For release on delivery
11:30 a.m. EDT (8:30 a.m. PDT)
May 3, 2019
Models, Markets, and Monetary Policy
Remarks by
Richard H. Clarida
Vice Chair
Board of Governors of the Federal Reserve System
at
"Strategies for Monetary Policy,"
a Hoover Institution Monetary Policy Conference
Stanford University
Stanford, California
May 3, 2019
It is an honor and a privilege to participate once again in this annual Hoover
Institution Monetary Policy Conference. The topic of this year's conference, "Strategies
for Monetary Policy," is especially timely. As you know, the Federal Reserve System is
conducting a review of the strategy, tools, and communication practices we deploy to
pursue our dual-mandate goals of maximum employment and price stability. In this
review, we expect to benefit from the insights and perspectives that are presented today,
as well as those offered at other conferences devoted to this topic, as we assess possible
practical ways in which we might refine our existing monetary policy framework to
better achieve our dual-mandate goals on a sustained basis.
My talk today will not, however, be devoted to a broad review of the Fed's
monetary policy framework-that process is ongoing, and I would not want to prejudge
the outcome-but it will instead focus on some of the important ways in which economic
models and financial market signals help me think about conducting monetary policy in
practice after a career of thinking about it in theory.1
The Role of Monetary Policy
Let me set the scene with a very brief-and certainly selective-review of the
evolution over the past several decades of professional thinking about monetary policy. I
will begin with Milton Friedman's landmark 1967 American Economic Association
presidential address, "The Role of Monetary Policy."2 This article is, of course, most
famous for its message that there is no long-run, exploitable tradeoff between inflation
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and unemployment. And in this paper, Friedman introduced the concept of the "natural
rate of unemployment," which today we call u*.3 What is less widely appreciated is that
Friedman's article also contains a concise but insightful discussion of Wicksell's "natural
rate of interest"-r* in today's terminology-the real interest rate consistent with price
stability. But while u* and r* provide key reference points in Friedman's framework for
assessing how far an economy may be from its long-run equilibrium in labor and
financial markets, they play absolutely no role in the monetary policy rule he advocates:
his well-known k-percent rule that central banks should aim for and deliver a constant
rate of growth of a monetary aggregate. This simple rule, he believed, could deliver
long-run price stability without requiring the central bank to take a stand on, model, or
estimate either r* or u*. Although he acknowledged that shocks would push u away
from u* (and, implicitly, r away from r*), Friedman felt the role of monetary policy was
to operate with a simple quantity rule that did not itself introduce potential instability into
the process by which an economy on its own would converge to u* and r*.4 In
Friedman's policy framework, u* and r* are economic destinations, not policy rule
inputs.
Of course, I do not need to elaborate for this audience that the history of k-percent
rules is that they were rarely tried, and when they were tried in the 1970s and the 1980s,
they were found to work much better in theory than in practice.5 Velocity relationships
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proved to be empirically unstable, and there was often only a very loose connection
between the growth rate of the monetary base-which the central bank could control-
and the growth rate of the broader monetary aggregates, which are more tightly linked to
economic activity. Moreover, the macroeconomic priority in the 1980s in the United
States, the United Kingdom, and other major countries was to do "whatever it takes" to
break the back of inflation and to restore the credibility squandered by central banks that
had been unable or unwilling to provide a nominal anchor after the collapse of the
Bretton Woods system.
By the early 1990s, the back of inflation had been broken (thank you, Paul
Volcker), conditions for price stability had been achieved (thank you, Alan Greenspan),
and the time was right for something to fill the vacuum in central bank practice left by the
realization that monetary aggregate targeting was not, in practice, a workable monetary
policy framework. Although it was mostly unspoken, there was a growing sense at the
time that a simple, systematic framework for central bank practice was needed to ensure
that the hard-won gains from breaking the back of inflation were not given away by
short-sighted, discretionary monetary experiments that were poorly executed, such as had
been the case in the 1970s.
Policy Rate Rules
That vacuum, of course, was filled by John Taylor in his classic 1993 paper,
"Discretion vs. Policy Rules in Practice." Again, for this audience, I will not need to
remind you of the enormous impact this single paper had not only on the field of
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monetary economics, but also-and more importantly-on the practice of monetary
policy. For our purposes today, I will note that the crucial insight of John's paper was
that, whereas a central bank could pick the "k" in a "k-percent" rule on its own, without
any reference to the underlying parameters of the economy (including r* and u*), a well-
designed rule for setting a short-term interest rate as a policy instrument should, John
argued, respect several requirements.6 First, the rule should anchor the nominal policy
rate at a level equal to the sum of its estimate of the neutral real interest rate (r*) and the
inflation target. Second, to achieve this nominal anchor, the central bank should be
prepared to raise the nominal policy rate by more than one-for-one when inflation
exceeds target (the Taylor principle). And, third, the central bank should lean against the
wind when output-or, via an Okun's law relationship, the unemployment rate-deviates
from its estimate of potential (u*).
In other words, whereas in Friedman's k-percent policy rule u* and r* are
destinations irrelevant to the choice of k, in the Taylor rule-and most subsequent
Taylor-type rules-u* and r* are necessary inputs. As Woodford (2003) demonstrates
theoretically, the first two requirements for a Taylor-type rule are necessary for it to be
consistent with the objective of price stability. The third requirement-that monetary
policy lean against the wind in response to an output or unemployment gap-not only
contributes to the objective of price stability, but is also obviously desirable from the
perspective of a central bank like the Fed that has a dual mandate. The Taylor approach
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to instrument-rule specification has been found to produce good macroeconomic
outcomes across a wide range of macroeconomic models. Moreover, in a broad class of
both closed and open economy dynamic stochastic general equilibrium, or DSGE,
models, Taylor-type rules can be shown to be optimal given the underlying micro
foundations of these models.
In original formulations of Taylor-type rules, r* was treated as constant and set
equal to 2 percent, and potential output was set equal to the Congressional Budget Office
(CBO) estimates of potential output, or, in specifications using the unemployment rate as
the activity variable, u* was set equal to the CBO's estimate of the natural unemployment
rate. These assumptions were reasonable at the time, and I myself wrote a number of
papers with coauthors in the years before the Global Financial Crisis that incorporated
them.7
A Dive into Data Dependence
Fast-forward to today. At each Federal Open Market Committee (FOMC)
meeting, my colleagues and I consult potential policy rate paths implied by a number of
policy rules, as we assess what adjustments, if any, may be required for the stance of
monetary policy to achieve and maintain our dual-mandate objectives.8 A presentation
and discussion of several of these rules has been included in the semiannual Monetary
Policy Report to the Congress since July 2017.9 One thing I have come to appreciate is
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that, as I assess the benefits and costs of alternative policy scenarios based on a set of
policy rules and economic projections, it is important to recognize up front that key
inputs to this assessment, including u* and r*, are unobservable and must be inferred
from data via models.10 I would now like to discuss how I incorporate such
considerations into thinking about how to choose among monetary policy alternatives.
A monetary policy strategy must find a way to combine incoming data and a
model of the economy with a healthy dose of judgment-and humility!-to formulate,
and then communicate, a path for the policy rate most consistent with the central bank's
objectives. There are two distinct ways in which I think that the path for the federal
funds rate should be data dependent.11 Monetary policy should be data dependent in the
sense that incoming data reveal at any point in time where the economy is relative to the
ultimate objectives of price stability and maximum employment. This information on
where the economy is relative to the goals of monetary policy is an important input into
interest rate feedback rules-after all, they have to feed back on something. Data
dependence in this sense is well understood, as it is of the type implied by a large family
of policy rules, including Taylor-type rules discussed earlier, in which the parameters of
the economy needed to formulate such rules are taken as known.
But, of course, key parameters needed to formulate such rules, including u* and
r*, are unknown. As a result, in the real world, monetary policy should be-and in the
United States, I believe, is-data dependent in a second sense: Policymakers should and
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do study incoming data and use models to extract signals that enable them to update and
improve estimates of r* and u*. As indicated in the Summary of Economic Projections,
FOMC participants have, over the past seven years, repeatedly revised down their
estimates of both u* and r* as unemployment fell and real interest rates remained well
below prior estimates of neutral without the rise in inflation those earlier estimates would
have predicted (figures 1 and 2). And these revisions to u* and r* appeared to have had
an important influence on the path for the policy rate actually implemented in recent
years. One could interpret any changes in the conduct of policy as a shift in the central
bank's reaction function. But in my view, when such changes result from revised
estimates of u* or r*, they merely reflect an updating of an existing reaction function.
In addition to u* and r*, another important input into any monetary policy
assessment is the state of inflation expectations. Since the late 1990s, inflation
expectations appear to have been stable and are often said to be "well anchored."
However, inflation expectations are not directly observable; they must be inferred from
models, other macroeconomic information, market prices, and surveys. Longer-term
inflation expectations that are anchored materially above or below the 2 percent inflation
objective present a risk to price stability. For this reason, policymakers should and do
study incoming data to extract signals that can be used to update and improve estimates
of expected inflation. In many theoretical rational expectations models, expected
inflation is anchored at the target level by assumption. From a risk-management
perspective, it makes sense, I believe, to regularly test this assumption against empirical
evidence.
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Financial Markets and Monetary Policy-Extracting Signal from Noise
Because the true model of the economy is unknown, either because the structure
is unknown or because the parameters of a known structure are evolving, I believe
policymakers should consult a number and variety of sources of information about
neutral real interest rates and expected inflation, to name just two key macroeconomic
variables. Because macroeconomic models of r* and long-term inflation expectations are
potentially misspecified, seeking out other sources of information that are not derived
from the same models can be especially useful. To be sure, financial market signals are
inevitably noisy, and day-to-day movements in asset prices are unlikely to tell us much
about the cyclical or structural position of the economy.12 However, persistent shifts in
financial market conditions can be informative, and signals derived from financial market
data-along with surveys of households, firms, and market participants, data, as well as
outside forecasts-can be an important complement to estimates obtained from
historically estimated and calibrated macroeconomic models.13
12
Uncertainty regarding r*, u*, and long-term inflation expectations is not the only source of uncertainty
that has implications for monetary policy. Edge, Laubach, and Williams (2005) show how the duration of a
productivity shock can affect even the direction of the best monetary policy response. Erceg and others
(2018) find that even in conditions of substantial output gap uncertainty and uncertainty about the slope of
the Phillips curve, a notable response to the estimated output gap in a Taylor-type rule is generally
beneficial. And Eusepi and Preston (2018) show that replacing model-consistent expectations with forms
of adaptive learning means that some, but not all, of the key results regarding best conduct in monetary
policy under full information carry through.
13
Like many others, I believe that monetary policy should respond to financial market fluctuations when
they have material implications for our outlook for employment and inflation, but monetary policy should
not generally target asset prices themselves.
The Federal Reserve uses survey data and conducts surveys of its own on a range of macroeconomic and
financial conditions. Among the surveys the Fed conducts are the Senior Loan Officer Opinion Survey on
Bank Lending Practices and the Senior Credit Officer Opinion Survey on Dealer Financing Terms. In
addition, the staff at the Federal Reserve Board uses disaggregated and high-frequency data to estimate the
state of the economy in real time. Such data include disaggregated labor market data from ADP and data
on expenditures from credit card transactions.
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Interest rate futures and interest rate swaps markets provide one source of high-
frequency information about the path and destination for the federal funds rate expected
by market participants (figure 3). Interest rate option markets, under certain assumptions,
can offer insights about the entire ex ante probability distribution of policy rate outcomes
for calendar dates near or far into the future (figure 4). And, indeed, when one reads that
a future policy decision by the Fed or any central bank is "fully priced in," this is usually
based on a "straight read" of futures and options prices. But these signals from interest
rate derivatives markets are only a pure measure of the expected policy rate path under
the assumption of a zero risk premium. For this reason, it is useful to compare policy rate
paths derived from market prices with the path obtained from surveys of market
participants, which, while subject to measurement error, should not be contaminated with
a term premium. Market- and survey-based estimates of the policy rate path are often
highly correlated. But when there is a divergence between the path or destination for the
policy rate implied by the surveys and a straight read of interest rate derivatives prices, I
place at least as much weight on the survey evidence (for example, derived from the
surveys of primary dealers and market participants conducted by the Federal Reserve
Bank of New York) as I do on the estimates obtained from market prices (figure 3).
The Treasury yield curve can provide another source of information about the
expected path and ultimate longer-run destination of the policy rate. But, again, the yield
curve, like the interest rate futures strip, reflects not only expectations of the path of
short-term interest rates, but also liquidity and term premium factors Thus, to extract
signal about policy from noise in the yield curve, a term structure model is required. But
different term structure models can and do produce different estimates of the expected
path for policy and thus the term premium. Moreover, fluctuations in the term premium
on U.S. Treasury yields are driven in part by a significant "global" factor, which
complicates efforts to treat the slope of the yield curve as a sufficient statistic for the
expected path of U.S. monetary policy (Clarida, 2018c). Again, here, surveys of market
participants can provide useful information-for example, about "the expected average
federal funds rate over the next 10 years," which provides an alternative way to identify
the term premium component in the U.S. Treasury curve.
Quotes from the Treasury Inflation-Protected Securities (TIPS) market can
provide valuable information about two key inputs to monetary policy analysis: long-run
r* and expected inflation.14 Direct reads of TIPS spot rates and forward rates are signals
of the levels of real interest rates that investors expect at various horizons, and they can
be used to complement model-based estimates of r*. In addition, TIPS market data,
together with nominal Treasury yields, can be used to construct measures of "breakeven
inflation" or inflation compensation that provide a noisy signal of market expectations of
future inflation. But, again, a straight read of breakeven inflation needs to be augmented
with a model to filter out the liquidity and risk premium components that place a wedge
between inflation compensation and expected inflation.
As is the case with the yield curve and interest rate futures, it is useful to compare
estimates of expected inflation derived from breakeven inflation data with estimates of
expected inflation obtained from surveys-for example, the expected inflation over the
next 5 to 10 years from the University of Michigan Surveys of Consumers (figure 5).
Market- and survey-based estimates of expected inflation are correlated, but, again, when
there is a divergence between the two, I place at least as much weight on the survey
evidence as on the market-derived estimates.
The examples I have mentioned illustrate the important point that, in practice,
there is not typically a clean distinction between "model-based" and "market-based"
inference of key economic variables such as r* and expected inflation. The reason is that
market prices reflect not only market expectations, but also risk and liquidity premiums
that need to be filtered out to recover the object of interest-for example, expected
inflation or long-run r*. This filtering almost always requires a model of some sort, so
even market-based estimates of key inputs to monetary policy are, to some extent, model
dependent.
Implications for Monetary Policy
Let me now draw together some implications of the approach to models, markets,
and monetary policy I have laid out in these remarks. Macroeconomic models are, of
course, an essential tool for monetary policy analysis, but the structure of the economy
evolves, and the policy framework must be-and I believe, at the Federal Reserve, is-
nimble enough to respect this evolution. While financial market signals can and
sometimes do provide a reality check on the predictions of "a model gone astray," market
prices are, at best, noisy signals of the macroeconomic variables of interest, and the
process of filtering out the noise itself requires a model-and good judgment. Survey
estimates of the long-run destination for key monetary policy inputs can-and, at the Fed,
do-complement the predictions from macro models and market prices (figure 6).15 Yes,
the Fed's job would be (much) easier if the real world of 2019 satisfied the requirements
to run Friedman's k-percent policy rule, but it does not and has not for at least 50 years,
and our policy framework must and does reflect this reality.
This reality includes the fact that the U.S. economy is in a very good place. The
unemployment rate is at a 50-year low, real wages are rising in line with productivity,
inflationary pressures are muted, and expected inflation is stable. Moreover, the federal
funds rate is now in the range of estimates of its longer-run neutral level, and the
unemployment rate is not far below many estimates of u*. Plugging these estimates into
a 1993 Taylor rule produces a federal funds rate very close to our current target range for
the policy rate.16 So with the economy operating at or very close to the Fed's dual-
mandate objectives and with the policy rate in the range of FOMC participants' estimates
of neutral, we can, I believe, afford to be data dependent-in both senses of the term as I
have discussed-as we assess what, if any, further adjustments in our policy stance might
be required to maintain our dual-mandate objectives of maximum employment and price
stability.
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Figure 1. Assessments of the Longer-Run Normal Unemployment Rate
from the Summary of Economic Projections
Note: Calculated from the final Summary of Economic Projections in each calendar year (November for 2011; December for other years).
The value shown is the median of the individual Federal Open Market Committee participant projections for the fourth quarter of each calendar
year. (For 2014, the value is the midpoint of the central tendency, as the median is not publicly available.)
Source: Federal Reserve Board, Summary of Economic Projections for 2011 to 2013
(https://www.federalreserve.gov/monetarypolicy/fomc_historical.htm) and for 2014 to 2018
(https://www.federalreserve.gov/monetarypolicy/fomccalendars.htm).
Figure 2. Assessments of the Longer-Run Inflation-Adjusted Federal
Funds Rate from the Summary of Economic Projections
Note: The value shown consists of the median of individual Federal Open Market Committee (FOMC) participant assessments of the
longerrun nominal federal funds rate or midpoint of the target range, as given in the December Summary of Economic Projections for the indicated
year, minus the FOMC's inflation objective of 2 percent. (For each of the years shown, FOMC participants' projections for the longer-run
inflation rate also equaled 2 percent.)
Source: See the source note of figure 1.
Figure 3. Projections of the Federal Funds Rate Path Implied by
Surveys and Market Quotes
Note: The path with zero term premium is estimated using overnight index swap quotes with a spline approach and a term premium of 0
basis points.
Source: Bloomberg Finance LP; Federal Reserve Board staff estimates; Wolters Kluwer, Blue Chip Financial Forecasts; Federal Reserve
Bank of New York, Survey of Primary Dealers and Survey of Market Participants.
Figure 4. Market-Implied Probability Distribution of the Federal Funds
Rate for Year-End 2019
Percentage Range
Note: Estimated from federal funds futures options (not adjusted for risk premiums). Probabilities are zero for values above 2.75 percent.
Source: CME Group; Federal Reserve staff estimates.
Figure 5. Market- and Survey-Based Measures of Longer-Run Inflation
Expectations
Note: Estimates based on smoothed nominal and inflation-indexed Treasury yield curves. Michigan survey expectations represent median responses.
TIPS is Treasury Inflation-Protected Securities; CPI is consumer price index.
* Adjusted for lagged indexation of Treasury Inflation-Protected Securities (the carry effect).
Source: Federal Reserve Bank of New York; Federal Reserve Board staff calculations; Bloomberg Finance LP; University of Michigan, Surveys of
Consumers, http://new.sca.isr.umich.edu.
Figure 6. Measures of Longer-Run Federal Funds Rate Expectati