HIGH-FREQUENCY IDENTIFICATION OF MONETARY

NON-NEUTRALITY: THE INFORMATION EFFECT∗

EMI NAKAMURA AND JÓN STEINSSON

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We present estimates of monetary non-neutrality based on evidence from high-
frequency responses of real interest rates, expected inflation, and expected output
growth. Our identifying assumption is that unexpected changes in interest rates in
a 30-minute window surrounding scheduled Federal Reserve announcements arise
from news about monetary policy. In response to an interest rate hike, nominal
and real interest rates increase roughly one-for-one, several years out into the
term structure, while the response of expected inflation is small. At the same time,
forecasts about output growth also increase—the opposite of what standard models
imply about a monetary tightening. To explain these facts, we build a model in
which Fed announcements affect beliefs not only about monetary policy but also
about other economic fundamentals. Our model implies that these information
effects play an important role in the overall causal effect of monetary policy shocks
on output. JEL Codes: E30, E40, E50.

I. INTRODUCTION
A central question in macroeconomics is how monetary pol-
icy affects the economy. The key empirical challenge in answering
this question is that most changes in interest rates happen for a
reason. For example, the Fed might lower interest rates to coun-
teract the effects of an adverse shock to the financial sector. In this
case, the effect of the Fed’s actions are confounded by the finan-
cial shock, making it difficult to identify the effects of monetary

∗ We thank Miguel Acosta, Matthieu Bellon, Vlad Bouchouev, Nicolas Crouzet,

Stephane Dupraz, Michele Fornino, Jesse Garret, and Shaowen Luo, for excel-
lent research assistance. We thank Michael Abrahams, Tobias Adrian, Richard K.
Crump, Matthias Fleckenstein, Michael Fleming, Mark Gertler, Refet Gürkaynak,
Peter Karadi, Hanno Lustig, Emanuel Moench, and Eric Swanson for generously
sharing data and programs with us. We thank Robert Barro, Marco Bassetto,
Gabriel Chodorow-Reich, Stephane Dupraz, Gauti Eggertsson, Mark Gertler, Refet
Gürkaynak, Samuel Hanson, Sophocles Mavroeidis, Emanuel Moench, Serena Ng,
Roberto Rigobon, Christina Romer, David Romer, Christoph Rothe, Eric Swanson,
Ivan Werning, Michael Woodford, Jonathan Wright, and seminar participants at
various institutions for valuable comments and discussions. We thank the Na-
tional Science Foundation (grant SES-1056107), the Alfred P. Sloan Foundation,
and the Columbia Business School Dean’s Office Summer Research Assistance
Program for financial support.


C The Author(s) 2018. Published by Oxford University Press on behalf of the
President and Fellows of Harvard College. All rights reserved. For Permissions, please
email: journals.permissions@oup.com
The Quarterly Journal of Economics (2018), 1283–1330. doi:10.1093/qje/qjy004.
Advance Access publication on January 29, 2018.

1283
1284 THE QUARTERLY JOURNAL OF ECONOMICS

policy. The most common approach to overcoming this endogeneity

problem is to try to control for confounding variables. This is the
approach to identification in vector autoregression (VAR) studies

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such as Christiano, Eichenbaum, and Evans (1999) and Bernanke,
Boivin, and Eliasz (2005), and in the work of Romer and Romer
(2004). The worry with this approach is that despite efforts to con-
trol for important confounding variables, some endogeneity bias
remains (see, e.g., Rudebusch 1998).
An alternative approach—the one we pursue in this article—
is to focus on movements in bond prices in a narrow window
around scheduled Federal Open Market Committee (FOMC) meet-
ings. This high-frequency identification approach was pioneered
by Cook and Hahn (1989), Kuttner (2001), and Cochrane and
Piazzesi (2002). It exploits the fact that a disproportionate amount
of monetary news is revealed at the time of the eight regularly
scheduled FOMC meetings each year. The lumpy way monetary
news is revealed allows for a discontinuity-based identification
scheme.
We construct monetary shocks using unexpected changes in
interest rates over a 30-minute window surrounding scheduled
Federal Reserve announcements. All information that is public at
the beginning of the 30-minute window is already incorporated
into financial markets and therefore does not show up as spuri-
ous variation in the monetary shock. Such spurious variation is
an important concern in VARs. For example, Cochrane and Pi-
azzesi (2002) show that VAR methods (even using monthly data)
interpret the sharp drop in interest rates in September 2001 as a
monetary shock as opposed to a reaction to the terrorist attacks
on 9/11/2001.
A major strength of the high-frequency identification ap-
proach we use is how cleanly it is able to address the endogeneity
concern. As is often the case, this comes at the cost of reduced sta-
tistical power. The monetary shocks we estimate are quite small
(they have a standard deviation of only about 5 basis points). This
“power problem” precludes us from directly estimating their effect
on future output. Intuitively, output several quarters in the future
is influenced by myriad other shocks, rendering the signal-to-noise
ratio in such regressions too small to yield reliable inference.
We can, however, measure the response of variables that re-
spond contemporaneously, such as financial variables and sur-
vey expectations. Since the late 1990s it has been possible to ob-
serve the response of real interest rates via the Treasury Inflation
 MONETARY NON-NEUTRALITY 1285

Protected Securities (TIPS) market. This is important because the

link between nominal interest rates and real interest rates is the
distinguishing feature of models in which monetary policy affects

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real outcomes. All models—neoclassical and New Keynesian—
imply that real interest rates affect output. However, New
Keynesian and neoclassical models differ sharply as to whether
monetary policy actions can have persistent effects on real interest
rates. In New Keynesian models, they do, whereas in neoclassi-
cal models real interest rates are decoupled from monetary policy.
By focusing on the effects of monetary policy shocks on real in-
terest rates, we are shedding light on the core empirical issue in
monetary economics.
We use the term structure of interest rates at the time of
FOMC meetings to show that the monetary shocks we identify
have large and persistent effects on expected real interest rates
as measured by TIPS. Nominal and real interest rates respond
roughly one-for-one several years out into the term structure in
response to our monetary shocks. The effect on real rates peaks
at around 2 years and then falls monotonically to 0 at 10 years.
In sharp contrast, the response of break-even inflation (the dif-
ference between nominal and real rates from TIPS) is essentially
zero at horizons up to three years. At longer horizons, the re-
sponse of break-even inflation becomes modestly but significantly
negative. A tightening of monetary policy therefore eventually re-
duces inflation—as standard theory would predict. However, the
response is small and occurs only after a long lag.
What can we conclude from these facts? Under the conven-
tional interpretation of monetary shocks, these facts imply a great
deal of monetary non-neutrality. Intuitively, a monetary policy-
induced increase in real interest rates leads to a drop in output
relative to potential, which in turn leads to a drop in inflation.
The response of inflation relative to the change in the real inter-
est rates is determined by the slope of the Phillips curve (as well
as the intertemporal elasticity of substitution). If the inflation re-
sponse is small relative to the change in the real rates, the slope of
the Phillips curve must be small, implying large nominal and real
rigidities and therefore large amounts of monetary non-neutrality.
There is, however, an additional empirical fact that does not
fit this interpretation. We document that in response to an un-
expected increase in the real interest rate (a monetary tight-
ening), survey estimates of expected output growth rise. Under
the conventional interpretation of monetary shocks, tightening
1286 THE QUARTERLY JOURNAL OF ECONOMICS

policy should lead to a fall in output growth. Our empirical find-

ing regarding output growth expectations is therefore the oppo-
site direction from what one would expect from the conventional

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interpretation of monetary shocks.
A natural interpretation of this evidence is that FOMC an-
nouncements lead the private sector to update its beliefs not only
about the future path of monetary policy but also about other eco-
nomic fundamentals. For example, when the Fed chair announces
that the economy is strong enough to withstand higher interest
rates, market participants may react by reconsidering their own
beliefs about the economy. Market participants may contemplate
that perhaps the Fed has formed a more optimistic assessment of
the economic outlook than they have and that they may want to
reconsider their own assessments. Following Romer and Romer
(2000), we refer to the effect of FOMC announcements on private
sector views of non-monetary economic fundamentals as “Fed in-
formation effects.”
The Fed information effect calls for more sophisticated mod-
eling of the effects of monetary shocks than is standard. The main
challenge is how to parsimoniously model these information ef-
fects. We present a new model in which monetary shocks affect
not only the trajectory of the real interest rate, but also private
sector beliefs about the trajectory of the natural rate of interest.
This is a natural way of modeling the information content of Fed
announcements since optimal monetary policy calls for interest
rates to track the natural rate in simple models. Because the Fed
is attempting to track the natural rate, it is natural to assume
that Fed announcements contain information about the path of
the natural rate.
This is important in interpreting the response of real interest
rates and inflation to monetary shocks that we estimate. The rea-
son is that the response of inflation is determined by the response
of the real interest rate gap—the gap between the response of real
interest rates and the natural real rate—which may be smaller
than the response of real interest rates themselves. If there is an
information effect, some of the increase of real rates is interpreted
not as a tightening of policy relative to the natural rate—which
would push inflation down—but as an increase in the natural rate
itself—which does not.
In the extreme case where most of the changes in real in-
terest rates at the time of FOMC announcements are due to a
Fed information effect, even a large response of real interest rates
 MONETARY NON-NEUTRALITY 1287

to a monetary shock is consistent with the conventional channel

of monetary non-neutrality being modest (since the real interest
rate movement is mostly due to a change in the natural real rate).

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However, this does not mean that the Fed is powerless. On the
contrary, if the Fed information effect is large, the Fed has a great
deal of power over private sector beliefs about economic funda-
mentals, which may in turn have large effects on economic activ-
ity. If a Fed tightening makes the private sector more optimistic
about the future, this may raise current consumption and invest-
ment. Depending on the strength of the Fed information effect,
our evidence therefore suggests either that the Fed has a great
deal of power over the economy through traditional channels or
that the Fed has a great deal of power over the economy through
nontraditional information channels (or some combination of the
two).
To assess the extent of Fed information and the nature of
Fed power over the economy, we estimate our Fed information
model using as target moments the responses of real interest
rates, expected inflation, and expected output growth discussed
above. Here, we follow in the tradition of earlier quantitative work
such as Rotemberg and Woodford (1997) and Christiano, Eichen-
baum, and Evans (2005), with two important differences. First,
our empirical targets are identified using high-frequency identifi-
cation as opposed to a VAR. Second, we allow for Fed information
effects in our model.
Our estimates provide strong support for both the Fed infor-
mation effect and more conventional channels of monetary non-
neutrality. Roughly two-thirds of the response of real interest
rates to FOMC announcements are estimated to be a response
of the natural rate of interest and one-third is a tightening of
real rates relative to the natural rate. This large estimate of the
Fed information effect allows us to simultaneously match the fact
that beliefs about output growth rise following a monetary shock
and inflation eventually falls. Beliefs about output growth rise
because agents are more optimistic about the path for potential
output. Inflation falls because a portion of the shock is interpreted
as rates rising relative to the natural rate. Our estimates of the
conventional effect of monetary policy – while smaller than they
would be ignoring the Fed information effect – still imply that the
Phillips curve is very flat.
Once we allow for Fed information effects, the causal effect
of monetary policy is much more subtle to define. Our estimates
1288 THE QUARTERLY JOURNAL OF ECONOMICS

imply that surprise FOMC monetary tightenings have large posi-

tive effects on expectations about output growth. Does this imply
that the monetary announcements cause output to increase by

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large amounts? Not necessarily. Much of the news the Fed reveals
about non-monetary fundamentals would have eventually been
revealed through other sources. To correctly assess the causal ef-
fect of monetary policy, one must compare versus a counterfactual
in which the changes in fundamentals the Fed reveals informa-
tion about occur even in the absence of the announcement. The
causal effect of the Fed information is then limited to the effect
on output of the Fed announcing this information earlier than it
otherwise would have become known.
Our model makes these channels precise. Recent discussions
of monetary policy have noted the Fed’s reluctance to lower inter-
est rates for fear it might engender pessimistic expectations that
would fight against its goal of stimulating the economy. Our anal-
ysis suggests that these concerns may be well founded at least
at the zero lower bound.1 Moreover, our model suggests that the
implications of systematic monetary policy actions are quite dif-
ferent from those of monetary shocks. Monetary shocks are likely
to entail particularly large information effects because they are
the component of monetary policy that surprise the private sector.
In contrast, systematic monetary policy actions don’t entail infor-
mation effects because, by definition, they are not based on private
information. This implies that the systematic component of mon-
etary policy is likely to yield a more conventionally Keynesian
response of the economy than monetary shocks. The information
content of a surprise policy change may also depend on how the
Fed communicates its motivation for the policy change.
Our measure of monetary shocks is based not only on surprise
changes in the Federal Funds rate but also on changes in the path
of future interest rates in response to FOMC announcements. This
is important because over the past 15 years forward guidance has
become an increasingly important tool in the conduct of monetary
policy (Gürkaynak, Sack, and Swanson 2005). This also implies
that it is important to focus on a narrow 30-minute window as op-
posed to the one-day or two-day windows more commonly used in

1. Revealing information about natural rates, even bad news, is likely to be

welfare improving as long as the Fed can vary interest rates to track the natural
rate. At the zero lower bound, the Fed however loses its ability to track the natural
rate. Withholding bad news may then be optimal.
 MONETARY NON-NEUTRALITY 1289

prior work. We make use of Rigobon’s (2003) heteroskedasticity-

based estimator to show that OLS results based on monetary
shocks constructed from longer-term interest rate changes over

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one-day windows around FOMC announcements are confounded
by substantial “background noise” that lead to unreliable infer-
ence and in particular can massively overstate the true statistical
precision of the estimates. In contrast, OLS yields reliable results
when a 30-minute window is used.
An important question about our empirical estimates is
whether some of the effects of our monetary shocks on longer-
term real interest rates reflect changes in risk premia as opposed
to changes in expected future short-term real interest rates. We
use three main approaches to analyze this issue: direct survey
expectations of real interest rates, an affine term structure model,
and an analysis of mean reversion. None of these pieces of evidence
suggest that movements in risk premia at the time of FOMC an-
nouncements play an important role in our results. In other words,
our results suggest that the expectations hypothesis of the term
structure is a good approximation in response to our monetary
shocks, even though it is not a good approximation uncondition-
ally. This is what we need for our analysis to be valid.
Another important (and related) question is whether there
might be a predictable component of the monetary shocks we an-
alyze and how this might affect the interpretation of our results.
In our analysis of real interest rates, the dependent variables
are high-frequency changes. The error terms in these regressions,
therefore, only contain information revealed in that narrow win-
dow, and the identifying assumption is that our monetary shock
is orthogonal to this limited amount of information. This method-
ology has the advantage that we need not assume that our mone-
tary shock is orthogonal to macro shocks occurring on other days
or to slow-moving confounding variables. The identifying assump-
tions are stronger when we analyze the effects of our monetary
shocks on survey expectations from the Blue Chip data. In that
analysis, the dependent variable is a monthly change and the
identifying assumption is that the monetary shock is orthogo-
nal to confounders over the whole month. Similar (stronger) as-
sumptions are required when high-frequency monetary shocks
are used as external instruments in a VAR—as in Gertler and
Karadi (2015)—since the outcome variables are changes over sev-
eral months. In addition, predictability is difficult to establish
convincingly because of data mining and peso problem concerns.
1290 THE QUARTERLY JOURNAL OF ECONOMICS

Our article relates to several strands of the literature in

monetary economics. The seminal empirical paper on Fed in-
formation is Romer and Romer (2000). Faust, Swanson, and

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Wright (2004) present a critique of their findings. More recently,
Campbell et al. (2012) show that an unexpected tightening leads
survey expectations of unemployment to fall. The theoretical lit-
erature on the signaling effects of monetary policy is large. Early
contributions include Cukierman and Meltzer (1986) and
Ellingsen and Söderström (2001). Recent contributions include
Berkelmans (2011), Melosi (2017), Tang (2015), Frankel and
Kartik (forthcoming), and Andrade et al. (2016). The prior lit-
erature typically assumes that the central bank must commu-
nicate only through its actions (e.g., changes in the Fed funds
rate), whereas we allow the Fed to communicate through its words
(FOMC statements).
Our estimates of the effects of monetary announcements on
real interest rates using high-frequency identification are related
to recent work by Hanson and Stein (2015) and Gertler and Karadi
(2015). We make different identifying assumptions than Hanson
and Stein, use a different definition of the monetary shock, and
come to quite different conclusions about the long-run effects of
monetary policy.2 There are also important methodological differ-
ences between our work and that of Gertler and Karadi (2015).
They rely on a VAR to estimate the dynamic effects of monetary
policy shocks. They are subject to the usual concern that the VAR
they use may not accurately describe the dynamic response of
key variables to a monetary shock. Our identification approach
is entirely VAR-free. Our article is also related to several re-
cent papers that have used high-frequency identification to study
the effects of unconventional monetary policy during the recent
period over which short-term nominal interest rates have been
at their zero lower bound (Gagnon et al. 2010; Krishnamurthy
and Vissing-Jørgensen 2011; Rosa 2012; Wright 2012; Gilchrist,
Lopez-Salido, and Zakrajsek 2015).
The article proceeds as follows. Section II describes the data
we use in our analysis. Section III presents our empirical results
regarding the response of nominal and real interest rates and

2. In earlier work, Beechey and Wright (2009) analyze the effect of unexpected
movements in the Fed funds rate at the time of FOMC announcements on nominal
and real 5-year and 10-year yields and the 5- to 10-year forward over the period
February 2004 to June 2008. Their results are similar to ours for the 5-year and
10-year yields.
 MONETARY NON-NEUTRALITY 1291

TIPS break-even inflation to monetary policy shocks. Section IV

presents our empirical evidence on output growth expectations.
Section V presents our Fed information model, describes our esti-

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mation methods, and presents the results of our estimation of the
Fed information model. Section VI discusses how to think about
the causal effect of the monetary announcement in the face of Fed
information. Section VII concludes.

II. DATA
To construct our measure of monetary shocks, we use tick-by-
tick data on Fed funds futures and eurodollar futures from the
CME Group (owner of the Chicago Board of Trade and Chicago
Mercantile Exchange). These data can be used to estimate changes
in expectations about the Fed funds rate at different horizons after
an FOMC announcement (see Online Appendix A). The tick-by-
tick data we have for Fed funds futures and eurodollar futures is
for the sample period 1995–2012. For the period since 2012 we use
data on changes in the prices of the same five interest rate futures
over the same 30-minute windows around FOMC announcements
that Refet Gürkaynak graciously shared with us.
We obtain the dates and times of FOMC meetings up to 2004
from the appendix to Gürkaynak, Sack, and Swanson (2005).
We obtain the dates of the remaining FOMC meetings from the
Federal Reserve Board website at http://www.federalreserve.gov/
monetarypolicy/fomccalendars.htm. For the latter period, we ver-
ified the exact times of the FOMC announcements using the first
news article about the FOMC announcement on Bloomberg. We
cross-referenced these dates and times with data we obtained from
Refet Gürkaynak and in a few cases used the time stamp from his
database.
To measure the effects of our monetary shocks on interest
rates, we use several daily interest rate series. To measure move-
ments in Treasuries at horizons of one year or more, we use daily
data on zero-coupon nominal Treasury yields and instantaneous
forward rates constructed by Gürkaynak, Sack, and Swanson
(2007). These data are available on the Fed’s website at http://
www.federalreserve.gov/pubs/feds/2006/200628/200628abs.html.
We also use the yields on three-month and six-month Treasury
bills. We retrieve these from the Federal Reserve Board’s H.15
data release.
To measure movements in real interest rates, we use zero-
coupon yields and instantaneous forward rates constructed by
1292 THE QUARTERLY JOURNAL OF ECONOMICS

Gürkaynak, Sack, and Wright (2010) using data from the TIPS
market. These data are available on the Fed’s website at http://
www.federalreserve.gov/pubs/feds/2008/200805/200805abs.html.

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TIPS are “inflation protected” because the coupon and principal
payments are multiplied by the ratio of the reference CPI on the
date of maturity to the reference CPI on the date of issue.3 The
reference CPI for a given month is a moving average of the CPI
two and three months prior to that month, to allow for the fact
that the Bureau of Labor Statistics publishes these data with
a lag.
TIPS were first issued in 1997 and were initially sold at ma-
turities of 5, 10, and 30 years, but only the 10-year bonds have
been issued systematically throughout the sample period. Other
maturities have been issued more sporadically. Although liquidity
in the TIPS market was initially poor, TIPS now represent a sub-
stantial fraction of outstanding Treasury securities. We start our
analysis in 2000 to avoid relying on data from the period when
TIPS liquidity was limited. For two- and three-year yields and
forwards we start our analysis in 2004. Gürkaynak, Sack, and
Wright (2010) only report zero-coupon yields for these maturities
from 2004 onward. One reason is that to accurately estimate zero-
coupon yields at this maturity it is necessary to wait until longer
maturity TIPS issued several years earlier have maturities in this
range. To facilitate direct comparisons between nominal and real
interest rates, we restrict our sample period for the corresponding
two- and three-year nominal yields and forwards to the same time
period.
To measure expectations, we use data on expectations of
future nominal interest rates, inflation and output growth from
the Blue Chip Economic Indicators. Blue Chip carries out a survey
during the first few days of every month soliciting forecasts of
these variables for up to the next eight quarters. We use the
mean forecast for each variable. We also use data on Green Book
forecasts from the Philadelphia Fed. These data are hosted and
maintained on the data set at https://www.philadelphiafed.org/
research-and-data/real-time-center/greenbook-data/philadelphia-
data-set. We use the real GDP growth variable from this data set.
To assess the role of risk premia, we use a daily decomposition
of nominal and real interest rate movements into risk-neutral

3. This holds unless cumulative inflation is negative, in which case no adjust-

ment is made for the principal payment.
 MONETARY NON-NEUTRALITY 1293

expected future rates and risk premia obtained from Abrahams

et al. (2015). To assess the robustness of our results regarding
the response of real interest rates we use daily data on inflation

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swaps from Bloomberg. Finally, we estimate the response of stock
prices to monetary announcements using daily data on the level
of the S&P500 stock price index obtained from Yahoo Finance.

III. RESPONSE OF INTEREST RATES AND EXPECTED INFLATION

Our goal in this section is to identify the effect of the mon-
etary policy news contained in scheduled FOMC announcements
on nominal and real interest rates of different maturities. Specif-
ically, we estimate

(1) st = α + γ it + t ,

where st is the change in an outcome variable of interest (e.g., the

yield on a five-year zero-coupon Treasury bond), it is a measure
of the monetary policy news revealed in the FOMC announcement,
 t is an error term, and α and γ are parameters. The parameter of
interest is γ , which measures the effect of the FOMC announce-
ment on st relative to its effect on the policy indicator it .
To identify a pure monetary policy shock, we consider the
change in our policy indicator (it ) in a 30-minute window around
scheduled FOMC announcements.4 The idea is that changes in the
policy indicator in these 30-minute windows are dominated by the
information about future monetary policy contained in the FOMC
announcement. Under the assumption that this is true, we can
simply estimate equation (1) by OLS. We also present results for
a heteroskedasticity-based estimation approach (Rigobon 2003;
Rigobon and Sack 2004) which is based on a weaker identifying
assumption to verify that our baseline identifying assumption is
reasonable. In our baseline analysis, we focus only on scheduled
FOMC announcements, since unscheduled meetings may occur in
reaction to other contemporaneous shocks.
The policy indicator we use is a composite measure of changes
in interest rates at different maturities spanning the first year of
the term structure. Until recently, most authors used unantici-
pated changes in the Fed funds rate (or closely related changes in

4. Specifically, we calculate the monetary shock using a 30-minute window

from 10 minutes before the FOMC announcement to 20 minutes after it.
1294 THE QUARTERLY JOURNAL OF ECONOMICS

very short-term interest rates) as their policy indicator. The key

advantage of our measure is that it captures the effects of “for-
ward guidance.” Forward guidance refers to announcements by

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the Fed that convey information about future changes in the Fed
funds rate. Over the past 15 years, the Federal Reserve has made
greater and greater use of such forward guidance. In fact, changes
in the Fed funds rate have often been largely anticipated by mar-
kets once they occur. Gürkaynak, Sack, and Swanson (2005) con-
vincingly argue that unanticipated changes in the Fed funds rate
capture only a small fraction of the monetary policy news associ-
ated with FOMC announcements in recent years (see also Camp-
bell et al. 2012).
The specific composite measure we use as our policy indicator
is the first principal component of the unanticipated change over
the 30-minute windows discussed above in the following five in-
terest rates: the Fed funds rate immediately following the FOMC
meeting, the expected Fed funds rate immediately following the
next FOMC meeting, and expected three-month eurodollar inter-
est rates at horizons of two, three, and four quarters. We refer
to this policy indicator as the “policy news shock.” We use data
on Fed funds futures and eurodollar futures to measure changes
in market expectations about future interest rates at the time
of FOMC announcements. The scale of the policy news shock
is arbitrary. For convenience, we rescale it such that its effect
on the one-year nominal Treasury yield is equal to one. Online
Appendix A provides details about the construction of the policy
news shock.5

III.A. Baseline Estimates

Table I presents our baseline estimates of monetary shocks
on nominal and real interest rates and break-even inflation. Each
estimate in the table comes from a separate OLS regression of
the form discussed above—equation (1). In each case the indepen-
dent variable is the policy news shock measured over a 30-minute

5. Our policy news shock variable is closely related to the “path factor” consid-
ered by Gürkaynak, Sack, and Swanson (2005). The five interest rate futures that
we use to construct our policy news shock are the same five futures as Gürkaynak,
Sack, and Swanson (2005) use. They motivate the choice of these particular futures
by liquidity considerations. They advocate the use of two principal components to
characterize the monetary policy news at the time of FOMC announcements—a
“target factor” and a “path factor.” We focus on a single factor for simplicity. See
also Barakchian and Crowe (2013).
 MONETARY NON-NEUTRALITY 1295

TABLE I
RESPONSE OF INTEREST RATES AND INFLATION TO THE POLICY NEWS SHOCK

Nominal Real Inflation

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3M Treasury yield 0.67
(0.14)
6M Treasury yield 0.85
(0.11)
1Y Treasury yield 1.00
(0.14)
2Y Treasury yield 1.10 1.06 0.04
(0.33) (0.24) (0.18)
3Y Treasury yield 1.06 1.02 0.04
(0.36) (0.25) (0.17)
5Y Treasury yield 0.73 0.64 0.09
(0.20) (0.15) (0.11)
10Y Treasury yield 0.38 0.44 −0.06
(0.17) (0.13) (0.08)
2Y Treasury inst. forward rate 1.14 0.99 0.15
(0.46) (0.29) (0.23)
3Y Treasury inst. forward rate 0.82 0.88 −0.06
(0.43) (0.32) (0.15)
5Y Treasury inst. forward rate 0.26 0.47 −0.21
(0.19) (0.17) (0.08)
10Y Treasury inst. forward rate −0.08 0.12 −0.20
(0.18) (0.12) (0.09)

Notes. Each estimate comes from a separate OLS regression. The dependent variable in each regression
is the one-day change in the variable stated in the left-most column. The independent variable is a change
in the policy news shock over a 30-minute window around the time of FOMC announcements. The sample
period is all regularly scheduled FOMC meetings from 1/1/2000 to 3/19/2014, except that we drop July 2008
through June 2009. For two-year and three-year yields and real forwards, the sample starts in January 2004.
The sample size for the two-year and three-year yields and forwards is 74. The sample size for all other
regressions is 106. In all regressions, the policy news shock is computed from these same 106 observations.
Robust standard errors are in parentheses.

window around an FOMC announcement, while the change in the

dependent variable is measured over a one-day window.6
The first column of Table I presents the effects of the pol-
icy news shock on nominal Treasury yields and forwards. Recall
that the policy news shock is scaled such that the effect on the
one-year Treasury yield is 100 basis points. Looking across dif-
ferent maturities, we see that the effect of the shock is somewhat
smaller for shorter maturities, peaks at 110 basis points for the
2-year yield and then declines monotonically to 38 basis points for

6. The longer window for the dependent variable adds noise to the regression
without biasing the coefficient of interest.
1296 THE QUARTERLY JOURNAL OF ECONOMICS

the 10-year yield. Because longer-term yields reflect expectations

about the average short-term interest rate over the life of the long
bond, it is easier to interpret the time-path of the response of

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instantaneous forward rates. Abstracting from risk premia, these
reveal market expectations about the short-term interest rate that
the market expects to prevail at certain points in time in the fu-
ture.7 The impact of our policy news shock on forward rates is
also monotonically declining in maturity from 114 basis points at
2 years to −8 basis points at 10 years. We show below that the neg-
ative effect on the 10-year nominal forward rate reflects a decline
in break-even inflation at long horizons.8
The second column of Table I presents the effects of the policy
news shock on real interest rates measured using TIPS. Although
the policy news shock affects nominal rates by construction, this
is not the case for real interest rates. In neoclassical models of
the economy, the Fed controls the nominal interest rate but has
no impact on real interest rates. In sharp contrast to this, we
estimate the impact of our policy news shock on the two-year real
yield to be 106 basis points, and the impact on the three-year
real yield to be 102 basis points. Again, the time-path of effects is
easier to interpret by viewing estimates for instantaneous forward
rates. The effect of the shock on the 2-year real forward rate is 99
basis points. It falls monotonically at longer horizons to 88 basis
points at 3 years, 47 basis points at 5 years, and 12 basis point
at 10 years (which is not statistically significantly different from
zero). Evidently, monetary policy shocks can affect real interest
rates for substantial amounts of time (or at least markets believe
they can). However, in the long run, the effect of monetary policy
shocks on real interest rates is zero as theory would predict.
The third column of Table I presents the effect of the policy
news shock on break-even inflation as measured by the difference
between nominal Treasury rates and TIPS rates. The first sev-
eral rows provide estimates based on bond yields, which indicate
that the response of break-even inflation is small. The shorter
horizon estimates are actually slightly positive but then become

7. For example, the effect on the two-year instantaneous forward rate is the
effect on the short-term interest rate that the market expects to prevail in two
years’ time.
8. Our finding that long-term inflation expectations decline in response to a
contractionary monetary policy shock is consistent with Beechey, Johannsen, and
Levin (2011) and Gürkaynak, Levin, and Swanson (2010).
 MONETARY NON-NEUTRALITY 1297

negative at longer horizons. None of these estimates are statisti-

cally significantly different from zero. Again, it is helpful to con-
sider instantaneous forward break-even inflation rates to get esti-

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mates of break-even inflation at points in time in the future. The
response of break-even inflation implied by the two-year forwards
is slightly positive, though statistically insignificant. The response
is negative at longer horizons: for maturities of 3, 5, and 10 years,
the effect is −6, −21, and −20 basis points, respectively. Only
the responses at 5 and 10 years are statistically significantly dif-
ferent from zero. Our evidence thus points to break-even inflation
responding modestly and quite gradually to monetary shocks that
have a substantial effect on real interest rates.
Table I presents results for a sample period from January 1,
2000, to March 19, 2014, except that we drop the period spanning
the height of the financial crisis in the second half of 2008 and the
first half of 2009.9 We choose to drop the height of the financial
crisis because numerous well-documented asset pricing anomalies
arose during this crisis period, and we wish to avoid the concern
that our results are driven by these anomalies. However, similar
results obtain for the full sample including the crisis, as well as
a more restrictive data sample ending in 2007, and for a sample
that also includes unscheduled FOMC meetings (see Appendix
Table A.1). The results for the sample ending in 2007 show that
our results are unaffected by dropping the entire period during
which the zero-lower-bound is binding and the Fed is engaged in
quantitative easing. Appendix Table A.2 presents results analo-
gous to those of Table I but using the unexpected change in the
Fed funds rate as the policy indicator.
Figure I presents a binned scatter plot of the relationship
between the policy news shock and the five-year real yield (the
average expected response of the short-term real interest rates
over the next five years). The variation in the policy news shock
ranges from −11 basis points to +10 basis points. The relationship
between the change in the five-year real yield and the policy news
shock does not seem to be driven by a few outliers.

III.B. Background Noise in Interest Rates

A concern regarding the estimation approach we describe
above is that other nonmonetary news might affect our monetary
policy indicator during the window we consider around FOMC

9. The sample period for two- and three-year yields and forwards is somewhat
shorter (it starts in 2004) because of data limitations (see Section II for details).
1298 THE QUARTERLY JOURNAL OF ECONOMICS

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FIGURE I
Binned Scatter Plot for Five-Year Real-Yield Regression

announcements. If this is the case, it will contaminate our mea-

sure of monetary shocks. This concern looms much larger if one
considers longer event windows than our baseline 30-minute win-
dow. It has been common in the literature on high-frequency
identification of monetary policy to consider a one- or two-day
window around FOMC announcements (e.g., Kuttner 2001;
Cochrane and Piazzesi 2002; Hanson and Stein 2015). In these
cases, the identifying assumption being made is that no other
shocks affect the policy indicator in question during these one or
two days. Especially when the policy indicator is based on inter-
est rates several quarters or years into the term structure—as
has recently become common to capture the effects of forward
guidance—the assumption that no other shocks affect this indi-
cator over one or two days is a strong assumption. Interest rates
at these maturities fluctuate substantially on non-FOMC days,
suggesting that other shocks than FOMC announcements affect
these interest rates on FOMC days. There is no way of knowing
whether these other shocks are monetary shocks or nonmonetary
shocks.
To assess the severity of this problem, Table II compares es-
timates of equation (1) based on OLS regressions to estimates
based on a heteroskedasticity-based estimation approach de-
veloped by Rigobon (2003) and Rigobon and Sack (2004). We
do this both for a 30-minute window and for a 1-day window.
 TABLE II
ALLOWING FOR BACKGROUND NOISE IN INTEREST RATES

2-year forward 5-year forward 10-year forward

Nominal Real Nominal Real Nominal Real

Policy news shock, 30-minute window

OLS 1.14 0.99 0.26 0.47 −0.08 0.12
[0.23, 2.04] [0.41, 1.57] [−0.12, 0.64] [0.14, 0.80] [−0.43, 0.28] [−0.12, 0.36]
Rigobon 1.10 0.96 0.22 0.46 −0.12 0.11
[0.31, 2.36] [0.45, 1.82] [−0.14, 0.64] [0.15, 0.84] [−0.46, 0.24] [−0.13, 0.35]
Policy news shock, 1-day window
OLS 1.24 1.00 0.44 0.48 0.05 0.15
[0.80, 1.69] [0.57, 1.43] [0.18, 0.70] [0.20, 0.76] [−0.20, 0.29] [−0.10, 0.39]
Rigobon 0.93 0.82 −0.11 0.33 −0.51 −0.04
[−0.64, 2.08] [0.38, 3.20] [−1.23, 0.33] [−0.07, 1.12] [−1.93, −0.08] [−0.51, 0.45]
2-year nominal yield, 1-day window
OLS 1.23 0.94 0.64 0.54 0.18 0.20
[1.07, 1.38] [0.69, 1.20] [0.43, 0.84] [0.31, 0.76] [0.01, 0.35] [0.02, 0.38]
Rigobon (90% CI) 1.14 0.82 −0.11 0.33 −0.51 −0.04
MONETARY NON-NEUTRALITY

[0.82, 1.82] [0.62, 2.98] [−7.94, 0.60] [−0.01, 7.48] [−10.00, −0.21] [−4.57, 0.38]

Notes. Each estimate comes from a separate “regression.” The dependent variable in each regression is the one-day change in the variable stated at the top of that column.
The independent variable in the first panel of results is the 30-minute change in the policy news shock around FOMC meeting times, in the second panel it is the one-day
change in the policy news shock, and in the third panel it is the one-day change in the two-year nominal yield. In each panel, we report results based on OLS and Rigobon’s
heteroskedasticity-based estimation approach. We report a point estimate and 95% confidence intervals except in the last row of Rigobon estimates, which reports 90% confidence
intervals. The sample of “treatment” days for the Rigobon method is all regularly scheduled FOMC meeting days from 1/1/2000 to 3/19/2014; this is also the period for which the
policy news shock is constructed in all “regressions.” The sample of “control” days for the Rigobon analysis is all Tuesdays and Wednesdays that are not FOMC meeting days over
the same period of time. In both the treatment and control samples, we drop July 2008 through June 2009 and 9/11/2001–9/21/2001. For two-year forwards, the sample starts in
January 2004. Confidence intervals for the OLS results are based on robust standard errors. Confidence intervals for the Rigobon method are calculated using the weak-IV robust
approach discussed in Online Appendix C with 5,000 iterations.
1299

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1300 THE QUARTERLY JOURNAL OF ECONOMICS

The heteroskedasticity-based estimator is described in detail in

Online Appendix B. It allows for “background” noise in interest
rates arising from other shocks during the event windows being

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considered. The idea is to compare movements in interest rates
during event windows around FOMC announcements to other
equally long and otherwise similar event windows that do not
contain an FOMC announcement. The identifying assumption is
that the variance of monetary shocks increases at the time of
FOMC announcements, while the variance of other shocks (the
background noise) is unchanged.
The top panel of Table II compares estimates based on OLS
to those based on the heteroskedasticity-based estimator (Rigobon
estimator) for a subset of the assets we consider in Table I when
the event window is 30 minutes as in our baseline analysis. The
difference between the two estimators is very small, both for the
point estimates and the confidence intervals.10 This result indi-
cates that there is in fact very little background noise in interest
rates over a 30-minute window around FOMC announcements
and the OLS identifying assumption—that only monetary shocks
occur within the 30-minute window—thus yields a point esti-
mate and confidence intervals that are close to correct. Table A.3
presents a full set of results based on the Rigobon estimator and
a 30-minute window. It confirms that OLS yields very similar re-
sults to the Rigobon estimator for all the assets we consider when
the event window is 30 minutes.
In contrast, the problem of background noise is quite impor-
tant when the event window being used to construct our policy
news shocks is one day. The second panel of Table II compares es-
timates based on OLS to those based on the Rigobon estimator for
policy news shocks constructed using a one-day window. In this
case, the differences between the OLS and Rigobon estimates are
substantial. The point estimates in some cases differ by dozens
of basis points and have different signs in three of the six cases
considered. However, the most striking difference arises for the

10. The confidence intervals for the Rigobon estimator in Table II are con-
structed using a procedure that is robust to inference problems that arise when
the amount of background noise is large enough that there is a significant proba-
bility that the difference in the variance of the policy indicator between the sample
of FOMC announcements and the “control” sample is close to zero. In this case,
the conventional bootstrap approach to constructing confidence intervals will yield
inaccurate results. Online Appendix C describes the method we use to construct
confidence intervals in detail. We thank Sophocles Mavroeidis for suggesting this
approach to us.
 MONETARY NON-NEUTRALITY 1301

confidence intervals. OLS yields much narrower confidence inter-

vals than those generated using the Rigobon method. According
to OLS, the effects on the five-year nominal and real forwards

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are highly statistically significant, while the Rigobon estimator
indicates that these effects are far from being significant.
This difference between OLS and the Rigobon estimator in-
dicates that there is a large amount of background noise in the
interest rates used to construct the policy news shock over a one-
day window. The Rigobon estimator is filtering this background
noise out. The fact that the confidence intervals for the Rigobon
estimator are so wide in the one-day window case implies that
there is very little signal left in this case. The OLS estimator, in
contrast, uses all the variation in interest rates (both the true sig-
nal from the announcement and the background noise). Clearly,
this approach massively overstates the true statistical precision of
the effect arising from the FOMC announcement when a one-day
window is used.
The difference between OLS and the Rigobon estimator is
even larger when a longer-term interest rate is used as the policy
indicator that proxies for the size of monetary shocks. The third
panel of Table II compares results based on OLS to those based
on the Rigobon estimator when the policy indicator is the change
in the two-year nominal yield over a one-day window. Again, the
confidence intervals are much wider using the Rigobon estimator
than OLS. In fact, here we report 90% confidence intervals for
the Rigobon estimator since the 95% confidence intervals are in
some cases infinite (i.e., we were unable to find any value of the
parameter of interest that could be rejected at that significance
level).
An important substantive difference arises between the OLS
and Rigobon estimates in the case of the 10-year real forward
rate when the 2-year nominal yield is used as the policy indicator.
Here, OLS estimation yields a statistically significant effect of
the monetary shock on forward rates at even a 10-year horizon.
This result is emphasized by Hanson and Stein (2015). However,
the Rigobon estimator with appropriately constructed confidence
intervals reveals that this result is statistically insignificant. Our
baseline estimation approach using a 30-minute window and the
policy news shock as the proxy for monetary shocks yields a point
estimate that is small and statistically insignificant.11

11. Hanson and Stein (2015) also present an estimator based on instrumenting
the two-day change in the two-year rate with the change in the two-year rate
1302 THE QUARTERLY JOURNAL OF ECONOMICS

III.C. Risk Premia or Expected Future Short-Term Rates?

One question that arises when interpreting our results is to
what extent the movements in long-term interest rates we iden-

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tify reflect movements in risk premia as opposed to changes in
expected future short-term interest rates. A large literature sug-
gests that changes in risk premia do play an important role in
driving movements in long-term interest rates in general. Yet for
our analysis, the key question is not whether risk premia matter
in general but how important they are in explaining the abrupt
changes in interest rates that occur in the narrow windows around
the FOMC announcements that we focus on.12
In Online Appendix D, we present three sets of results that
indicate that risk premium effects are not driving our empirical
results. First, the impact of our policy news shock on direct mea-
sures of expectations from the Blue Chip Economic Indicators
indicate that our monetary shocks have large effects on expected
short-term nominal and real rates. Second, the impact of our policy
news shock on risk-neutral expected short rates from the state-
of-the-art affine term structure model of Abrahams et al. (2015)
is similar to our baseline results. Third, the impact of our policy
news shock on interest rates over longer event windows does not
suggest that the effects we estimate dissipate quickly (although
the standard errors in this analysis are large).

during a 60-minute window around the FOMC announcement. This yields similar
results to their baseline. Since this procedure is not subject to the concerns raised
above, it suggests that there are other sources of difference between our results
and those of Hanson and Stein than econometric issues. One possible source of
difference is that we use different monetary shock indicators. Their policy indicator
(the change in the two-year yield) is further out in the term structure and may
be more sensitive to risk premia. As we discuss in Section III.C, our measure of
monetary shocks is uncorrelated with the risk premia implied by the affine term
structure model of Abrahams et al. (2015), whereas Hanson and Stein’s monetary
shocks are associated with substantial movements in risk premia. The difference
could also arise from the fact that Hanson and Stein focus on a two-day change
in long-term real forwards, which could yield different results if the response of
long-term bonds to monetary shocks is inertial.
12. Piazzesi and Swanson (2008) show that Fed funds futures have excess re-
turns over the Fed funds rate and that these excess returns vary counter-cyclically
at business cycle frequencies. However, they argue that high-frequency changes in
Fed funds futures are likely to be valid measures of changes in expectations about
future Fed funds rates because they difference out risk premia that vary primarily
at lower frequencies.
 MONETARY NON-NEUTRALITY 1303

We also consider an alternative, market-based measure of

inflation expectations based on inflation swap data.13 The sam-
ple period for this analysis is limited by the availability of swaps

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data to begin on January 1, 2005. Unfortunately, due to the short
sample available to us, the results are extremely noisy, and are
therefore not particularly informative. As in our baseline analysis
there is no evidence of large negative responses in inflation to our
policy news shock (as would arise in a model with flexible prices).
Indeed, the estimates from this approach (which are compared
to our baseline results in Appendix Table A.4) suggest a some-
what larger “price puzzle”—that is, positive inflation response—at
shorter horizons, though this is statistically insignificant.

IV. THE FED INFORMATION EFFECT

The results in Section III show that variation in nominal in-
terest rates caused by monetary policy announcements have large
and persistent effects on real interest rates. The conventional in-
terpretation of these facts is that they imply that prices must
respond quite sluggishly to shocks. We illustrate this in a con-
ventional business cycle model in Online Appendix E. This con-
ventional view of monetary shocks has the following additional
prediction that we can test using survey data: a surprise increase
in interest rates should cause expected output to fall. To test this
prediction, we run our baseline empirical specification—equation
(1)—at a monthly frequency with the monthly change in Blue
Chip survey expectations about output growth as the dependent
variable and the policy news shock that occurs in that month as
the independent variable.14
Table III reports the resulting estimates. The dependent vari-
able is the monthly change in expected output growth over the

13. An inflation swap is a financial instrument designed to help investors

hedge inflation risk. As is standard for swaps, nothing is exchanged when an
inflation swap is first executed. However, at the maturity date of the swap, the
counterparties exchange Rtx − t , where Rtx is the x-year inflation swap rate and
t is the reference inflation over that period. If agents were risk neutral, therefore,
Rt would be expected inflation over the x year period. See Fleckenstein, Longstaff,
and Lustig (2014) for an analysis of the differences between break-even inflation
from TIPS and inflation swaps.
14. We exclude policy news shocks that occur in the first week of the month
because in those cases we do not know whether they occurred before or after the
survey response.
1304 THE QUARTERLY JOURNAL OF ECONOMICS

TABLE III
RESPONSE OF EXPECTED OUTPUT GROWTH OVER THE NEXT YEAR

1995–2014 2000–2014 2000–2007 1995–2000

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Policy news shock 1.01 1.04 0.95 0.79
(0.32) (0.35) (0.32) (0.63)
Observations 120 90 52 30

Notes. We regress changes from one month to the next in survey expectations about output growth over the
next year from the Blue Chip Economic Indicators on the policy news shock that occurs in that month (except
that we drop policy news shocks that occur in the first week of the month because we do not know whether
these occurred before or after the survey response). Specifically, the dependent variable is the change in the
average forecasted value of output growth over the next three quarters (the maximum horizon over which
forecasts are available for the full sample). See Online Appendix F for details. We present results for four
sample periods. The longest sample period we have data for is 1995m1–2014m4; this is also the period for
which the policy news shocks are constructed. We also present results for 2000m1–2014m4 (which corresponds
to the sample period used in Table I), 2000m1–2007m12 (a precrisis sample period), and 1995m1–1999m12.
As in our other analysis, we drop data from July 2008 through June 2009. Robust standard errors are in
parentheses.

next year (see Online Appendix F for details). In sharp contrast

to the conventional theory of monetary shocks, policy news shocks
that raise interest rates lead expectations about output growth to
rise rather than fall.15 We present results for four sample periods.
The longest sample period for which we are able to construct our
policy news shock is 1995–2014. We also present results for the
sample period 2000–2014, which corresponds to the sample pe-
riod we use in most of our other analysis. For robustness, we also
present results for two shorter sample periods (1995–2000 and
2000–2007). The results are similar across all four sample peri-
ods, but of course less precisely estimated for the shorter sample
periods.
Figure II presents a binned scatter plot of the relationship
between changes in expected output growth and our policy news
shock over the 1995–2014 sample period. This scatter plot shows
that the results in Table III are not driven by outliers. Finally,
Appendix Table A.5 presents the response of output growth ex-
pectations separately for each quarter that the Blue Chip survey
asks about. These are noisier but paint the same picture as the
results in Table III.
A natural interpretation of this evidence is that FOMC an-
nouncements lead the private sector to update its beliefs not only
about the future path of monetary policy but also about other eco-
nomic fundamentals. For example, when an FOMC announcement

15. Campbell et al. (2012) present similar evidence regarding the effect of
surprise monetary shocks on Blue Chip expectations about unemployment.
 MONETARY NON-NEUTRALITY 1305

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FIGURE II
Binned Scatter Plot for Expected Output Growth Regression

signals higher interest rates than markets had been expecting,

market participants may view this as implying that the FOMC
is more optimistic about economic fundamentals going forward
than they had thought, which in turn may lead the market par-
ticipants themselves to update their own beliefs about the state
of the economy. We refer to effects of FOMC announcements on
private sector views of non-monetary economic fundamentals as
“Fed information effects.”
The idea that the Fed can have such information effects relies
on the notion that the FOMC has some knowledge regarding the
economy that the private sector doesn’t have or has formulated a
viewpoint about the economy that the private sector finds valu-
able. Is it reasonable to suppose that this is the case? In terms of
actual data, the FOMC has access to the same information as the
private sector with minor exceptions.16 However, the Fed does em-
ploy a legion of talented, well-trained economists whose primary
role is to process and interpret all the information being released

16. The FOMC may have some advance knowledge of industrial production
data since the Federal Reserve produces these data. It also collects anecdotal infor-
mation on current economic conditions from reports submitted by bank directors
and through interviews with business contacts, economists, and market experts.
This information is subsequently published in reports commonly known as the
Beige Book.
1306 THE QUARTERLY JOURNAL OF ECONOMICS

about the economy. This may imply that the FOMC’s view about
how the economy will evolve contains a perspective that affects the
views of private agents. This is the view Romer and Romer (2000)
argue for in their classic paper on Federal Reserve information.17

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The idea that the Fed can influence private sector beliefs
through its analysis of public data is somewhat unconventional
in macroeconomics. However, the finance literature on analyst
effects suggests this is not implausible. This literature finds that
the most influential analyst announcements can have quite large
effects on the stock market (see, e.g., Loh and Stulz 2011, p. 593).
Loh and Stulz note: “Kenneth Bruce from Merrill Lynch issued a
recommendation downgrade on Countrywide Financial on August
15, 2007, questioning the giant mortgage lender’s ability to cope
with a worsening credit crunch. The report sparked a sell-off in
Countrywide’s shares, which fell 13% on that day.” If Kenneth
Bruce can affect the market’s views about Countrywide, perhaps it
is not unreasonable to believe that the Fed can affect the market’s
views about where the economy is headed.
If Fed information is important, one might expect that con-
tractionary monetary shocks would disproportionately occur when
the Fed is more optimistic than the private sector about the state
of the economy. In Online Appendix G, we test this proposition
using the Fed’s Green Book forecast about output growth as a
measure of its optimism about the economy.18 We find that in-
deed, our policy news shocks tend to be positive (i.e., indicate a
surprise increase in interest rates) when the Green Book fore-
cast about current and future real GDP growth is higher than the
corresponding Blue Chip forecast (Table G.1, panel A). We fur-
thermore find that the difference between Green Book and Blue
Chip forecasts tends to narrow after our policy news shocks occur
(Table G.1, panel B). This suggests that private sector forecasters
may update their forecasts based on information they gleam from
FOMC announcements.

17. This does not necessarily imply that the Fed should be able to forecast
the future evolution of the economy better than the private sector. The private
sector, of course, also processes and interprets the information released about the
economy. It may therefore also be able to formulate a view about the economy
that the Fed finds valuable. In other words, information can flow both ways with
neither the Fed nor the private sector having a clear advantage.
18. The Green Book forecast is an internal forecast produced by the staff of the
Board of Governors and presented at each FOMC meeting. Greenbook forecasts
are made public with a five-year lag.
 MONETARY NON-NEUTRALITY 1307

V. CHARACTERIZING MONETARY NON-NEUTRALITY WITH FED

INFORMATION
The evidence we present in Section IV calls for more so-

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phisticated modeling of the effects of monetary announcements
than is standard in the literature. Rather than affecting beliefs
only about current and future monetary policy, FOMC announce-
ments must also affect private sector beliefs about other economic
fundamentals.
An important consequence of this is that our evidence does
not necessarily point to nominal and real rigidities being large. It
may be that the responses of real interest rates that we estimate
in response to FOMC announcements mostly reflect changes in
private sector expectations about the natural rate of interest. If
this is the case, the fact that we find that our shocks have little
effect on inflationary expectations may be consistent with small
nominal and real rigidities, since the tightening of policy relative
to the natural rate is small.19
But even if this is true—that the responses of real interest
rates that we estimate mostly reflect changes in private sector ex-
pectations about the natural rate of interest—this does not imply
that the Fed is powerless. Quite to the contrary, in this case, the
Fed has enormous power over beliefs about economic fundamen-
tals, which may in turn have large effects on economic activity.
Our evidence on the response of real interest rates and ex-
pected inflation to a monetary announcement, therefore, implies
that either (i) nominal and real rigidities are large, or (ii) the
Fed can affect private sector beliefs about future nonmonetary
fundamentals by large amounts. In other words, it implies that
the Fed is powerful, either through the conventional channel or a
nonconventional channel (or some combination).
To make these arguments precise, we now present a New
Keynesian model of the economy augmented with Fed informa-
tion effects. We then estimate this model to match the responses
of interest rates, expected inflation, and expected output growth
to FOMC announcements calculated above. Finally, we use the
estimated model to assess the degree of monetary non-neutrality
implied by our evidence and to assess how much of this monetary
non-neutrality arises from traditional channels versus informa-
tion effects.

19. This idea is explained in more detail below and in Online Appendix E.
1308 THE QUARTERLY JOURNAL OF ECONOMICS

V.A. A New Model with Fed Information Effects

Most earlier theoretical work on the signaling effect of mon-
etary policy has made the very restrictive assumption that the

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Fed can only signal through its actions. The focus of much of this
literature has been on the limitations of what the Fed can sig-
nal with its actions. The recent empirical literature on monetary
policy has, however, convincingly demonstrated that the Fed also
signals through its statements (Gürkaynak, Sack, and Swanson
2005). This implies that the Fed’s signals can be much richer;
they can incorporate forward guidance, and they can distinguish
between different types of shocks. With a much richer signal struc-
ture, the key question becomes: what information would the Fed
like to convey?
We model FOMC announcements as affecting private sector
beliefs about the path of the “natural rate of interest,” the real
interest rate that would prevail absent pricing frictions. This is
a natural choice since tracking the natural rate is optimal in the
model we consider absent information effects. If the Fed’s goal is to
track the natural rate of interest, it seems natural that announce-
ments by the Fed about its current and future actions provide
information about the future path of the natural rate of interest.
Apart from including a Fed information effect, the model we
use differs in two ways from the textbook New Keynesian model:
households have internal habits, and we allow for a backward-
looking term in the Phillips curve. These two features allow the
model to better fit the shapes of the impulse responses we have
estimated in the data. Detailed derivations of household and
firm behavior in this model are presented in Online Appendix
H. There, we show that private sector behavior in this model can
be described by a log-linearized consumption Euler equation and
Phillips curve that take the following form:

(2) λ̂xt = Et λ̂xt+1 + (ι̂t − Et π̂t+1 − r̂tn),

(3) π̂t = β Et π̂t+1 + κωζ̂ x̂t − κ ζ̂ λ̂xt .

Hatted variables denote percentage deviations from steady state.

π̂t = π̂t − π̂t−1 . The variable λ̂xt = λ̂t − λ̂nt denotes the marginal
utility gap (the difference between actual marginal utility of con-
sumption λ̂t and the “natural” level of marginal utility λ̂nt that
 MONETARY NON-NEUTRALITY 1309

would prevail if prices were flexible), x̂ = ŷt − ŷtn denotes the “out-
put gap,” π̂t denotes inflation, ι̂t denotes the gross return on a
one-period, risk-free, nominal bond, and r̂tn denotes the “natural

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rate of interest,” which is a function of exogenous shocks to tech-
nology. The parameter β denotes the subjective discount factor of
households, while κ, ω, and ζ̂ are composite parameters that de-
termine the degree of nominal and real rigidities in the economy.
With internal habits, the marginal utility gap is

(4) λ̂xt = −(1 + b2 β)σc x̂t + bσc x̂t−1 + bβσc Et x̂t+1 ,

−1
σ
where b governs the strength of habits and σc = − (1−b)(1−bβ) , where
σ is the intertemporal elasticity of substitution.
We assume that the monetary authority sets interest rates
according to the following simple rule:

(5) ι̂t − Et π̂t+1 = r̄t + φπ π̂t ,

with r̄t following an AR(2) process

(6) r̄t = (ρ1 + ρ2 )r̄t−1 − ρ1 ρ2r̄t−2 + t ,

where ρ 1 and ρ 2 are the roots of the lag polynomial for r̄t and
 t is the innovation to the r̄t process. Here  t is the monetary
shock. Notice that it can potentially have a long-lasting effect on
real interest rates through the AR(2) process for r̄t . We choose this
specification to be able to match the effects of the monetary shocks
we estimate in the data. The shocks we estimate in the data have
a relatively small effect on contemporaneous interest rates but a
much larger effect on future interest rates (see Table I)—that is,
they are mostly but not exclusively forward guidance shocks. The
AR(2) specification for r̄t can capture this if ρ 1 and ρ 2 are both large
and positive leading to a pronounced hump shape in the impulse
response of r̄t (and therefore a pronounced hump shape across the
term structure in the contemporaneous response of longer-term
interest rates as in Table I).20

20. How should the monetary shocks  t be interpreted? A natural interpre-

tation is the following: the Fed seeks to target the natural rate of interest. When
the Fed makes an announcement, it seeks to communicate changes in its beliefs
about the path of the natural rate to the public. The changes in beliefs sometimes
surprise the public and therefore lead to a shock.
1310 THE QUARTERLY JOURNAL OF ECONOMICS

As discussed already, the way we model the Fed information

effect is by assuming that FOMC announcements may affect the
private sector’s beliefs about the path of the natural rate of inter-

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est. The simplest way to do this is to assume that private sector
beliefs about the path of the natural rate of interest shift by some
fraction ψ of the change in r̄t . Formally, in response to a monetary
announcement

j = ψ Et r̄t+ j .
n
(7) Et r̂t+

Moreover, we assume that the shock to expectations about the

current value of the natural rate of output is proportional to the
shock to expectations about the current monetary policy with the
same factor of proportionality, that is, Et ŷtn = ψ Et r̄t .21
Here the parameter ψ governs the extent to which mone-
tary announcements have information effects versus traditional
effects. A fraction ψ of the shock shows up as an information ef-
fect, while a fraction 1 − ψ shows up as a traditional gap between
the path for real interest rates and the (private sector’s beliefs
about the) path for the natural rate of interest.22

V.B. Estimation Method

We estimate four key parameters of the model using simu-
lated method of moments. The four parameters we estimate are
the two autoregressive roots of the shock process (ρ 1 and ρ 2 ), the
information parameter (ψ) and the “slope of the Phillips curve”
(κ ζ̂ ). We fix the remaining parameters at the following values:
we choose a conventional value of β = 0.99 for the subjective dis-
count factor. Our baseline value for the intertemporal elasticity of
substitution is σ = 0.5, but we explore robustness to this choice.
We fix the Taylor rule parameter to φ π = 0.01. This is roughly
equivalent to a value of 1.01 for the more conventional Taylor rule

21. Here we assume that the FOMC meeting occurs at the beginning of the
period, before the value of ŷtn is revealed to the agents. In reality, uncertainty
persists about output in period t until well after period t, due to heterogeneous
information. We abstract from this.
22. This way of modeling the information effect has the crucial advantage that
it is simple and parsimonious enough to allow us to account for the effects of FOMC
announcements on the entire path of future interest rate expectations—that is, the
role of forward guidance. This is a distinguishing feature versus previous work.
Ellingsen and Söderström (2001) present a model in which the signaling effect
derives from announcements about the current interest rate.
 MONETARY NON-NEUTRALITY 1311

specification without the Et π̂t+1 term on the left-hand side of equa-

tion (5). We choose this value to ensure that the model has a unique
bounded equilibrium but at the same time limit the amount of en-

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dogenous feedback from the policy rule. This helps ensure that
the response of the real interest rate dies out within 10 years as
we estimate in the data.23 We set the elasticity of marginal cost
with respect to own output to ω = 2. This value results from a
Frisch labor supply elasticity of 1 and a labor share of 23 . Finally,
we set the habit parameter to b = 0.9, a value very close to the
one estimated by Schmitt-Grohé and Uribe (2012).
To ease the computational burden of the simulated method
of moments estimation we use a two-stage iterative procedure.
In the first stage, we estimate the two autoregressive roots of
the monetary shock process (ρ 1 and ρ 2 ) to fit the hump-shaped
response of real interest rates to our policy news shock. We do this
for fixed values of the information parameter and the slope of the
Phillips curve. The moments we use in this step are the responses
of 2-, 3-, 5-, and 10-year real yields and forwards reported in
Table I. In the second step, we estimate the information parameter
(ψ) and the slope of the Phillips curve (κ ζ̂ ) for fixed values of the
two autoregressive roots. The moments we use in this step are
the responses of 2-, 3-, 5-, and 10-year break-even inflation (both
yields and forwards) reported in Table I and the responses of
output growth expectations reported in Appendix Table A.5. We
iterate back and forth between these steps until convergence.
In both steps, we use a loss function that is quadratic in the
difference between the moments discussed above and their the-
oretical counterparts in the model.24 We use a weighting matrix
with the inverse standard deviations of the moments on the diag-
onal, and with the off-diagonal values set to 0. We use a bootstrap
procedure to estimate standard errors. Our bootstrap procedure
is to resample the data with replacement, estimate the empirical
moments on the resampled data, and then estimate the structural

23. Recent work has shown that standard New Keynesian models such as the
one we are using are very sensitive to interest rate movements in the far future
(Carlstrom, Fuerst, and Paustian 2015; Mckay, Nakamura, and Steinsson 2016).
24. The theoretical counterparts are the responses of the corresponding vari-
able to a monetary shock in the model. Since the magnitude of the shock in our
simulations is arbitrary, we make sure to rescale all responses from the model in
such a way that the three-year real forward rate is perfectly matched. We use the
methods and computer code described in Sims (2001) to calculate the equilibrium
of our model.
1312 THE QUARTERLY JOURNAL OF ECONOMICS

TABLE IV
ESTIMATES OF STRUCTURAL PARAMETERS

ψ κ ζ̂ × 10−5 ρ1 ρ2

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Baseline 0.68 11.2 0.9 0.79
[0.33, 0.84] [0.0, 60.2] [0.83, 0.96] [−0.69, 0.89]
No Fed information 0.00 3.4 0.9 0.79
(ψ = 0) — [0.0, 24.1] [0.83, 0.96] [−0.69, 0.89]
Full Fed information 0.99 563 0.9 0.79
(ψ = 0.99) — [0, 12,538] [0.82, 0.96] [−0.67, 0.89]
Lower IES 0.67 13.7 0.9 0.79
(σ = 0.25) [0.25, 0.89] [0.0, 94.6] [0.83, 0.96] [−0.69, 0.89]
Higher IES 0.68 8.2 0.9 0.79
(σ = 1) [0.42, 0.81] [0.0, 44.0] [0.83, 0.96] [−0.69, 0.89]
No habits 1 1,000 0.9 0.79
(b = 0) [0.92, 1.00] [0, 43,236] [0.83, 0.96] [−0.69, 0.89]

Notes. The table reports our estimates of the structural parameters of the model that we estimate. We
report 95% confidence intervals in square brackets below the point estimate for each parameter. These are
based on the bootstrap procedure described in the text. In the No Fed information case and the Full Fed
information case, the slope of the Phillips curve is estimated only off of inflation moments. In the other cases,
the slope of the Phillips curve and the information parameter are estimated off of both inflation and GDP
growth moments.

parameters as described above using a loss function based on the

estimated empirical moments for the resampled data.25 We repeat
this procedure 1,000 times and report the 2.5% and 97.5% quan-
tiles of the statistics of interest. Importantly, this procedure for
constructing the confidence intervals captures the statistical un-
certainty associated with our empirical estimates in Table I and
Appendix Table A.5.

V.C. Results and Intuition

Our primary interest is to assess the extent to which FOMC
announcements contain Fed information and how this affects in-
ference about other key aspects of the economy such as the slope
of the Phillips curve. Table IV presents our parameter estimates,

25. The resampling procedure is stratified because the empirical moments are
estimated from different data sets and different sample periods. The stratification
makes sure that each resampled data set is consistent with the original data set
along the following dimensions: the number of observations for the yields and
forwards before and after 2004 is the same as in the original data set (since the
sample period for the two-year and three-year yields and forwards starts in 2004).
The number of Blue Chip observations that do not report four and seven quarters
ahead expected GDP growth are the same as in the original data set, since Blue
Chip only asks forecasters to forecast the current and next calendar year.
 MONETARY NON-NEUTRALITY 1313

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FIGURE III
Responses of Nominal and Real Rates and Inflation to a Contractionary Shock

FIGURE IV
Responses of Expected Output Growth and Output Gap to a Contractionary
Shock

and Figures III–V illustrate the fit of the model. As in the data, the
estimated model generates a persistent, hump-shaped response of
nominal and real interest rates with a small and delayed effect
on expected inflation (see Figure III). To generate this type of re-
sponse, we estimate that both of the autoregressive roots of the
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FIGURE V
Decomposition of Real Rate into Interest Rate Gap and Natural Interest Rate

monetary shock process are large and positive, and we estimate a

small slope of the Phillips curve.
We also estimate that the monetary shock leads to a pro-
nounced increase in expectations about output growth as in the
data (see Figure IV). The model can match the increase in ex-
pected growth following a surprise increase in interest rates by
estimating a large information effect. We estimate that roughly
two-thirds of the monetary shock is a shock to beliefs about future
natural rates of interest (see Figure V).
As Figure IV illustrates, our monetary shock simultaneously
leads to an increase in expectations about output growth and a
decrease in output relative to the natural rate of output (i.e., a
decrease in the output gap). This is a consequence of the fact that
the information effect is large but still substantially smaller than
the overall increase in interest rates. Output growth expectations
rise because the monetary shock is interpreted as good news about
fundamentals. But since the Fed increases interest rates by more
than the private sector believes the natural rate of interest rose,
private sector expectations about the output gap fall.
Despite estimating a large information effect, we estimate a
very flat Phillips curve. This is consistent with prior empirical
work. Mavroeidis, Plagborg-Moller, and Stock (2014) survey the
literature that has estimated Phillips curves and, using a common
data set, run a huge number of a priori reasonable specifications
 MONETARY NON-NEUTRALITY 1315

that span different choices made in this literature. They find that
the estimated values of the slope of the Phillips curve vary sub-
stantially across specifications and are symmetrically dispersed

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around a value of 0. One reason our estimated Phillips curve is
very flat is that the shocks that we estimate are substantially more
persistent than most other identified monetary policy shocks (e.g.,
Christiano, Eichenbaum, and Evans 2005). This means that our
shocks imply forward guidance about interest rates quite far in
the future. It has recently been shown that standard New Keyne-
sian models implies that far future forward guidance has large ef-
fects on current outcomes (Carlstrom, Fuerst, and Paustian 2015;
McKay, Nakamura, and Steinsson 2016).
To illustrate how allowing for the information effect affects
our estimates, we reestimate the model setting the information
effect to 0. In this case, we remove the expected output growth
moments from the objective function of the estimation because
these moments are impossible to match without an information
effect. The second row in Table IV presents the estimates for this
case. We see that ignoring the information effect yields a substan-
tially flatter Phillips curve—implying a substantial overestimate
of nominal and real rigidities—relative to our baseline estimation.
We also report a case where the information effect is set to a value
close to 1. In this case, the slope of the Phillips curve is estimated
to be much steeper than in our baseline case.
Clearly, the information effect has an important effect on in-
ference about the slope of the Phillips curve. This arises because
the effect of the monetary shock on the interest rate gap—the
gap between the interest rate and the natural rate of interest—is
much smaller when the information effect is estimated to be large
than it is when the information effect is estimated to be small. It
is the response of the interest rate gap as opposed to the response
of the real interest rate itself that determines the response of in-
flation (see equation (12) in Online Appendix E). The intuition is
that, when the Fed raises rates, people perceive this as good news
about economic fundamentals, and this counters the conventional
channel of monetary policy whereby an interest rate hike lowers
output.
Table IV also reports alternative estimates where we vary
the value of the intertemporal elasticity of substitution (IES) and
the habit formation parameter. Varying the IES does not affect
our estimates much. This may seem surprising. A smaller value
of the IES implies that larger movements in the natural rate of
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FIGURE VI
Response of Actual and Natural Expected Output

interest are needed to match the movements in expected growth

rates observed in the data. This would suggest that a lower value
of the IES would yield a larger value of the information effect.
However, in our model with substantial habit formation the IES
and the information parameters also affect the shape of the re-
sponse of output growth. These effects imply that similar values
of the information parameter are estimated for a wide range of
values of the IES. In contrast, when we set the habit parameter
to 0, we estimate that the entire change in interest rates is an
information effect. As a consequence, we also estimate a much
steeper Phillips curve. However, the fit of the model to the output
growth moments is much worse without habit formation.

VI. THE CAUSAL EFFECT OF MONETARY SHOCKS

The large information effect we estimate in Section V funda-
mentally changes how we should interpret the response of output
and inflation to monetary policy announcements. Figure VI plots
the response of private sector beliefs about output, the natural
rate of output, and the output gap to a monetary shock that in-
creases interest rates in our estimated model from Section V. The
figure shows that this surprise monetary tightening leads to a
large and permanent increase in expected output.
 MONETARY NON-NEUTRALITY 1317

How can this be? Can monetary policy really have such huge
effects on output 10 years in the future? Isn’t monetary policy
neutral in the long run? Shouldn’t a monetary tightening de-

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crease output? Here it is crucial to recognize that the information
the Fed reveals about economic fundamentals is largely (perhaps
mostly) information that the private sector would have learned
about eventually through other channels in the absence of the
Fed’s announcement. This introduces an important subtlety into
the assessment of the effect of monetary policy that has, to our
knowledge, not been discussed in the existing literature. To cor-
rectly assess the causal effect of monetary policy on output, we
need to compare versus a reasonable counterfactual that accounts
for the fact that the changes in fundamentals that the Fed’s an-
nouncement reveals would have occurred even in the absence of
the announcement. In other words, we want a counterfactual in
which the path of productivity—the exogenous fundamental we
assume the Fed provides information about—follows the same
path as in the actual response.
To construct this counterfactual, we must take a stand on
when the private sector would have learned about the changes
in fundamentals revealed by the Fed in the absence of the Fed
announcement. We choose a particularly simple counterfactual.
In this counterfactual, the private sector learns about changes in
productivity when they occur and it believes that productivity fol-
lows a random walk. To be clear, this counterfactual represents
our assumption about what would have happened regarding pri-
vate sector beliefs about economic fundamentals in the absence of
the Fed announcement. One could consider other counterfactuals.
We don’t have any data to precisely pin down the counterfactual.
But we think that our chosen counterfactual is reasonable and it
serves the purpose of illustrating the main issue that one needs to
use a counterfactual in which the changes in economic fundamen-
tals that the Fed provides information about would occur even in
the absence of the Fed announcement.
We must also make an assumption about how monetary policy
reacts to changes in the natural rate of interest in the counterfac-
tual. In keeping with the general assumption that the Fed seeks
to track the natural rate of interest, we assume that monetary
policy varies the interest rate to track the natural rate of interest
in the counterfactual.
Figure VII presents actual and counterfactual output growth
constructed in this way. The figure reveals that most of the
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FIGURE VII
Response of Actual and Counterfactual Expected Output

increase in output would have occurred anyway in the absence of

the Fed announcement. Given this new counterfactual, the “causal
effect” of the Fed on output growth—the difference between what
happens following the monetary shock and what would have hap-
pened as represented by the counterfactual—no longer looks so
implausible. This difference is much more modest than the over-
all change in beliefs about the path of output (which includes the
effects of the productivity shocks the Fed is informing the public
about).
Figure VIII plots this measure of the causal effect of monetary
shocks on output. The figure also decomposes it into two compo-
nents: the effect on the output gap (which falls) and the effect on
the natural rate of output (which rises). The effect on the output
gap is the conventional channel of monetary policy: an interest
rate increase relative to the natural rate of interest leads to a
drop in output relative to the natural rate of output. The second
component is a novel effect of Fed information.
A positive shock to beliefs about economic fundamentals—
which leads the future path of the natural rate of interest to
rise—has a positive causal effect on output even relative to the
counterfactual described above. Why is this? This effect arises
because of the dynamic linkages in our model. In our model
habit formation by households is important and households under-
stand this. When consumers expect consumption to be high in the
 MONETARY NON-NEUTRALITY 1319

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FIGURE VIII
Causal Effect of Monetary Shocks on Expected Output

future, they want to consume more today to build up their habit.

This implies that positive news about the future raises consump-
tion and output today. Other dynamic linkages would yield similar
effects of Fed information. For example, in a model with capital
accumulation, news about high future productivity would cause
an increase in investment on announcement and thereby affect
current output.

VI.A. Policy Implications

The findings discussed above have important policy implica-
tions. The fact that the information effect of surprise Fed tight-
enings stimulates output implies that the Fed is “fighting against
itself ” when it surprises markets. Figure VIII shows that for our
estimated parameters the overall effect of the two channels is
for an interest rate increase to raise output—the opposite from
the conventional view of how monetary policy works.26 If the Fed
would like to stimulate economic activity, a surprise policy easing
may be counterproductive because the increase in pessimism that
it causes itself pulls the economy further down.

26. Recall that this result is not simply an implication of the fact that the Fed’s
announcement is good news that raises output expectations. We are subtracting
the counterfactual. The effect we are talking about here is the effect that learning
the good news earlier has on the path of output.
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FIGURE IX
Expected Output Growth with and without the Information Effect

This raises the question: should the Fed withhold bad news
about the economy to avoid the increased pessimism this infor-
mation would cause? A full analysis of this question is beyond the
scope of this article. But we note two reasons it may not be the case
that the Fed should withhold bad news. First, an attempt by the
Fed to systematically bias its signals is likely to be ineffective be-
cause the private sector will learn how to interpret what the Fed
says and adjust for the bias. Second, advance knowledge about
changes in fundamentals allows agents to prepare gradually for
these changes, which is likely to improve welfare. A possible ex-
ception to this intuition is a circumstance when the Fed is not able
to respond to the information it is revealing by tracking the up-
dated natural rate, for example, when interest rates are at the zero
lower bound. In that case, it may be optimal for the Fed to withhold
information.
The information effect also implies that there is an impor-
tant distinction between interest rate changes associated with
the monetary policy rule and deviations from this rule. The sys-
tematic response of monetary policy to public information—by
definition—does not have information effects associated with it
and therefore will not lead to the “perverse” effects on output
discussed above. Figure IX contrasts the consequences of an un-
expected monetary shock with its associated information effect
 MONETARY NON-NEUTRALITY 1321

and the effects of a similarly sized change in interest rates that

comes about due to the systematic component of monetary pol-
icy and therefore does not have an information effect associ-
ated with it.27 The contrast is stark. Since our estimates im-

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ply considerable nominal and real rigidities, increases in inter-
est rates associated with the monetary policy rule reduce output
substantially.
This analysis makes clear that the information content of
a monetary shock matters in determining its effects. Most of
what the Fed does is anticipated by markets exactly because
it depends in a systematic way on public information. The ef-
fects of this systematic component of monetary policy are likely
to be more conventionally Keynesian than the effects of mone-
tary surprises that contain significant information effects and
are therefore a mix of the response to a conventional monetary
shock and the response to the nonmonetary news contained in
the Fed surprise.28 The effects of monetary surprises will also
vary depending on the amount and nature of the information
they convey. In the case of the Volcker disinflation, for exam-
ple, the narrative evidence suggests that few observers inter-
preted Volcker’s decision to raise interest rates as reflecting an
optimistic view of the economic outlook. To the extent that the
Volcker tightening was broadly interpreted as reflecting a differ-
ent loss function, or a greater degree of “conservatism” in dealing
with inflation, then this would have a small information effect.
Although we do not allow for any heterogeneity of this nature
in our model, this strikes us as an interesting avenue for future
research.

VI.B. Stock Price Effects

We finish the article with one additional piece of evidence that
sheds light on the information content of FOMC announcements.
Table V presents the response of stock prices to FOMC announce-

27. It is important to understand that our empirical results can be used to

think about both the effects of monetary policy shocks and changes in interest rates
that come about due to the systematic component of policy. In the linear models
we use, it does not matter why interest rates change (except for the information
effect). In other words, the comparative static of a given change in interest rates on
other variables is the same irrespective of the reason for the interest rate change
(except for the information effect).
28. This distinction is analogous to the “local average treatment effect” versus
“average treatment effect” distinction in applied microeconomics.
1322 THE QUARTERLY JOURNAL OF ECONOMICS

TABLE V
RESPONSE OF STOCK PRICES

Stock prices

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Response in the data −6.5
(3.9)
Response in the model
Baseline −6.8
[−11.5, −1.6]
No Fed Information effect −11.1
[−19.4, −2.5]

ments. A pure tightening of monetary policy leads stock prices to

fall for two reasons: higher discount rates and lower output. How-
ever, good news about future fundamentals can raise stock prices
(if higher future cash flows outweigh higher future discount rates).
In the data, we estimate that the S&P500 index falls by 6.5% in re-
sponse to a policy news shock that raises the two-year nominal for-
ward by 1%.29 This estimate is rather noisy, with a standard error
of 3.3%.
Table V also presents the response of stock prices to our mon-
etary policy shock in our estimated model.30 In the calibration of
our model where monetary policy announcements convey infor-
mation about both future monetary policy and future exogenous
economic fundamentals, stock prices fall by 6.8% in response to the
FOMC announcement. In contrast, if monetary policy is assumed
not to convey information about future exogenous fundamentals,
stock prices fall by 11%. The response of stock prices in the data is
thus another indicator that favors the view that monetary policy
conveys information to the public about future exogenous funda-
mentals.

VII. CONCLUSION
We use a high-frequency identification approach to estimate
the causal effect of monetary shocks. The monetary shocks that

29. Earlier work by Bernanke and Kuttner (2005) and Rigobon and Sack
(2004) finds large responses of the stock market to surprise movements in the Fed
funds rate.
30. For simplicity, we model stocks as an unlevered claim to the consumption
stream in the economy.
 MONETARY NON-NEUTRALITY 1323

we identify have large and persistent effects on real interest rates.

Real rates move essentially one-for-one with nominal rates several
years into the term structure. Contractionary monetary shocks

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lead to no significant effect on inflation in the short run and the
effect becomes significantly negative only several years into the
term structure. However, in sharp contrast with the implications
of standard monetary models, contractionary shocks raise expec-
tations about output growth.
We interpret the increase in expected output growth after a
monetary tightening as evidence of a Fed information effect. When
the Fed raises interest rates, this leads to increased optimism
about economic fundamentals. We develop a model of this Fed
information effect, in which the private sector interprets part of
an unexpected increase in the interest rate as information about
the natural rate. We estimate the model and find strong evidence
for both channels: the conventional monetary policy channel and
the information effect. One implication of our analysis is that the
information content of a monetary shock matters in determining
its causal effects.
 APPENDIX TABLE A.1
RESPONSE OF INTEREST RATES TO MONETARY SHOCKS FOR DIFFERENT SAMPLE PERIODS
1324

Baseline sample Precrisis (2000–2007) Full sample Baseline w/ unsched.

Nominal Real Nominal Real Nominal Real Nominal Real
3M Treasury yield 0.67 0.76 0.73 0.76
(0.14) (0.13) (0.15) (0.11)
6M Treasury yield 0.85 0.85 0.90 0.91
(0.11) (0.12) (0.12) (0.10)
1Y Treasury yield 1.00 1.00 1.00 1.00
(0.14) (0.14) (0.13) (0.13)
2Y Treasury yield 1.10 1.06 1.11 1.04 1.19 1.46 1.30 1.21
(0.33) (0.24) (0.36) (0.24) (0.29) (0.39) (0.37) (0.26)
3Y Treasury yield 1.06 1.02 1.03 0.97 1.21 1.34 1.26 1.18
(0.36) (0.25) (0.39) (0.25) (0.31) (0.33) (0.40) (0.28)
5Y Treasury yield 0.73 0.64 0.67 0.58 0.78 0.81 0.69 0.68
(0.20) (0.15) (0.20) (0.15) (0.18) (0.16) (0.16) (0.11)
10Y Treasury yield 0.38 0.44 0.36 0.44 0.50 0.57 0.38 0.43
(0.17) (0.13) (0.18) (0.13) (0.17) (0.16) (0.13) (0.10)
2Y Tr. inst. forward rate 1.14 0.99 1.07 0.90 1.31 0.97 1.34 1.15
(0.46) (0.29) (0.48) (0.27) (0.38) (0.30) (0.49) (0.31)
3Y Tr. inst. forward rate 0.82 0.88 0.66 0.76 1.14 1.09 1.00 1.03
(0.43) (0.32) (0.43) (0.29) (0.41) (0.35) (0.46) (0.34)
5Y Tr. inst. forward rate 0.26 0.47 0.20 0.47 0.44 0.61 0.27 0.45
(0.19) (0.17) (0.19) (0.16) (0.21) (0.21) (0.13) (0.12)
10Y Tr. inst. forward rate −0.08 0.12 −0.01 0.21 0.05 0.10 −0.07 0.04
THE QUARTERLY JOURNAL OF ECONOMICS

(0.18) (0.12) (0.19) (0.13) (0.17) (0.13) (0.12) (0.11)

Notes. Each estimate comes from a separate OLS regression. The dependent variable in each regression is the one-day change in the variable stated in the left-most column. The
independent variable is a change in the policy news shock over a 30-minute window around regularly scheduled FOMC announcements, except the last two columns where we include
unscheduled FOMC announcements. The baseline sample period is 1/1/2000 to 3/19/2014, except that we drop July 2008 through June 2009. The “precrisis” sample is January 2000
through December 2007. The “full sample” is 1/1/2000 to 3/19/2014. In the last two columns, we exclude a 10-day period after 9/11/2001. For two-year and three-year yields and real
forwards, the sample starts in 2004. For each sample period, we construct the policy news shocks from the same sample of observations as the regressions are run on, that is, the
results from the different sample periods use slightly different policy news shock series. Robust standard errors are in parentheses.

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 MONETARY NON-NEUTRALITY 1325

APPENDIX TABLE A.2

RESPONSE TO A FED FUNDS RATE SHOCK

Nominal Real Inflation

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3M Treasury yield 0.50
(0.16)
6M Treasury yield 0.59
(0.10)
1Y Treasury yield 0.41
(0.16)
2Y Treasury yield 0.48 0.50 −0.02
(0.32) (0.20) (0.15)
3Y Treasury yield 0.38 0.41 −0.03
(0.34) (0.19) (0.18)
5Y Treasury yield 0.11 0.21 −0.10
(0.16) (0.12) (0.09)
10Y Treasury yield −0.00 0.10 −0.10
(0.12) (0.09) (0.07)
2Y Treasury inst. forward rate 0.29 0.30 −0.01
(0.40) (0.20) (0.25)
3Y Treasury inst. forward rate 0.07 0.13 −0.06
(0.34) (0.19) (0.22)
5Y Treasury inst. forward rate −0.09 0.07 −0.16
(0.13) (0.12) (0.07)
10Y Treasury inst. forward rate −0.11 −0.02 −0.08
(0.16) (0.11) (0.09)

Notes. Each estimate comes from a separate OLS regression. The dependent variable in each regression is
the one-day change in the variable stated in the left-most column. The independent variable is a change in
the Fed funds future over the remainder of the month over a 30-minute window around the time of FOMC
announcements. The sample period is all regularly scheduled meetings from 1/1/2000 to 3/19/2014, except
that we drop July 2008 through June 2009. For two-year and three-year yields and real forwards, the sample
starts in January 2004. The sample size for the two-year and three-year yields and forwards is 74. The sample
size for all other regressions is 106. In all regressions, the policy news shock is computed from these same
106 observations. Robust standard errors are in parentheses.
1326 THE QUARTERLY JOURNAL OF ECONOMICS

APPENDIX TABLE A.3

RESPONSES TO POLICY NEWS SHOCK USING THE RIGOBON ESTIMATOR

Nominal Real Inflation

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3M Treasury yield 0.69
(0.15)
6M Treasury yield 0.85
(0.12)
1Y Treasury yield 0.98
(0.15)
2Y Treasury yield 1.07 1.03 0.05
(0.37) (0.29) (0.20)
3Y Treasury yield 1.03 0.99 0.04
(0.42) (0.30) (0.19)
5Y Treasury yield 0.69 0.62 0.07
(0.22) (0.16) (0.12)
10Y Treasury yield 0.34 0.42 −0.08
(0.19) (0.14) (0.09)
2Y Treasury inst. forward rate 1.10 0.96 0.14
(0.51) (0.34) (0.25)
3Y Treasury inst. forward rate 0.78 0.86 −0.08
(0.49) (0.37) (0.17)
5Y Treasury inst. forward rate 0.22 0.46 −0.24
(0.20) (0.18) (0.09)
10Y Treasury inst. forward rate −0.12 0.11 −0.22
(0.19) (0.13) (0.10)

Notes. Each estimate comes from a separate “regression.” The dependent variable in each regression is
the one-day change in the variable stated in the left-most column. The independent variable is a change in
the policy news shock over a 30-minute window around the time of FOMC announcements. All results are
based on Rigobon’s (2003) method of identification by heteroskedasticity. The sample of “treatment” days for
the Rigobon method is all regularly scheduled FOMC meeting days from 1/1/2000 to 3/19/2014; this is also
the period for which the policy news shock is constructed in all regressions. The sample of control days for
the Rigobon analysis is all Tuesdays and Wednesdays that are not FOMC meeting days over the same period
of time. In both the treatment and control samples, we drop July 2008 through June 2009 and 9/11/2001–
9/21/2001. For two-year forwards, the sample starts in January 2004. Standard errors are calculated using a
nonparametric bootstrap with 5,000 iterations.
 MONETARY NON-NEUTRALITY 1327

APPENDIX TABLE A.4

BREAKEVEN INFLATION VERSUS INFLATION SWAPS

Breakeven Swaps

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Inflation over next 2 years −0.02 0.37
(0.18) (0.35)
Inflation over next 3 years −0.03 0.41
(0.17) (0.32)
Inflation over next 5 years −0.13 −0.02
(0.14) (0.15)
Inflation over next 10 years −0.22 −0.17
(0.12) (0.16)

Notes. Each estimate comes from a separate OLS regression. The dependent variable in each regression is
the one-day change in expected inflation measured either by break-even inflation from the difference between
nominal Treasuries and TIPS (first column) or from inflation swaps (second column) for the period stated
in the left-most column. The independent variable is a change in the policy news shock over a 30-minute
window around the time of FOMC announcements, where the policy news shocks are constructed on the
2000–2014 sample used in Table I. The sample is all regularly scheduled FOMC meeting days from 1/1/2005
to 11/14/2012, except that we drop July 2008 through June 2009. Robust standard errors are in parentheses.

APPENDIX TABLE A.5

RESPONSE OF EXPECTED OUTPUT GROWTH

Exp. output growth in current qr 1.38

(0.72)
Exp. output growth 1 qr ahead 1.56
(0.54)
Exp. output growth 2 qr ahead 0.66
(0.37)
Exp. output growth 3 qr ahead 0.82
(0.27)
Exp. output growth 4 qr ahead 0.51
(0.25)
Exp. output growth 5 qr ahead 0.54
(0.28)
Exp. output growth 6 qr ahead 0.48
(0.25)
Exp. output growth 7 qr ahead 0.90
(0.57)

Notes. Each estimate comes from a separate OLS regression. We regress changes in survey expectations
from the Blue Chip Economic Indicators on the policy news shock. Since the Blue Chip survey expectations
are available at a monthly frequency, we construct a corresponding monthly measure of our policy news
shock. In particular, we use any policy news shock that occurs over the month except for those that occur
in the first week (because we do not know whether these occurred before or after the survey response). The
dependent variable is the change in the forecasted value of output growth N quarters ahead, between this
month’s survey and last month’s survey. See Online Appendix F for details. The sample period is all regularly
scheduled FOMC meetings between January 1995 to April 2014, except that we drop July 2008 through
June 2009 and the aforementioned first-week meetings. The policy news shock is constructed on the same
sample period. Sample sizes are 120 for the first five rows, then 75, 45, and 13. Robust standard errors are in
parentheses.
1328 THE QUARTERLY JOURNAL OF ECONOMICS

COLUMBIA UNIVERSITY

SUPPLEMENTARY MATERIAL

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An Online Appendix for this article can be found at The
Quarterly Journal of Economics online. Data and code repli-
cating the tables and figures in this article can be found in
Nakamura and Steinsson (2018), in the Harvard Dataverse,
doi:10.7910/DVN/HZOXKN.

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