of the inferences from
an identified VAR. Money appears to stabilize the system across time, it eliminates the anomalous price
puzzle following MP shocks, and it helps to distinguish
aggregate supply from nonmonetary policy aggregate
demand disturbances. The instability of M2 velocity
since the early 1990s, which has motivated some
researchers to eliminate money from their analyses,
does not appear to raise difficulties for identifying
monetary policy behavior. Neither does it interfere
with the stability of predictions about the dynamic
impacts of exogenous shifts in policy.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

CHART 10
Four-Variable Identified VAR over Various Sub-Periods
y

p

MD

MP

0.1839
0

y

–1.8435
2.0386

p
0

–1.2169
1.6007

0

M

–3.0287
0.8189

i
0
–0.4011
0

4

8

12

1959:Q1–2000:Q2

0

4

8

12

0

1959:Q1–1979:Q3

4

8

12

0

4

8

12

1959:Q1–2000:Q2 excluding 1979:Q4–1982:Q4

Note: The shaded region is a 68 percent error band associated with the model estimated from 1959:Q1 to 1979:Q3.

Implications of VAR Estimates. NK analyses
with simple policy rules consistently find that
Federal Reserve behavior has been qualitatively different since 1979. Indeed, many authors attribute
the superior performance of the U.S. economy over
the past decade to superior policymaking. Central to
this conclusion is that estimates of simple rules display substantial instability across time. We find no
such instability in loosely identified VAR models
with money. The contrast in our findings raises the
possibility that some authors have overinterpreted
the apparently shifting parameters in simple policy
rules. The VAR literature, which does not attempt to
reduce all policy behavior to two parameters, leads
one to doubt the NK conclusions about policy.33

Views about the price puzzle in VARs have been
influenced by Sims’s (1992) argument. The Fed bases
its choices on more information than small VARs contain, Sims argues, so what appears in a VAR to be an
exogenous policy move is actually a response to extramodel information about aggregate supply disturbances. If this behavior is systematic, it can create a
pattern of lower funds rates being followed by higher
output and lower inflation. That view led to expanding
VARs to include commodity prices, which serve as an
“information variable” about supply developments,
and thus diminishes or eliminates the price puzzle.
Hanson (2000a) questions the commodity price
fix for the price puzzle. He shows that with Christiano, Eichenbaum, and Evans’s (1999) recursive

33. Evidence from estimates of policy “reaction functions” supports the VAR findings. For example, Sims (1999) estimates a regimeswitching equation describing the Fed’s behavior. Although he finds that parameters describing systematic policy responses to
the economy seem to shift across regimes, allowing for such shifts contributes little to the overall fit of the equation.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

33

identification scheme, allowing the Fed to respond to
current information in commodity prices does not
resolve the price puzzle in data before 1979.34 We cannot discuss these arguments in detail here. We are
sympathetic to Hanson’s view that the role of commodity prices in policy behavior is poorly understood
and that they seem like a weak reed on which to rest
identification of policy. We also believe that the role of
commodity prices in helping to identify policy behavior has been overemphasized. In the four-variable
identified VAR system, exogenous policy contractions
never exhibit a price puzzle: the price level smoothly
and strongly declines in all sample periods. These
results obtain without the contrivance of commodity
prices. Conditioning on commodity prices is neither
necessary nor sufficient for resolving the price puzzle.

Conclusion
ew Keynesian research offers little advice on
how, at the frequency of FOMC meetings,
the Fed should behave. Few authors suggest
the FOMC should mechanically obey the simple rule
assumed in the theory. The most detailed discussion
of the practical application of a simple rule comes
from Taylor (1993a), who suggests that policymakers use it to compare recent FOMC decisions to the
rule. And forecasts could include those of the funds
rate using the rule. This analysis, Taylor suggests,
could include a range of forecasts corresponding to
different coefficients in the rule. This suggestion is
close to how Leeper and Zha (2001) use an identified VAR to conduct counterfactual policy analysis.
Jansson and Vredin (2000) propose blending the
two approaches. From the standpoint of a practical
policy analyst, therefore, the two approaches could
be applied in similar ways.
Applying the estimated NK models to the kind of
policy analysis Taylor suggests leads to a quandary.
In all the NK models we estimated, the inflation
effects of policy disturbances—or deviations from
the estimated policy rule—are minimal. By extension, changes in policy parameters, if private decision makers view them as temporary, will also have
trivial impacts on inflation. It is not at all clear what
monetary policy can do to stabilize inflation in the
estimated models.
We introduced the paper by noting that policy analysts face tough choices. Our results do nothing to

N

make those choices easier. But an analyst who wishes
to base policy advice on a stylized model and a simple
policy rule should be aware of the pitfalls of doing
so. While the stories are compelling, they also appear
to be fragile. The trade-off between simplicity and
robustness is an unpleasant reality of policy analysis.
To be sure, our analysis did not exploit all the
structure embodied in the canonical IS-AS-MP
New Keynesian model. Cross-equation restrictions
implied by dynamic behavior may help resolve some
of the identification problems we highlighted. On
the other hand, experience suggests that those
dynamic restrictions are precisely the ones most
at odds with data.
It is a mistake to regard this paper as running a
horse race between stylized models with simple
rules and identified VARs with complex dynamics
and loose behavioral interpretations. Each has its
place in policy advising. For a model to help inform
policy choice, though, its identifying assumptions
should be robust and its fit—both in-sample and
out-of-sample—should be respectable.
We have argued, and demonstrated in several
ways, that it is treacherous to draw inferences about
policy effects solely from policy rules estimated in
isolation from a complete macro model. System estimates of policy parameters can differ substantially
from single-equation estimates. And system properties do not align well with values of particular policy
parameters.
A central theme of the NK literature is that the
Fed’s performance in the Volcker-Greenspan era is
far superior to the Fed’s performance in earlier periods. This dramatic conclusion is based on the following: policy parameters have changed across two
subperiods; NK models predict that policy in the
recent period produces more stable economic outcomes. We find that from the perspective of system
estimation, instability of policy rules does not appear
to be a serious concern. Even if particular policy
parameters are unstable, when the dynamics of
behavior are well modeled, the equilibrium effects
of policy are quite stable. And it’s the equilibrium
effects that concern policymakers.
At a minimum our results argue forcefully that the
bold NK conclusion—that U.S. monetary policy has
improved dramatically in the past twenty years—
deserves more careful scrutiny.

34. The price puzzle is not mere VAR esoterica. Taking Hanson’s findings as background, Barth and Ramey (2000) propose that the
price puzzle actually is no puzzle. Through the “cost channel,” a monetary contraction reduces “working capital” and impacts
both aggregate demand and aggregate supply. Under certain elasticities, the equilibrium price level should rise after a contraction. They offer industry-level support for the view that monetary contractions reduce output and raise price-wage ratios.

34

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Fourth Quarter 2001

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