Frederic S Mishkin: Estimating potential output
Remarks by Mr Frederic S Mishkin, Member of the Board of Governors of the US Federal Reserve
System, at the Conference on Price Measurement for Monetary Policy, Federal Reserve Bank of
Dallas, Dallas, 24 May 2007.
* * *
This conference focuses on measurement issues, and in my remarks I want to focus on one of the
most important measurement issues that we at the Federal Reserve and other central banks face:
How do we determine whether the economy is operating above or below its maximum sustainable
level? That is, how do we estimate the path of potential output?1
The Federal Reserve operates under a dual mandate to achieve both price stability and maximum
sustainable employment. In that context, it is natural to think of potential output as the level of output
that is consistent with the maximum sustainable level of employment: That is, it is the level of output at
which demand and supply in the aggregate economy are balanced so that, all else being equal,
inflation tends to gravitate to its long-run expected value.
The combination of the dual mandate and this definition suggests two reasons that estimating the path
of potential output is so central to the conduct of monetary policy. First, to evaluate whether our
policies will help achieve the maximum sustainable employment objective of the dual mandate, we
need know the level of future output that would be consistent with that objective. Second, the level of
output relative to potential output, which is referred to as the output gap, plays an important role in the
inflation process. When the actual level of output is above potential output - so that the output gap is
positive - labor and product markets are excessively tight; then, if things such as expected inflation
and temporary supply factors are held constant, inflation will tend to rise. Conversely, when the output
gap is negative and labor and product markets are slack, inflation will tend to fall. Estimates of the
future path of potential output are therefore needed to assess whether the projected path of output
that is implied by current monetary policy will lead inflation to move in a direction that is consistent with
price stability.
Because estimates of potential output are an important part of central bankers' toolkits, the Federal
Reserve and other central banks devote considerable resources to getting the best measures of
potential output possible. In this talk, I want to explore something that Bismarck warned us we
shouldn't want to examine: "what goes into the sausage" - or in this case, what goes into central
bankers' thinking about how to estimate potential output.
Broadly speaking, there are three basic approaches to estimating potential output: (1) aggregate
approaches; (2) production function, or growth-accounting, approaches; and (3) the newest kid on the
block, dynamic stochastic general equilibrium (DSGE) approaches. Let's look at each of these in turn,
with the major focus on the production function approach, one to which we at the Federal Reserve
currently pay a lot of attention.
(Please note that my comments here reflect my own views and not necessarily those of the Board of
Governors or the Federal Reserve System.)
Aggregate approaches
Aggregate approaches to estimating potential output can be thought of as top down approaches
because they look at relationships involving aggregate variables and use them to derive measures of
potential output. For example, one way of estimating potential output is to assume that if a change in
employment or output is sustainable, then it is likely to be permanent. This assumption suggests using
univariate statistical methods to identify the permanent component of changes in output, which could
then be viewed as a reasonable measure of potential output. Examples of such an approach include
the work of Beveridge and Nelson (1981) and Clark (1987). Although univariate approaches to
measuring potential output have the advantage of simplicity and can provide a feel for what potential
output might be, they suffer from several disadvantages. First, they require a variety of statistical
assumptions about which economic theory provides little guidance - for example, about the correlation
between permanent and transitory components or whether the permanent component should be
modeled as a random walk. Perfectly sensible alternative assumptions can lead to very different
estimates of potential output. Second, these purely statistical approaches do not tell us whether this
measure of the permanent component of output movements provides information about the most
important aspect of potential output from a central banker's perspective - namely, its association with
a stable rate of inflation.
In measuring potential output, we therefore need to bring in some economics. One potentially valuable
economic relationship we can use is the "natural rate" version of the Phillips curve, which followed
from the seminal research of Nobel prize winners Milton Friedman (1968) and Edmund (Ned) Phelps
(1967). Friedman and Phelps demonstrated that there should not be a long-run tradeoff between
inflation and unemployment and that the economy will gravitate to some natural rate of unemployment
in the long run no matter what the rate of inflation is. In other words, the long-run Phillips curve is
vertical, and attempts to lower unemployment below the natural rate will only result in higher inflation.
According to the natural-rate hypothesis, there is a natural rate of unemployment - also more
commonly referred to as the NAIRU (non-accelerating inflation rate of unemployment) - at which
inflation tends to gravitate to its long-run expected value.2 A natural rate of output - that is, potential
output - corresponds to the NAIRU. The difference between actual and potential output, the output
gap, tells us whether inflation will tend to move up or down, holding things like inflation expectations,
energy prices, import prices, and so forth constant. The natural-rate hypothesis thus suggests that
potential output can be estimated from a multivariate approach in which potential output is an
unobserved component in the relationship between inflation and the output gap. Kuttner (1994)
provides a good example of this approach.3
An alternative approach involves deriving the NAIRU directly from estimates of Phillips curves and
then using Okun's law - which relates the output gap to the unemployment gap (the actual
unemployment rate minus the NAIRU) - to estimate potential output.4 These multivariate approaches
are reasonably simple and make intuitive sense, but they also have serious drawbacks. First, they
require that the specification of the Phillips curve is correct. For example, the model needs to correctly
characterize the relationship between the unemployment rate gap and inflation dynamics while taking
into account how inflation expectations are formed, and it should not leave out any other variables that
have an impact on inflation.5 Indeed, many economists criticize the Phillips curve, with some even
declaring it dead.6
Second, using Okun's law to derive potential output requires an appropriate specification for the
dynamics of the relationship between output and unemployment gaps. However, cyclical fluctuations
in productivity and labor supply can complicate this relationship. Moreover, Okun's law can be thrown
well off course during periods of unanticipated structural change in the economy, such as the early
1970s, when U.S. productivity growth slowed. As a result of these influences, Okun's law has not
2
The term "NAIRU" comes from a paper by Nobel prize winner Franco Modigliani and Lucas Papademos, now the vice
president of the European Central Bank (Modigliani and Papademos, 1978). Although NAIRU and the natural rate of
unemployment are frequently used interchangeably, there is a subtle distinction between the two. The natural rate of
unemployment is the rate at which inflation would tend to gravitate to its long-run expected value, while NAIRU, as originally
defined, is the unemployment rate at which inflation will have no tendency to move up or down. Depending on what shocks
are hitting the economy, the NAIRU could deviate from the natural rate of unemployment (for example, see Estrella and
Mishkin, 1999). Because the NAIRU terminology is more common than the natural rate of unemployment terminology, I am
using the NAIRU terminology even though I think that the natural rate of unemployment terminology is more accurate.
3
Other examples of the multivariate approach include Apel and Jansson (1999), Cochrane (1994), and Dupasquier, Guay
and St-Amant (1999).
4
Okun's law was originally specified as the relationship between real GDP growth and changes in the unemployment rate
(see Okun, 1962).
5
See Gordon (1982 and many subsequent papers) for a description of the "triangle" model of inflation. The three sides of the
triangle are inflation inertia (captured by the lags of inflation), excess demand (measured by the unemployment rate gap or
GDP gap), and supply shocks - such as the relative prices of imports, food, and energy. Following George Perry's (1970)
early work, it is common to adjust the NAIRU for changes in the composition of the labor force. Fallick, Fleischman, and
Pingle (2006) estimate that shifts in the demographic composition of the labor force can explain a decline in the
unemployment rate of nearly 1 percentage point between 1977 and 2006.
6
See, for example, Atkeson and Ohanian (2001).
BIS Review 53/2007
always been a reliable guide to the relationship between the unemployment gap and the output gap
and thus has not always been the most useful guide for estimating potential output.7 Finally, even if
the Phillips curve and the Okun's law relationships are specified correctly - a big if - the statistical
uncertainty about the estimates of the NAIRU, and therefore also about potential output, is very large -
certainly larger than a policymaker would like. For example, the estimates of Staiger, Stock, and
Watson (1997a and b) of the 95 percent confidence interval for the NAIRU were as much as 3
percentage points wide. Thus estimates of the NAIRU, in isolation, provide policymakers with little
real-time insight for assessing the effect of labor markets on inflation pressures.
Production function (growth-accounting) approaches
Because of the shortcomings of the aggregate approaches described above, some researchers
estimate potential output using a production function approach that generates an estimate of potential
from the underlying factors of production. This approach is sometimes referred to as "growth
accounting" because, after the log of a production function is differentiated, output growth can be
expressed as a weighted average of the growth of factor inputs - that is, capital services and labor
input (hours worked and labor quality) - and a residual - multifactor (also called total factor)
productivity growth. The NAIRU concept still plays an important role in this approach because it helps
to determine the level of potential output.
A major advantage of the growth-accounting approach is that it focuses on the various factors that
drive growth in potential output, rather than simply on the historical behavior of output growth or on the
historical relationship between output and labor inputs as in Okun's law. The disaggregated nature of
the growth-accounting approach means that more data can be used to estimate potential output.
These additional data are likely to be particularly valuable when the economy is undergoing major
structural changes - for example, the productivity slowdown starting around the early 1970s, the surge
and subsequent slowdown in population growth from the baby-boom generation's entry and (now) exit
from the labor force, the remarkable upsurge in labor participation of females in the 1970s and 1980s,
and the pickup in productivity growth starting in the second half of the 1990s. Given these advantages,
it is not surprising that the growth-accounting framework is widely used to estimate the path of
potential output, both by central banks such as the Federal Reserve and by academic researchers.
In its simplest formulation, the growth-accounting framework characterizes output growth as the sum
of the growth rate of raw labor hours and the growth rate of output per hour, that is, labor productivity.8
In turn, the growth rate of labor hours is described as the sum of population growth (civilian
noninstitutional population aged sixteen and older), the rate of change of the labor force participation rate,
the rate of change in the employment rate, and the rate of change in the number of hours in the
average workweek. Labor productivity is decomposed into the contributions of capital deepening (the
marginal product of capital - typically estimated as capital's share of income - times the growth rate of
capital services per hour), changes in labor quality, and the growth rate of multifactor productivity.
To obtain estimates of potential output growth, researchers can use economic analysis to estimate the
individual components above that go into the growth-accounting framework. Much research has been
conducted along these lines in the Federal Reserve System and elsewhere. For example, Aaronson
and others (2006) examine what variables influence individual decisions to participate in the labor
market (birth cohort, age, sex, number and age of children, and so forth). They then use this
information together with the relative size of different cohorts to show that the aging of the baby-boom
cohort can explain much of the decline in labor force participation since 2000 and why the participation
rate is likely to continue to decline further in the future. Given slower projected population growth and
what appears to be a downward trend in the number of hours in the average workweek, these results
suggest that potential output growth will be slower than it otherwise would have been. Of course, such
projections are subject to uncertainty, and economists hold a range of views about the prospects for
future labor force growth. One important unknown is whether increases in longevity and better health
will boost the labor force participation rates of older individuals more than currently embedded in these
projections.
Similarly, economists at the Fed have been at the forefront of research on why labor productivity
growth ratcheted upward in the second half of the 1990s and continued at a surprisingly robust pace
over the first half of this decade. Oliner, Sichel, and Stiroh (2007), for example, find that the IT
(information technology) sector has been a key element in the higher productivity growth that we have
been experiencing since the mid-1990s.9 But they also find that the sources of growth in the second
half of the 1990s were quite different than in the period after 2000. In the 1995-2000 period, labor
productivity growth was driven by substantial gains in multifactor productivity in the tech sector, which,
in turn, led to sharp reductions in the prices of high-tech equipment and stimulated investment in this
type of capital in other sectors of the economy. Since 2000, high productivity growth has apparently
been driven importantly by industry restructuring in response to pressures on profits (the firms that
saw the sharpest drops in profits were those that had the largest gains in labor productivity) and by a
reallocation of material and labor inputs across industries. Their estimate for the current trend labor
productivity growth is centered around 2-1/4 percent, but they find large uncertainty around this
estimate, with a 95 percent confidence interval that ranges from 1-1/4 percent to 3-1/4 percent.
Roberts (2001) estimates even larger uncertainty around structural labor productivity growth.
Despite the advantages of the growth-accounting framework, it still presents some difficulties. For one,
there is, as I just highlighted, a large degree of uncertainty surrounding the estimates of the
components that go into the growth-accounting formulas. In addition, the growth-accounting
framework requires a substantial amount of data, some of which are not especially reliable. For
example, it is especially difficult to measure the growth rate of capital services, and the Bureau of
Labor Statistics releases its initial estimates with a lag of at least one year. In addition, the bureau's
two measures of employment, one derived from a survey of firms and the other from a survey of
households, often provide very different pictures of what is happening in the labor market. Also data
for capital services, labor composition, and multifactor productivity are not readily available for all
sectors of the U.S. economy.
Because it is so difficult to reliably estimate potential output using either the aggregate or the
growthaccounting approach, it should come as no surprise that we at the Federal Reserve use a lot of
judgment in constructing our estimates of potential output. In particular, we see judgment as playing
three important roles in our procedures. First, it enables us to take account of the effects of structural
changes in the economy that cannot be modeled directly. Second, it allows us to deal with model
misspecifications that cannot be corrected. Third, we can use judgment to correct for measurement
errors or inconsistencies in economic data. For example, we judgmentally adjust model-based
estimates of the NAIRU to account for movements in the unemployment rate unrelated to changes in
labor market slack. Of these, the most important has been the shift in the demographic composition of
the labor force, driven largely by the entrance and subsequent maturation of the baby-boom
generation. But economists have pointed to a number of other factors that would influence the NAIRU
as well.
Another example relates to the way in which we estimate the trend growth rate of multifactor
productivity. In particular, estimates of trend multi-factor productivity growth tend to be sensitive to the
choice of modeling strategy, a problem that became particularly apparent during the "jobless recovery"
of the early 1990s, when the normal relationship between output growth and employment growth
appeared to break down. Statistical filtering models like those described by Roberts (2001) tended in
real time to attribute much of the weak employment growth to an acceleration in trend productivity.
Later on, however, after employment recovered, it became clear that the unusual behavior of
employment during that period was explained better as a temporary reluctance by firms to hire than as
a step-up in the rate of trend productivity growth. In a similar vein, it may at times make sense to down
weight more-recent estimates of the data used in filtering exercises to account for the possibility of
future revisions to the data.
Finally, it can often be useful to look at Okun's law as a check on the estimates of potential output
derived from the growth-accounting approach. Although, as I noted above, one should not expect
Okun's law to hold from quarter to quarter, the relationship is relatively robust over longer periods, and
a persistent deviation in the unemployment rate from that predicted by Okun's law might call into
question the estimated trends in one or more of the components in potential output.
Dynamic stochastic general equilibrium approaches
The real business cycle literature, which started with the work of Nobel Prize winners Finn Kydland
and Edward Prescott (1982), features optimizing agents and emphasizes the role of technology
shocks in explaining both economic growth and business cycles. Dynamic stochastic general
equilibrium (DSGE) models contain many features of the earlier real business cycle literature, but,
because they allow for rigidities and imperfections in markets, they are often referred to as New
Keynesian models. The New Keynesian DSGE models provide more-realistic, yet still theoretically
elegant, representations of the economy, and their development has been an exciting area of research
in macroeconomics in recent years.
New Keynesian DSGE models provide a somewhat different, but complementary, perspective on the
definition of potential output than the one I outlined at the beginning of this speech. In particular, we
might think of potential output as the level of output that an economy could attain if the inefficiencies
resulting from nominal wage and price rigidities were removed - that is, if wages and prices were fully
flexible.10 The definition of potential output as a flexible price equilibrium has much in common with the
more conventional definition I discussed earlier because over time prices (and wages) do gravitate
toward their equilibrium levels. As a result, the DSGE definition accords with the idea that potential
output is the level of output at which inflation tends neither to rise nor fall.
That said, the DSGE view of potential output also has important differences with the earlier
approaches to estimating potential output. Although research on using DSGE models to estimate
potential output is in its infancy and so should be read cautiously, papers such as Neiss and Nelson
(2005) and Edge, Kiley, and Laforte (2007) are finding that the properties of potential output and
output gap fluctuations can be quite different from conventional measures. For example, in many
DSGE models, potential output can undergo swings over the business cycle, a result that should not
be surprising considering that the early real business cycle models viewed the business cycle as being
primarily an efficient response to shocks to the economy. In addition, fiscal policy shocks, changes in
households' preferences with regard to saving and consumption, changes in preferences about leisure
that affect labor supply, and terms-of-trade shocks can all cause potential output to fluctuate. In
contrast, growth-accounting approaches to estimating potential output generally assume that such
shocks have no important effects on potential output at business-cycle frequencies. As a
consequence, their estimates typically have smaller fluctuations than measures of potential output
derived from DSGE models, and thus the output gaps in the current generation of DSGE models tend
to be less variable than conventional measures and can be quite different for particular periods.
Although the research on DSGE models is promising, measures of potential output and the output gap
from these models are controversial. The DSGE measures of potential output are far more model
dependent than more-conventional measures because they depend on the estimated parameters of
the model and on the model's estimates of the structural shocks hitting the economy. As a result,
DSGE models with different characterizations of the economy's underlying structure can produce
substantially different estimates of potential output. This is apparent, for example, in the large
differences between the potential output measures in the DSGE models of Neiss and Nelson (2005)
from those in Edge, Kiley, and Laforte (2007). Moreover, DSGE models often require strong
assumptions to identify the shocks to potential output from model equation residuals. The finding that
these models imply smaller and less persistent output gaps than traditional models may simply reflect
the fact that inefficiencies other than price rigidities, such as real wage rigidities, are important for
output fluctuations.11 As a result, some policymakers have been quite critical of the implication of
DSGE models that a substantial fraction of business-cycle fluctuations are efficient and so do not
require a response from monetary policymakers.12
Implications for policy
Now that we have looked inside the sausage of estimating potential output, I hope you have not lost
your appetite for thinking about what these measurement issues mean for monetary policy. As I
indicated earlier, considerable uncertainty surrounds the measures of potential output derived from
any of the approaches I have discussed. In addition, there is also what economists call Knightian
uncertainty (named after the famous University of Chicago economist Frank Knight) - the fact that we
are not even sure of the appropriate modeling approach to measure potential output. Adding even
more to the uncertainty of potential output measures are (1) that the observable data do not always
correspond to the data we would like to have to produce measures of potential output and (2) that
initial estimates of observable data can subsequently be revised substantially, resulting in a very
different picture of what is happening to potential output and the output gap. Orphanides (2001) points
out that output gaps were grossly mismeasured in the 1970s, in part because the initially published
data did not reflect the true state of the economy.13
Where does the high uncertainty about actual and potential output leave us at central banks? Does it
mean that we should abandon our focus on potential output and output gaps in making decisions on
monetary policy? I think not. For better or worse, we cannot escape the need for information on output
gaps so that we can forecast the future path of inflation and evaluate the current setting of our
monetary policy instruments. However, we also need to recognize that because measures of potential
output and output gaps are so uncertain, we must always be aware that they might be providing
misleading signals as to the future course of inflation and the appropriateness of the stance of policy.
In assessing whether there is slack in the economy, we at central banks look not only at our estimates
of output gaps but also at a wide range of indicators drawn from the labor, product, and financial
markets to provide us with a perspective on the balance of supply and demand in the economy. Most
important, the substantial uncertainty in our measures of potential output implies that we need to be
cautious about taking on board the implications of our current estimates of the output gap. For
example, if inflation is moving in a different direction than the output gap would suggest, then we
should take seriously the possibility that our output gap measure is not providing us with reliable
information.
The bottom line is that we must never take our eye off of the inflation ball. Good policymaking requires
that we acknowledge what we are unsure about, and this requirement applies particularly to measures
of potential output.
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