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Federal Reserve Bank of Chicago
What Does Anticipated Monetary
Policy Do?
Stefania D’Amico and Thomas B. King
November 2015
WP 2015-10
What Does Anticipated Monetary Policy Do?
Stefania D’Amico
Thomas B. King
November 30, 2015
Abstract
Forward rate guidance, which has been used with increasing regularity by monetary policymakers, relies on the manipulation of expectations of future short-term interest rates. We identify shocks to these
expectations at short and long horizons since the early 1980s and examine their e¤ects on contemporaneous macroeconomic outcomes. Our
identi…cation uses sign restrictions on survey forecasts incorporated in a
structural VAR model to isolate expected deviations from the monetarypolicy rule. We …nd that expectations of future policy easing that materialize over the subsequent four quarters— similar to those generated
by credible forward guidance— have immediate and persistent stimulative e¤ects on output, in‡ation, and employment. The e¤ects are larger
than those produced by an identical shift in the policy path that is not
anticipated. Our results are broadly consistent with the mechanism underlying forward guidance in New Keynesian models, but they suggest
that those models overstate the persistence of the in‡ation response.
Further, we …nd that changes in short-rate expectations farther in the
future have weaker macroeconomic e¤ects, the opposite of what most
New Keynesian models predict.
Federal Reserve Bank of Chicago.
Contact:
stefania.damico@chi.frb.org,
thomas.king@chi.frb.org. Portions of this article originally circulated as part of our companion paper, "Policy Expectations, Term Premia, and Macroeconomic Performance." We
thank Jonas Arias, Bob Barsky, Je¤ Campbell, Matteo Iacoviello, Alejandro Justiniano,
Thomas Laubach, Leonardo Melosi, Daniel Rees, Min Wei, and conference and seminar
participants at the the Banque du France, the Federal Reserve Board, the Federal Reserve
Bank of Chicago, the University of Tor Vergata, and the Western Economic Association International for helpful comments and suggestions. The views expressed here do not re‡ect
o¢ cial positions of the Federal Reserve.
1
1
Introduction
Since short-term nominal interest rates in the United States and abroad reached
their e¤ective lower bound, monetary policy has increasingly utilized forward
rate guidance as a means to stimulate the economy. For example, from 2011
onward, the FOMC provided calendar dates through which it expected the
policy to be maintained, economic thresholds that it believed would warrant
maintaining the same policy, and statements signaling potential upcoming deviations from the conventional policy rule. While the active use of explicit
communication about the future path of the policy rate is novel for most central banks, its potency relies on a mechanism that, in principle, should always
be present— namely, agents’ current behavior should depend on their expectations about the future state of the economy, which in turn depend on the
expected con…guration of future interest rates. This paper attempts to measure the e¤ects of changes in short-rate expectations on the macroeconomy and
thereby assess the potential quantitative impact of forward-guidance policies.
The idea that expectations of future monetary policy can a¤ect today’s
economy is not new, and authors beginning with Krugman (1999) and Eggertsson and Woodford (2003) have argued that central banks can stimulate
the economy by committing to maintain a high level of policy accommodation
in the future. While this theory has been most explored in the context of
New Keynesian models (also see, e.g., Laseen and Svensson, 2011, and Werning, 2011), the central mechanism is common to a broad class of models in
which agents are forward looking and monetary policy is not neutral. Yet the
quantitative importance of this monetary-policy-expectations channel remains
an open question. Even within the context of New Keynesian models, alternative variations, such as those explored in Levin et al. (2010), McKay et al.
(2014), and Werning (2015), can cause the impact of forward guidance to differ substantially, and standard versions of such models deliver macroeconomic
e¤ects that are generally viewed as implausibly large (Carlstrom et al., 2012;
Del Negro et al., 2013).
But what is a plausible e¤ect of monetary-policy expectations? To date,
the most direct, model-free estimates come from Campbell et al. (2012), who
used data from surveys and interest-rate futures to examine how expectations
about the economy change when expectations about short-term rates change.
But those authors found the opposite of what theory predicts: when the Federal Reserve signals that lower rates are coming, survey expectations of GDP
and in‡ation decline. As they argued, this result likely re‡ects agents interpreting accommodative signals by the Fed as conveying negative information
about the prospects for the economy ("Delphic" forward guidance), rather
than as commitments to future stimulative deviations from the historical pol2
icy rule ("Odyssean" forward guidance). This same underlying phenomenon
extends beyond cases of explicit guidance by the Fed— any shock that lowers
short-rate expectations is good news if it re‡ects exogenously looser future
policy but bad news if it re‡ects an anticipated endogenous policy response
to a weaker economy. The presence of these two types of short-rate expectations shocks with potentially opposite impacts also creates an identi…cation
problem for other studies, such as Gurkaynak et al. (2005) and Gertler and
Karadi (2014), that examine how economic and …nancial variables respond to
shifts in monetary-policy expectations without making the distinction.
The absence of clear evidence on the macroeconomic e¤ects of monetarypolicy expectations leaves researchers with no benchmark for calibrating and
assessing their models of forward guidance, and it leaves policymakers with
little to go on when considering what type of forward guidance to articulate.
We …ll this gap by developing new measures of shocks to short-rate expectations and examining their dynamic e¤ects on macroeconomic variables. As in
Campbell et al. (2012), we use survey forecasts to capture the anticipated path
of short-term rates and the economy in a model-free way.1 However, as just
discussed, the surveys are not by themselves su¢ cient to distinguish whether
the expectations shocks are good news or bad news for the economy. We
therefore embed them in a structural VAR model that separates the sources of
forecast ‡uctuations. In particular, we use sign restrictions to ensure that we
have isolated shocks to anticipated future monetary policy: the expected short
rate must move in the opposite direction of expected in‡ation and expected
GDP. In nearly all theoretical models, this directional pattern of changes in
macro variables is unique to monetary policy shocks. For example, changes in
short-rate expectations that arise from the anticipated endogenous response
of policymakers to economic shocks (such as those associated with Delphic
forward guidance) cause expected short rates to move in the same direction
as expected in‡ation or expected GDP, so our identi…cation scheme strips out
these systematic policy responses.2
We …nd that when survey respondents anticipate a monetary policy easing
over the subsequent year (controlling for past macro data and the current
policy stance), this leads to an immediate and persistent increase in both prices
1
Indeed, if theories of forward guidance are correct, there is little hope of measuring its
e¤ect without direct measures of expectations, since, when economic agents’ anticipations
matter and are not included in the econometrician’s information set, structural policy shocks
cannot generally be recovered by econometricians (Leeper et al., 2013; Milani and Treadwell,
2012).
2
The title of our paper is a homage to Leeper et al. (1996), which was among the
…rst studies to use a VAR to grapple with an analagous identi…cation problem— how to
separate the response of monetary policy to the economy from the response of the economy
to monetary policy.
3
and output. For example, a decrease of 25 basis points in expectations for the
average short-term rate over the next year, holding all else constant, results
in a short-run increase in both GDP and the price level of about 1 percent.
These e¤ects occur much faster than those of a conventional monetary-policy
shock, which we identify in the same VAR. After about two years, a given shock
to policy expectations has about the same e¤ect on output as a conventional
policy shock of the same size and an e¤ect on in‡ation that is 2 to 3 times
as large. Shocks to expectations beyond the one-year horizon still have e¤ects
in the same direction, but they are smaller, less persistent, and not always
statistically signi…cant.
Forward rate guidance can be viewed as a combination of an immediate
shock to the expected short rate and subsequent shocks to the actual short
rate. This is because, after a future deviation from the policy rule is successfully communicated to agents, the central bank must introduce a series of
innovations to the short rate in order to keep it at its promised level. We
therefore use our results to build a policy experiment that simulates credible,
Odyssean forward guidance. In particular, we analyze how the economy responds when there is a shock to the expected path of the policy rate, followed
by a series of conventional policy shocks that make the initial change in shortrate expectations materialize as anticipated. Our results suggest that such a
pre-announced reduction in the policy-rate path over the one-year horizon results in a signi…cantly larger and more-rapid stimulus than the same reduction
in the path does when it is not anticipated. Again, this di¤erence is much
smaller when applied to longer-horizon expectations.
These results on the e¤ectiveness of anticipated monetary-policy changes
qualitatively support the basic mechanism underlying forward guidance in New
Keynesian models— expectations for unusually accommodative policy in the
future boost the macroeconomy today. In the next section, using a simple
New Keynesian model under a standard calibration we show that an anticipated 25-basis-point cut in the short rate over the coming year raises today’s
output gap and annual in‡ation rate by about 1:5 and 3:5 percentage points,
respectively. These magnitudes are in the ballpark of what our empirical
results suggest. Importantly, however, our results are substantially at odds
with New Keynesian predictions in other respects. First, we …nd that the effect of forward-guidance on in‡ation is short lived, essentially taking the form
of a one-time jump in the price level. In contrast, most theoretical models
obtain an in‡ation response that is persistent and often even growing larger
over time; consequently, they predict a much larger cumulative e¤ect on prices
than we …nd in the data. Second, New Keynesian models typically predict
that forward-rate guidance should have a larger e¤ect on current outcomes
when it pertains to horizons that are farther in the future (see, e.g., Carlstrom
4
et al., 2012). Our results suggest the opposite: the e¤ect of shocks to policy
expectations is smaller when the expectations involve more-distant periods.
In this sense, we quantify the forward-guidance puzzle discussed in Del Negro
et al. (2013).3
Our use of direct measures of beliefs relates to several di¤erent branches
of the literature. First, a number of recent papers show that augmenting
VARs with survey forecasts of the nominal short rate, real GDP growth, and
in‡ation leads to a signi…cant improvement in the …t of the term structure
and in forecasting yields out of sample (e.g., Chun, 2011, and Orphanides and
Wei, 2012), as well as to a better understanding of the expectation component
of interest rates (Kim and Orphanides, 2012; Piazessi et al., 2015). Second,
a growing body of work studies the impact of "news" shocks on macroeconomic ‡uctuations by incorporating various forward-looking measures either
into reduced-form analysis (Beaudry and Portier, 2006; Romer and Romer,
2010; Ramey, 2011; Barsky and Sims, 2012; Leduc and Sill, 2013; Barsky,
Basu, and Lee, 2014) or into DSGE models (Milani and Rajbhandari, 2012;
Miyamoto and Nguyen, 2014). These studies are mostly concerned with the
analysis of anticipated changes to future …scal policy or nonpolicy fundamentals such as total factor productivity. We focus on changes in expectations
about future monetary policy, but we discuss in detail how these changes—
and forward guidance in particular— can in some cases be interpreted as a
type of news shock. As noted above, Campbell et al (2012), Gurkaynak et
al. (2005), and Gertler and Karadi (2014) have also considered the e¤ects of
monetary-policy expectations, but those papers have not distinguished expectations for exogenous policy shocks from expectations for endogenous policy
responses to economic conditions, nor have they considered di¤erential e¤ects
of expectations about di¤erent horizons.
In Section 2 of the paper, we …rst describe a process of expectations formation that is general enough to accommodate various forms of deviations
between subjective and statistical beliefs encountered in the literature, and we
then motivate our identi…cation strategy by embedding these expectational
deviations in a simple New Keynesian model. In Section 3, we discuss the details of our survey-augmented VAR. Section 4 summarizes the baseline results
and discusses in depth the causality of monetary-policy expectations shocks.
In Section 5, we use these results to construct monetary policy experiments
similar to forward rate guidance and analyze its impact. Section 6 conducts
a battery of robustness checks, and Section 7 concludes the paper.
3
On the other hand, our results could be consistent with modi…cations of the standard
model, such as Milani and Treadwell (2012) and McKay et al. (2015), that give forward
guidance its most powerful e¤ects at relatively short horizons.
5
2
Modeling Shocks to Expectations
2.1
Subjective expectations: A general framework
Since, in our empirical work, we intend to identify shocks to monetary-policy
expectations and examine the response of the economy to those shocks, we
need to clarify what we mean by "shocks to expectations." In order to talk
meaningfully about such shocks, it must be the case that expectations can contain some exogenous component that is not related to observed fundamentals.
In other words, we have to allow for the possibility that subjective beliefs (in
our case, measured by survey forecasts) might deviate from statistical beliefs
derived only from historical data (e.g., Piazzesi et al., 2015).
The literature contains a variety of structures in which agents form expectations in a way that di¤ers from the full-information, rational-expectations
("objective") statistical belief. One set of models relaxes the rationality assumption. For example, in Bullard, Evans, and Honkapohja (2008), agents’
forecasting models include a "judgement" term that is tacked on the objective
forecast. In Milani (2011), a similar term is motivated by agents’ di¤ering
degrees of pessimism and optimism. Indeed, most notions of "market sentiment" or "animal spirits" can be modeled in this way. A second set of models
maintains rationality, but allows agents’information sets to di¤er from those
of the econometrician. Agents may use less information than statistical beliefs would imply, either because information is "sticky" (as in Mankiw and
Reis, 2002), or because they face noisy information (as in Lucas, 1972; Woodford, 2002; and Sims, 2003). Recent work by Coibion and Gorodnichenko
(2015) indeed shows that forecast revisions have a strong predictive power for
ex-post mean forecast errors across di¤erent variables in various surveys, supporting this class of models with information rigidities. Alternatively, agents’
information sets may be larger than those of the econometrician. In models
of "news" in the sense of Barsky and Sims (2012) or perfect foresight as in
Leeper et al. (2013) the exogenous shift in expectations is due to information
about changes in future fundamentals that is not directly re‡ected in the current state vector. Forward rate guidance may be thought of as an example
of news: agents anticipate changes in future monetary policy as a result of
explicit central bank communication aimed at signaling upcoming deviations
from the historical policy rule.4
Since we want to remain agnostic about the speci…c process governing
4
See for example the following sentence in 2014 and 2015 FOMC statements: "The Committee currently anticipates that, even after employment and in‡ation are near mandateconsistent levels, economic conditions may, for some time, warrant keeping the target federal
funds rate below levels the Committee views as normal in the longer run."
6
information ‡ows and beliefs formation, we require a framework that is general
enough to encompass all of these possibilities. To that end, we consider models
of the form
(1)
xt = Axt 1 + BEtS [xt+1 ] + "t
where A, B, and are parameter matrices, "t is a vector of iid "fundamental"
shocks with mean zero and identity covariance matrix, and EtS xt+1 denotes
agents’"subjective expectation" of the 1-period-ahead state vector. Expectations are subjective in the sense that may deviate from the statistical expectation that an agent or an econometrician with knowledge of both the time-t
state and the model parameters would compute.
We assume that equation (1) satis…es the conditions for invertibility, that
agents have knowledge of the structure of the economy in (1), including the
values of A, B, and , and that the operator EtS obeys the law of iterated
S
expectations (i.e., EtS Et+h
[xt+j ] = EtS [xt+j ] forj > h > 0). Under these
assumptions, (1) can be rewritten as
xt =
1
X
l xt l
+
1
X
h
EtS ["t+h ] + "t
(2)
h=1
l=1
where the matrices l and h are reduced-form combinations of A, B, and .
If agents’information set consists of the history of the state fxt ; xt 1 ; :::g and
subjective expectations are rational, then EtS ["t+h ] = 0 for all h > 0.
To move beyond the full-information, rational-expectations benchmark, we
allow subjective expectations of future fundamental shocks to follow the general linear process:
EtS
["t+h ] =
1
X
Clh xt l
+
l=1
1
X
Dlh EtS l ["t+h ] + F h "t +
h
t
(3)
l=1
where Clh , Dlh and F h are coe¢ cient matrices and ht is an iid random vector
representing shocks to expectations. (Given equation (2) and the assumption
that agents know the structural parameters, characterizing EtS ["t+j ] for j =
1; :::; h is su¢ cient to uniquely characterize EtS [xt+h ].) We assume that ht is
independent of "t for all h. However, to allow for the possibility that agents
may receive informative signals about the future, we do not restrict ht to be
independent of "t+h for h > 0. In particular, we can write
h
t
=
h
"t+h + uht
where uht is independent of all leads and lags of "t , that is, is an erroneous
signal about the state of the economy.
7
This framework encompasses a number of interesting special cases. For example, since expectations of future fundamental shocks are formed as arbitrary
linear functions of their own lags, previous state values, fundamental shocks,
and noise, equation (3) can be consistent with a variety of belief-formation
processes, including certain models of learning and sticky information. However, we are particularly interested in two important cases:
1. News. If h 6= 0 for some h, then agents e¤ectively receive informative
signals (i.e., news) about period t + h in period t. The most intuitive
case is h = 1, which implies that the signal is unbiased. This setup is
consistent with how forward guidance about monetary policy has been
modeled in Laseen and Svensson (2011), Campbell et al. (2012), and
Del Negro et al. (2013).
2. Noise. If var[uht ] > 0 for some h, then agents’ beliefs are subject to
random ‡uctuations that do not correspond to any past, current, or
future fundamental shocks. This type of information ‡ow is consistent
with models of sentiment or judgment. It is also consistent with models
in which, for whatever reason, agents pay attention to a signal that has
no economic content.
There are two relevant observations to make about the e¤ects of expectations shocks that are news versus those that are noise. First, note that if
both news and noise are potentially present at some horizon (i.e., h 6= 0 and
var[uht ] > 0), they are observationally equivalent to agents and to econometricians in periods t through t + h 1. In other words, since expectations
only involve the term ht (but not its exact decomposition), and since this term
responds equally to both news and noise (up to the scaling factor h ), both
types of shocks have the same e¤ect on EtS ["t+h ] and therefore on xt . Only
in period t + h does a di¤erence emerge, because in that period the shock
"t+h is realized in equation (2) and agents infer how correct the initial signal
was. Second, although the path of the economy may di¤er for news and noise
shocks after period t + h 1, the portion of this path that is caused by the
change in expectations is the same for both shocks. The di¤erence between
the two paths results only from the direct e¤ect that the actual materialization
of the shock has on the economy, that is,
in (2), and not from the e¤ect
h
h
( ) that t has on the economy through the expectation term within the
same equation.
Thus, although the impulse-responses di¤er between the news and the noise
cases, the extent to which beliefs cause ‡uctuations in the state is the same.
8
This implies that, while for noise shocks, the entire impulse-response function
is caused by the expectation shock, for news shocks, we must subtract o¤
the response to the fundamental shock that would have occurred even if such
shock had not been anticipated— namely, the e¤ect following from "t . Fortunately, in the particular case of monetary-policy VARs, we have an estimate
of this e¤ect, since it is precisely the impulse-response of the economy to a
(conventional) unanticipated monetary-policy shock.
We dwell on this issue because it will be important for us in two ways.
First, we do not need to separately identify news and noise shocks in order
to determine how the economy changes following a change in expectations.
Indeed, as noted by Blanchard et al. (2013), it is generally impossible to do
so because of the observational-equivalence problem. Instead, our empirical
exercise will identify the random variable ht , without attempting to decompose
it into "t+h and uht . Of course, as just discussed, the extent to which our
estimates of the economic response to ht can be interpreted as causal will
depend on its news/noise composition. However, in practice, we …nd that
our inference about the causal e¤ects of expectations under the assumption
that ht = uht (expectations shocks are all noise) is not much di¤erent from our
inference under the assumption ht = "t+h (expectations shocks are all news).
We discuss this result further in Section 4.2.
Second, since the path of the economy following the revelation of news
about "t+h is equal to the path that is caused by the change in time-t expectations plus the path that would have arisen if "t+h had been realized (in
period t + h) without having been anticipated, one can construct the response
of the economy to the revelation of news by adding together the appropriately
weighted impulse-response functions for these two shocks. In our particular
case, this means that we can construct forward-guidance scenarios by a combination of expectations shocks and conventional policy shocks. A situation
in which agents receive surprising forward guidance at time t (assuming it is
credible) is equivalent to a situation in which their time-t short-rate expectations change exogenously and then subsequent shocks to the actual short rate
occur such that the initial change in expectations turn out to be correct. We
will have estimates of how the economy responds when there are shocks to
both the expected and actual short rates, so simulating the e¤ects of forward
guidance as the sum of these estimated responses is straightforward.
2.2
Expectations Shocks in a New Keynesian Model
We now consider what happens when we allow for exogenous ‡uctuations in
subjective expectations of future monetary policy in an otherwise standard
9
New Keynesian (NK) model. The purpose of this exercise is threefold. First,
it illustrates in a familiar setting the mechanism underlying the impact of the
expectations shocks mentioned above. Second, it demonstrates the qualitative
and quantitative responses to such shocks implied by NK models, providing
hypotheses (such as the so-called forward-guidance puzzle) to be tested in our
empirical work. Lastly, it illustrates how the sign restrictions that will be
used to identify monetary-policy expectations shocks in our VAR are speci…ed
by the theory.
We borrow the basics of the model from Gali (2008). Speci…cally, under standard NK assumptions, the equilibrium conditions can be written as
follows:
S
(4)
t = Et t+1 + yt
1
yt = EtS yt+1
it
EtS
t+1
r
(5)
where yt is the output gap, r is the natural rate of interest, 0 < < 1 is
the rate of time preference, > 0 is the coe¢ cient of relative risk aversion,
and > 0 is a nonlinear combination of structural parameters. In addition,
assume that the short-term interest rate is set by the central bank according
to the rule
it = y yt +
(6)
t+ t
where
t
=
t 1
+ "t
(7)
with "t being a mean-zero iid shock, and
0, y 0; and 0
1.
We depart from the standard treatment only by assuming that expectations
are formed under the subjective measure de…ned in equation (3) of the previous
section. However, to keep the exposition simple, we assume that C, D, and
F are all equal to zero— that is, EtS "t+h = ht . This speci…cation is similar in
some respects to models of beliefs shocks (news or noise), as, for example, in
Schmitt-Grohe and Uribe (2012) and Lorenzoni (2009).5 In the special case
in which the variance of ht is zero for all h, it reduces to the standard NK
model.
To see how monetary-policy expectations shocks a¤ect the current output
gap and in‡ation in this model, note that the model can be solved forward to
obtain a solution for time-t in‡ation and output as a function of the current
policy stance t and an in…nite-order moving average of expected future shocks:
5
Milani and Treadwell (2012) also present a related DSGE analysis in which monetary
policy shocks may be anticipated by agents. Although the details of their model di¤er from
those here (for example, they incorporate habit formation), they also …nd that the e¤ects
of the shocks to anticipated future policy are larger than those to current policy.
10
t
=
0
t
+
1
X
h
EtS "t+h
0
=
t
+
yt =
0
y t
+
h h
t
h=1
h=1
1
X
1
X
h S
y Et "t+h
0
y t
=
+
1
X
h h
y t
h=1
h=1
The multipliers can be found as follows. First, note that the e¤ect of
an unanticipated monetary-policy shock "t , which is given by the multiplier
on t , is the same as in the usual case. In particular,
0
0
y
0
i
= [(1
=
(8)
=
(1
)
) (1
)
(9)
(10)
]
1
where =
(
) + (1
) y + (1
)
. (See Gali, 2008, for a
0
0
derivation.) Since
and y are necessarily negative for admissible values
of the structural parameters, time-t in‡ation and output move in the opposite
direction of the monetary-policy shock "t . Under standard parameterizations,
0
i is positive, implying that the nominal short rate moves in the same direction
as the shock, and we will assume for the remainder of the discussion that this
is the case.6 One can show that the multipliers for shocks to expectations
about horizon h > 0 are given by the recursion:
h 1
h
h
y
=R
(11)
h 1
y
where
R=
+
y
+ (1
)
1
(1
)
To take an example, consider how an economy that is initially in the steady
state ( t = 0) responds at time t to a shock to monetary-policy expectations
one period ahead, 1t . First note how expectations of the state variables
themselves react in the period of the shock:
EtS t+1 =
EtS yt+1 =
EtS it+1 =
6
0 1
t
0 1
y t
0 1
i t
The expected real interest rate, rt = it Et t+1 ; necessarily moves in the same direction
as the shock, with 0r = [(1
) (1
) ] . This implies a slightly di¤erent set of sign
restrictions than what we will use for our baseline empirical model, which we consider as
robustness checks.
11
Intuitively, expectations of a future monetary-policy change have on expectations of future in‡ation, output, and interest rates the same impact that
current policy shocks have on current in‡ation, output, and interest rates.
That is, a shock to future policy expectations causes both EtS t+1 and EtS yt+1
to move in the opposite direction of EtS it+1 . This observation motivates our
sign-based identi…cation scheme. Notably, no other shock in standard models of this type can produce this response pattern. For example, a shock to
expectations about future technology, which would enter through r , would
generally move the short rate in the same direction as expected output and
in‡ation. A "markup shock," which would appear as an additional stochastic
term in equation (4), would generally move in‡ation and output in opposite
directions.
From (11), the responses of time-t in‡ation and output to the expectations
shock are:
t
=
yt =
1 1
t
1 1
y t
=
= (1
+
y
)
+ (1
0
+ (1
0
)
)
0
y
+
0
y
1
t
1
t
So long as
> , the time-t response of in‡ation to a monetary-policy expectations shock in t + 1, 1 , has the same sign as the change in expected t + 1
in‡ation, 0 . (I.e., an expected tightening of policy results in lower in‡ation
both today and tomorrow.) This e¤ect represents the impounding of future
expected in‡ation into today’s prices. On the other hand, the sign of 1y is
ambiguous. If the central bank does not respond too aggressively to in‡ation,
then it is negative— lower expected interest rates cause a rise in output today
as consumers attempt to smooth out the anticipated boost in future income.
But if
is large, the central bank’s response to the higher time-t in‡ation
can more than o¤set this smoothing e¤ect and result in a decrease in time-t
output. Beyond the one-period horizon, the signs of both responses will depend on the speci…c parameterization, and they may increase or decrease in
magnitude as the horizon increases. Thus, even within the context of this
very stylized NK model, there is no clear prediction for which direction the
e¤ects of expectations shocks should go, let alone for their size. The response
of the economy depends on the central bank’s behavior.7
To get a sense of the magnitudes associated with these shocks and how the
responses vary across the expectational horizon, Figure 1 illustrates how the
state of the economy responds in a typical calibration to expectations shocks
7
A particularly relevant case is that, at the ZLB, small changes in in‡ation do not result
in any change in the interest rate, so that
is e¤ectively zero. Thus, at the ZLB the
smoothing e¤ect always dominates, and accommodative shocks to future expected policy
always raise current output.
12
that are 1 to 4 periods ahead. Taking periods to be quarterly (and again
following Gali, 2008), let
= 1,
= :99,
= :15, y = :125,
= 1:5,
0
0
0
= :32, y = 1:08, and i = :38.) The
and = :5. (In this case,
h
impulses are shocks t that are su¢ cient to lower the expected h-period-ahead
annualized interest rate by 25 basis points, where h = 1; :::; 4: Given the
calibration, this translates into an expected "t+h equal to 0:17 basis points
(under the subjective measure).
We …rst consider the case in which ht consists purely of noise ( ht = uht ).
That is, once period h arrives, there is no actual policy shock. As shown in
panel A of the …gure, in‡ation rises immediately in response to the expectations
shocks, and it rises by more the farther in the future the shock to the short
rate is expected to occur. For an anticipated monetary easing of 25 basis
points one year ahead (the blue line) current quarterly in‡ation rises by about
3 percent (at an annual rate). As noted, this e¤ect is somewhat damped
because of the systematic response of policy. This reaction is re‡ected in
higher nominal and real short-term rates. For this calibration, the policy
response is large enough to drive the output gap negative in early periods,
even though the expectations shock itself is a stimulative one. Further, since
no fundamental shocks actually materialize in these examples, and since the
subjective expectational errors are assumed not to be persistent, the state
always returns to zero once period h has passed.
Panel B illustrates the case in which ht consists of news. That is, ht = "t+h .
As noted in the previous section, the response of the economy to news shocks is
the same as its response to noise shocks up until period t + h. After that time,
the economy receives the additional stimulus that it would have received from
an unanticipated monetary policy shock of 25 basis points at time t + h. This
case is one that could, in principle, result from credible forward guidance—
agents are made to believe that a policy shock will occur in the future, and
then it actually does. However, if the path of the economy shown in this
panel resulted from deliberate policy, the central bank’s behavior would be
somewhat bizarre. As in panel A, the systematic response of monetary policy
causes nominal and real rates to rise in the near term when a future easing
is anticipated; thus, the central bank mechanically …nds itself raising rates in
response to its own forward rate guidance. While there is nothing logically
inconsistent about this outcome, it seems unrealistic that a central bank would
announce an unusually accommodative future policy only to o¤set part of that
accommodation now by adhering to its usual rule.
We therefore consider a more realistic forward-guidance scenario, in which
the central bank maintains nominal rates at their time-t 1 levels until period
t + h, at which point it adopts the pre-announced change. To achieve this
outcome, it must introduce additional monetary policy shocks in each period
13
t; :::; t + h 1 in order to o¤set its own systematic response to the economy
and keep the short rate at its initial level. We assume that these additional
policy shocks are also anticipated at time t by agents. (I.e., agents correctly
believe that the path of the short rate will be unchanged until period t + h.)
Panel C shows the impacts in the theoretical model. Without the short-term
policy o¤set to the expected future easing that was present in panel B, the
output gap now rises substantially. Given the stabilized nominal short rate,
the higher in‡ation results in a signi…cant downward movement in real rates.8
Finally, in panel D, we show what happens if the central bank promises
to lower the short rate by 25 basis points, not just in period t + h, but for
the entirety of the period t + 1 through t + h. This is closer to what central
banks have done in practice, and it essentially mirrors the forward-guidance
experiments we will conduct with our empirical results in Section 5. Perhaps
surprisingly, the responses are only slightly greater than in panel C. The
reason is that, in the forward-guidance scenario, most of the e¤ect derives
from the large short-rate shock that is needed in the …rst period to keep rates
from rising. The size of that shock primarily depends on the timing and
magnitude of the change in expectations that is farthest in the future, which
is the same in both panels C and D.
The magnitudes of the responses in Figure 1 are large when compared to
those of conventional monetary policy shocks within the same model. For
example, an unanticipated shock to the actual short rate has an initial impact
of only +0:2 percentage points on both the in‡ation rate and the output gap—
an order of magnitude smaller than when the same shock is anticipated to
occur a year in the future. These are manifestations of the "forward guidance
puzzle" pointed out by del Negro et al. (2013). They are not speci…c to
the structure of the simple model here, its calibration (within reason), or its
assumed policy rule. Rather, as discussed by McKay et al. (2015), they result
from the large in‡uence of future interest rates on the path of the output gap
and the way that path compounds into in‡ation via the NK Phillips Curve.
While some authors, such as Kiley (2014b) and McKay et al. (2015), have
proposed modi…cations to the basic NK structure that can reduce the e¤ect
of forward guidance, it is unclear what a reasonable result from such models
8
The output gap is much higher in panel C than in panel B, but in‡ation is only slightly
higher. The reason is subtle. Since the short-rate shocks that occur in t through t + h 1
in panel C are persistent, less of an expectations shock is needed to achieve a given value
of EtS vt+h than was needed in panel B. For in‡ation, the e¤ect of the short-rate and
expectations shocks is about the same, so trading o¤ one for the other makes little di¤erence.
But for the output gap, negative shocks to the short rate have a positive e¤ect, whereas
negative shocks to the expected short rate mostly have a negative e¤ect (as shown in panels
A and B). Consequently, the shift in the source of the shocks provides a large positive
boost.
14
should be, since there is no empirical work estimating this impact in a modelfree way. Our results below provide some benchmarks along these lines.
3
The Survey-Augmented VAR
3.1
Speci…cation and Data
Returning to the more general model of Section 2.1, note that we can write
equations (2) and (3) in the reduced form
xt =
1
X
11
l xt l
+
l=1
EtS [xt+h ] =
1 X
1
X
12 S
h;l Et l
[xt+h ] +
x
t
(12)
h=1 l=1
1
X
21
h;l xt l
+
1
X
22 S
h;l Et l
[xt+h ] +
E
t
(13)
l=1
l=1
This sets up a VAR system, which will be the target of our estimation. It will
be useful to divide the vector xt into macroeconomic variables that we will
assume cannot respond contemporaneously to conventional monetary-policy
shocks (x1t ), those that potentially can (x2t ), and the short rate itself (it ).
0
We can stack the data in the vector Xt = x1t it x2t EtS [xt+h ] , where
EtS [xt+h ] is the subjective measures of expectations about the macro data at
horizon h. Also let t be a corresponding stacked vector of the reduced-form
0
=
. We rewrite the system as
errors, xt and E
t , with covariance matrix
Xt =
(L)Xt +
t:
(14)
(We make the usual assumption that the in…nite sums in the above system can
be well approximated by a VAR process of low order, which we will determine
using information criteria.)
To measure agents’subjective expectations, we use survey data from both
the Survey of Professional Forecasters (SPF) and the Blue Chip Survey (BCS).9
The principal advantages of the SPF data are that they begins in 1981 (the
year when the three-month Treasury bill rate forecast becomes available) and
are reported at a consistent quarterly frequency. However, the longest available forecasting horizon in these data is one year ahead. The BCS data, by
contrast, include forecasts of up to 11 years in the future, but they do not
begin until 1983 and for some forecasting horizons are reported only twice a
9
We obtained the SPF data from the Federal Reserve Bank of Philadelphia’s website.
15
year at a slightly irregular interval. Each survey reports the respondents’average forecasts of GDP growth, CPI in‡ation, and the three-month Treasury
bill (3-month T-Bill) rate, which we use as a proxy for the monetary-policy
instrument. For the one-year horizon, where both surveys are available, we
obtain VAR results that are quite similar whether we use one survey or the
other. Therefore, in our baseline speci…cations, we focus on the BCS, leaving
the SPF results as a robustness check.
Due to idiosyncrasies in the conventions and timing of their reporting,
the survey data from both sources require some manipulation to be useful in
our VAR model. Our method for obtaining constant-horizon quarterly series
from these data is described in detail in the appendix. The three panels
of Figure 2 plot the resulting time series of the survey-based expectations of
the average 3-month T-Bill rate, CPI in‡ation, and GDP growth over the
next year. The projections of the 3-month T-Bill rate and CPI in‡ation are
very similar between the two surveys. In the case of GDP growth, on the
other hand, the SPF projections are more volatile and, at least through about
the year 2000, more pessimistic than the BCS projections. Miyamoto and
Nguyen (2014) compare the SPF data not only to the BCS forecasts but also to
the Consensus Forecasts and the Fed’s Greenbook forecasts and also conclude
that they are similar. These …ndings are comforting because, if survey data
on expectations agree across a large set of forecasters, it is more likely that
they represent the actual expectations of agents in the economy. Figure 3
illustrates the properties of the term structure of BCS forecasts for the same set
of variables (3-month T-Bill, CPI in‡ation, and GDP growth) by plotting their
time series at the one-, six-, and 11-year horizon. Shorter-term expectations
(blue lines) display much more variation than longer-term expectations, and
there is very little di¤erence between 6- and 11-year projections (red and green
lines, respectively). These results are consistent with the stylized fact that it
is di¢ cult to forecast economic variables far in the future.
Apart from the inclusion of the survey data, the speci…cation of our baseline
VAR model is similar to others in the literature. Speci…cally, we build loosely
on Christiano, Eichenbaum, and Evans (2005) in our choice of macro variables.
In x1t we include log GDP, log CPI, and log labor productivity.10 In x2t we
include log real pro…ts and the M2 growth rate. (To conserve degrees of
freedom, we omit a few variables— consumption, investment, and wages— that
were of speci…c interest to Christiano et al. (2005) but are nearly collinear with
the other variables in the VAR.) We also include a long-term Treasury yield in
x2t , with a maturity corresponding to the horizon of the survey expectations
used in each model speci…cation, in order to capture any e¤ects of expected
10
We will also use the results to discuss employment, which of course can be calculated
as the di¤erence between output and productivity.
16
short rates that operate through longer-term spot rates.
Overall, we found that, although the basic sign and magnitude patterns
of our results were consistent across speci…cations, the choice of lag length
mattered in some cases for the con…dence bounds around the results. In our
baseline model, we use the BIC to select lag length. The robustness checks in
Section 6 consider some alternatives to this speci…cation, including di¤erent
choices of regressors and lag length and estimation using Bayesian methods.
A necessary condition for our exercise to work is that the VAR to must do
a better job of forecasting the economic variables than the surveys do. There
are two reasons for this requirement. First, as with virtually any VAR, an
implicit assumption is that the estimated model closely approximates the true
probability model of the economy. If the surveys performed better than the
VAR, that would call into question the speci…cation. Second, we need the
survey-based expectational deviations to be non-negligible, because otherwise
they would add little information to the VAR and produce a poor identi…cation
of the anticipated changes to policy. These two conditions jointly imply that
the survey expectations must not be more accurate on average— and preferably
not nearly as accurate— as the expectations based on the survey-augmented
VAR.
Table 1 shows that the VAR forecasts are indeed much more accurate than
the survey forecasts by comparing root mean-squared errors. For example,
the BCS has errors about 60% larger than those of the VAR when it comes
to forecasting the 3-month T-Bill rate one year ahead. At long horizons,
the RMSEs of the survey data are 1.8 to 6.5 times as large as those of the
VAR. Of course, the VAR has several advantages over the survey data, in
that it is using ex post data, forecasting in-sample, and including information
from the surveys themselves. If we were running a horse race between the
two forecasts, this comparison would not be fair. (On the other hand, it
is not strictly guaranteed that the VAR must do better, since it minimizes
one-step-ahead errors, and these are multi-step forecasts.) But the purpose
of the survey-augmented VAR is not to attempt to mimic real-time forecasts,
but rather to come as close as possible to the true probabilities. It seems
clear that the VAR does a considerably better job than the surveys in this
regard. Consequently, we are comfortable interpreting di¤erences between
the VAR forecasts and the survey forecasts as re‡ecting expectations shocks
in the sense of Section 2.
17
3.2
Identi…cation of structural shocks
We will distinguish three types of structural shocks, conventional (unanticipated) monetary-policy shocks (et ), policy-expectations shocks ( ht ), and a
vector of other shocks that we leave unspeci…ed (other). The contemporaneous coe¢ cients can be partitioned conformably with Xt :
1
0 x1
x1
x1
B
B
=B
@
e
i
e
x2
e
E S [x]
e
h
i
h
x2
h
E S [x]
h
other
i
C
other C
x2
C
other A
E S [x]
other
(15)
where, for example, xe 1 is a vector containing the response of the block x1t
to the standard monetary-policy shock and i h is a scalar representing the
contemporaneous response of the policy rate to the policy-expectations shock.
(Elements of the
matrix highlighted in bold represent vectors, while the
others are scalars.)
In order to identify the elements of , we impose a combination of exact
and partial identi…cation restrictions. As noted, we assume that x1t does not
respond contemporaneously to standard monetary-policy shocks, providing us
with a set of short-run exclusion restrictions, as are common in the monetarypolicy VAR literature. The exact restrictions then amount to:11
x1
e
=0
(16)
Expectations shocks are identi…ed by drawing from the space of possible
matrices that satisfy the exclusion restrictions just described and discarding
all draws that do not satisfy sign restrictions on the contemporaneous impacts
on the surveys. Those sign restrictions— intended to capture changes in expectations about future monetary policy— enforce the following condition: the
time-t impact on the survey forecast of the average T-Bill rate over periods t
to t + h must be in the opposite direction of the impact on the survey forecast of the time-t + h GDP and price level. To ensure that the expectations
shocks are strongly identi…ed from our conventional shocks, we also impose
that the contemporaneous T-Bill rate does not move in the same direction
as the forecasted T-Bill rate in response to an expectations shock. These
assumptions about the contemporaneous impacts of expectations shocks are
consistent with the predictions of the NK model discussed earlier and, indeed,
with a large class of forward-looking macroeconomic models. As a normalization, we consider expectations shocks that move expected short rates in
11
Note that we do not require the other half of the usual short-run restriction: policy can
respond contemporaneously to all of the macro variables and expectations.
18
the negative direction (i.e., expectations for future policy easing). Thus, the
partial restrictions amount to:
E S [i]
n
h
ES [ ]
h
;
E S [y]
h
;
i
h
o
< 0
(17)
> 0
Note that the way we have identi…ed our expectations shocks rules out
two potential sources of contamination. First, one might be concerned that
we are erroneously picking up conventional monetary-policy shocks in time t:
perhaps the Fed is lowering rates today, leading to expectations of lower rates
and higher output and in‡ation tomorrow, as well as boosting output and
in‡ation today. If we have correctly and fully identi…ed conventional policy
shocks with the exclusion restriction (16), this should not occur. But even
if some conventional policy shocks do not exactly satisfy that restriction, our
imposition that i h > 0 in (17) implies that they cannot be misinterpreted as
expectations shocks. As we have set it up, expectations shocks associated with
anticipated lower rates tomorrow cannot also be associated with lower realized
rates today. Second, one might be concerned that we are actually picking up
the e¤ects of time-t aggregate-demand shocks: perhaps output and in‡ation
rise today, and persistence causes expectations for their values tomorrow to
rise as well, rather than the other way around. But, since the Fed raises
rates in response to exogenous increases in output and in‡ation, short-rate
expectations would rise in that scenario. The …rst condition in (17) ensures
that we do not include such situations among the expectations shocks that we
identify.
Table 2 summarizes the baseline identi…cation restrictions on the contemporaneous impacts of the two types of shocks. To implement this identi…cation
scheme, we follow the procedure of Arias et al. (2014), who show how to draw
uniformly (under the Haar measure) from the possible set of matrices that
satisfy a given set of zero restrictions. To compute impulse-response functions (IRFs), we draw jointly 10; 000 times from the posterior distribution of
the VAR parameters and the set of admissible ’s, and we simulate the effects of a one-standard-deviation shock under each draw over 44 subsequent
quarters. (This matches the horizon of our longest-range surveys.) Following
other studies using partial identi…cation, we focus on the pointwise medians
across all of the draws.
19
4
4.1
Results
Baseline
Table 3 displays the contemporaneous impacts of the policy-expectations shocks
on the survey expectations themselves. By construction, these shocks have
negative e¤ects on expected T-Bill rates and positive e¤ects on expected GDP
and in‡ation. However, nothing guarantees a priori the magnitude or signi…cance of these e¤ects. The table shows that exogenous shocks to expectations
of the policy rate are modest, resulting, on impact, in an average change of
the one-year expected T-Bill rate of just 3 basis points. A decline of this
magnitude in expected short rates is associated with a contemporaneous increase in the four-quarter-ahead level of GDP of 0:14 percent and an increase
in one-year expected in‡ation of 0:09 percent.
For longer-horizon expectations, the size of the impact of expectations
shocks on the expected T-Bill rate is about the same, that is, about 3 basis
points. However, since this is the average e¤ect over a much longer time
period, it represents a larger change in beliefs than the shock to the same rate
over a one-year period and, presumably, should impart a greater amount of
stimulus. Yet, while the e¤ects of the 6-year and 11-year policy-expectations
shocks on the projected levels of GDP and the CPI are indeed monotonically
increasing across maturities, the implied expected average growth rates are
decreasing. A policy-expectations shock that lowers the expected average
short rate over the next 11 years by 3 basis points only raises expected average
annual GDP growth and in‡ation over the same period by 0:03 percent. A
likely explanation for this …nding is that agents do not believe that monetary
policy innovations can have sustained e¤ects on output and in‡ation over such
long horizons. A policy that results in a 3-basis-point reduction in short-term
rates for a period of eleven years in a row may be interpreted as a structural
change in the policy rule or the steady state of the economy, rather than as
monetary stimulus.
Table 4 o¤ers some evidence that the policy-expectations shocks that we
have identi…ed do indeed correspond to periods in which agents’expectations
for future monetary policy may have shifted. In particular, we list all of
the quarters in which expectations shocks larger than one standard deviation
occurred between 1999, when the FOMC began including meaningful forwardlooking commentaries in its statements, and 2008, when the e¤ective lower
bound was reached.12 In nearly all of these periods, we can match our iden12
We exclude the ZLB period from this analysis, even though several clear forwardguidance events do show up in our model as large expectations shocks, because nearly every
quarter during this period contains some FOMC communication that could be interpreted
20
ti…ed shocks to obvious changes to the wording of the statement that point
toward future policy moves in the same direction of the shocks. Some of
these events, such as the introduction of the "patient" language in 2004, are
also those that are identi…ed by Gurkaynak et al. (2005) as being particularly
potent episodes of forward guidance.13
Panel A of Figure 4 shows the responses of key variables to each of the two
shocks, standard monetary-policy shocks (et ) and policy-expectations shocks
( ht ), in our baseline model using the one-year BCS expectations. The IRFs
are represented as medians (black line), interquartile ranges (red region), and
interdecile ranges (blue regions) across draws of the reduced-form coe¢ cients,
and the shocks considered are all one-standard-deviation in an accommodative
direction (lower interest rates).
The estimated responses to conventional monetary-policy shocks are fairly
standard— for example, they are similar to those found in Christiano et al.
(2005). Output rises slowly but persistently in the quarters following the shock
and peaks at about 0:2 percent after three years, while there is a marginally
signi…cant and sluggish response of in‡ation following an initial modest price
puzzle. Interestingly, while these are the standard …ndings over samples that
include the 1970s, exclusion restrictions do not typically deliver these results
over the post-1982 period used in this study, likely due to the monetary policy
reaction function becoming more forward-looking (see Barakchian and Crowe,
2013). Our use of the survey data in the VAR seems to help with some of the
problems related to the existing identi…cation schemes. However, note that
the price puzzle remains even though we include the forward-looking survey
data on in‡ation among our regressors.
The expectations shock has a large immediate impact on output, with the
response reaching a peak of about 0:1 percent within the …rst year, but it
decays relatively quickly, stabilizing after about three years. The response
of the price level to this shock reaches its peak of 0:1 percent at impact, and
it reverts only very slowly. The e¤ects of these shocks on employment are
also quite large, especially in the short run. (This can be seen by comparing
the IRFs for GDP and labor productivity.) These …ndings indicate that,
particularly at short horizons, shocks to expectations can be quite powerful.
Note that the expectations shocks do not exhibit a price puzzle.
as dovish and because of possible concerns about identi…cation during this time.
13
In constructing this table, we have taken care to account for the timing of the surveys
relative to FOMC meetings. In particular, the BCS data for each quarter is typically
gathered in the …rst week of the last month of each quarter, while the last FOMC meeting
of the quarter takes place a couple of weeks later and therefore would not be re‡ected in
survey responses until the following quarter. The dates in the table re‡ect the dates of the
shock, not of the corresponding FOMC statement.
21
Also note that the amount by which expectations shocks actually a¤ect
short-rate forecasts is quite modest. A one-standard-deviation shock generates only a 3-basis-point change in the survey forecast on impact. Such a
small magnitude is to be expected in a world in which agents typically do
not have much reason to anticipate deviations from the policy rule. In contrast, conventional policy shocks move contemporaneous one-year short-rate
expectations, and the contemporaneous short rate itself, by 11 basis points on
average. Thus, although the IRFs for the two shocks depicted in Figure 4 are
of similar magnitude, the basis-point size of the expectations shock needed to
generate this response is much smaller. To see this comparison more precisely,
Table 5 displays the size of conventional policy shocks required to equal the
macroeconomic e¤ect of a 25-basis-point one-year expectations shock. Over
the short run, the expectations shock is …ve times more e¤ective, as the magnitude of the conventional policy shock should be of almost 125 basis points to
achieve the same cumulative e¤ect on GDP and over 200 basis points to have
the same e¤ect on hours worked. Over the long-run, the expectations shock
is about twice as e¤ective as the conventional policy shock for the cumulative
e¤ect on GDP and CPI. The point estimate of its e¤ect on hours is smaller,
although (not shown in the table) the di¤erences at these horizons are not
statistically signi…cant.
Panels B and C of Figure 4 show the e¤ects using longer-horizon surveys.
The responses to policy-expectations shocks are similar in magnitude although
they are shorter-lived, becoming statistically insigni…cant after two to three
years. The responses to the standard monetary-policy shocks are similar to
those in Panel A. When the model is estimated with these longer maturities,
the expectations shocks are less important relative to monetary-policy shocks,
although they still have some substantial e¤ects, particularly for near-term
in‡ation and employment.
4.2
Causality of expectations shocks: news versus noise
As discussed in Section 2.1, the IRFs shown above describe what happens to
the economy in the aftermath of a policy-expectations shock, but they do not
necessarily imply that the expectations shock causes the entire responses. In
the period of the shock itself, this is not an issue— any movement must be due
to the e¤ects of changes in expectations, because nothing else has had time to
happen yet. However, some of the changes subsequent to the period of the
shock could have taken place even if they had not been anticipated. Therefore,
in order to isolate the causal e¤ect of the expectations shocks beyond the …rst
period, we need to purge the IRFs of the response that would have materialized
22
anyway.
To do this, we exploit the distinction made earlier between news (anticipated policy changes that actually occur) and noise (anticipated policy
changes that do not occur). We consider two extreme cases in which our identi…ed shocks consist either entirely of news or entirely of noise. If the the
expectations shocks that we have identi…ed entirely re‡ect noise ( h = 0 in
the notation of Section 2.1), then the IRFs depicted in Figure 4 only re‡ect
causality— since the shocks do not embed any fundamental changes, any response of the economy must arise only from the e¤ects of the shift in beliefs.
On the other hand, if the identi…ed shocks entirely re‡ect news (var[uht ] = 0),
then the IRFs depicted in Figure 4 are equal to the e¤ect of the change in
beliefs plus the e¤ect of the subsequent policy change itself. Thus, to isolate
the causality of expectations in that case, we must subtract the e¤ect of a
conventional policy shock of the anticipated size. Fortunately, we also have an
estimate of exactly this object.
One complication is that, because of the way the survey data are reported,
our measures of subjective expectations are averages over several periods.
Thus, there are generally multiple possible expected short-rate paths that
would be consistent with any initial expectations shock, and consequently the
appropriate series of conventional policy shocks to use in the above-mentioned
subtraction is not uniquely determined. (For example, a decline of 25 basis
points in the expected one-year average rate could be consistent with a path
of short rates that is 25 basis points lower for the entire year or with a path
that is unchanged over the …rst six months and 50 basis points lower over the
last six months.) For the purposes of this exercise, we assume shocks that
would be su¢ cient to generate a constant short rate at the anticipated average
level over the forecast period. Reasonable variations on this choice make little
quantitative di¤erence.
The "all noise" and "all news" cases are obviously extreme. In reality,
the expectations shocks that we have identi…ed likely re‡ect a combination of
both noise and news. Intuitively, assuming convexity, as in the linear model of
Section 2.1, the true e¤ects of expectations shocks must lie somewhere between
these two polar cases that we can actually compute. Consequently, if these
two cases are empirically close to each other, we will have a fairly precise idea
of the causality of expectations shocks, and the distinction between news and
noise will not be particularly relevant.
Figure 5.A shows the upper (blue) bounds for the case of fully noise and
the lower bounds (red) for the case of fully news for our one-year model,
given an expectations shock of 25 basis points. The di¤erence between
news and noise a¤ects our interpretation of the GDP IRF by at most 0:4
percentage points, at medium horizons. At short horizons, and at all horizons
23
for in‡ation, there is virtually no di¤erence. This is not surprising since we
already knew from the estimated responses plotted in panel A of Figure 4
that unanticipated monetary-policy shocks (et ) have modest e¤ects relative
to the policy-expectations shocks ( ht ) and therefore, in the case of news, the
cumulative impact that needs to be removed is fairly small. Figure 5.B shows
the same comparison using the six-year expectations. (The 11-year results are
very similar to the 6-year and are omitted from here on.) Again, the bounds
are generally quite close to each other quantitatively and are never statistically
di¤erent. Thus, we conclude that our procedure identi…es the e¤ects of policyexpectations shocks— in the causal sense— within a fairly tight range.
5
Modeling forward rate guidance
In this section, we use the results of our model to consider the e¤ects of credible, Odyssean forward guidance— a shock to expectations of future short-term
rates that is followed by deviations from the policy rule that are su¢ cient to
make the expectations materialize. This method of constructing policy scenarios as combinations of di¤erent fundamental structural shocks over several
quarters is also employed, for example, by Mountford and Uhlig (2012) in the
context of …scal policy.
Apart from some timing di¤erences, the forward-guidance scenarios we
consider here are conceptually the same as those we analyzed in the theoretical
model of Section 2.2 (depicted in panels C and D of Figure 1). In particular, we
simulate a shock to h-period expectations in period 0, followed by conventional
monetary-policy shocks in periods 1 through h that cause the initial change in
short-rate expectations to be exactly correct. As above, we assume that the
Fed maintains the short rate constant at the level it has announced over the
entire period covered by the forecast.
The blue lines in Figure 6, panel A show the e¤ect of a 25-basis-point
forward-guidance shock over a one-year horizon, together with 10 90 percent
con…dence regions. Again, in the period of impact, the responses are the
same as those estimated for the expectations shock and pictured in Figure 4.
However, in Figure 4, the actual short rate tightens following the expectations
shock. It is precisely this outcome that we neutralize here. To do so, we
must impose that the Fed hits the economy with multiple stimulative shocks
to the actual short rate following the shift in the expected rate, just as we did
in the theoretical model of Section 2.2. As a consequence, the responses of
GDP and in‡ation are generally higher than what would be generated by a
25-basis-point expectations shock alone. Indeed, the forward rate guidance
24
raises GDP in the short run by over 1 percent and the price level by nearly 1
percent.
The initial responses of output and in‡ation to forward guidance are quantitatively similar to those produced by the NK model. (See Figure 1, panel
D.) There, the output gap rose by about 1:5 percentage points in response
to a one-year forward-guidance shock of 25 basis points, and in‡ation rose
by about 3:5 percent. Indeed, the empirical increase in the price level is
essentially a one-quarter jump, which translates into an annualized in‡ation
rate of about 3:7 percent. More importantly, however, while the empirical
price level is little changed after the …rst quarter (and even declines a bit),
in the theoretical model in‡ation continues to be positive for several quarters.
Thus, while the response on impact is similar, the theoretical model predicts
a much higher cumulative change in in‡ation. In contrast, the output gap in
the theoretical model is much less persistent than our empirical response, but
this is not surprising because the theoretical model that we considered did not
have much of a built-in persistence mechanism, which, for example, could be
introduced through habit formation.
To assess whether forward guidance is itself e¤ective, we need to compare
it to what would have happened if the Fed had pursued the same short-rate
policy without announcing it in advance. The red lines in Figure 6 plot a
simlulation of what would have occurred if the Fed had followed the same
short-rate path that it maintained under the forward-guidance scenario but
with no preceding change in expectations. (Note that this implies a di¤erent
series of conventional policy shocks than were used to construct the blue line.)
Looking at the di¤erence between the red and the blue, the marginal e¤ects
of 25-basis-point forward guidance on both GDP and prices are as much as 1
percentage point in the short- to medium-run and about 0:5 percent after ten
years, with the di¤erences being statistically signi…cant for this entire period.
The marginal e¤ect on hours is also about 1 percent in the short run, although
it decays somewhat faster. To the best of our knowledge, the importance of
this expectations channel in the data has not previously been documented.
Panel B shows the same type of forward-guidance scenario and comparison
for the model based on the six-year survey data. In this case, the experiment
considered is the rather heroic one of the Fed announcing a credible 25-basispoint reduction in the short rate for the entirety of the next six years and then
following through on that promise. Despite the much longer horizon of this
forward rate guidance, the e¤ect relative to adopting the same short-rate path
without pre-announcing it is still about 1 percent on GDP and about 0.5 percent on the price level, with neither statistically signi…cant after two to three
years. The marginal e¤ect on hours is similar to that in the one-year-forwardguidance case. These results contrast with the New Keynesian simulations
25
depicted in Figure 1, where the impact of forward guidance was stronger at
longer horizons. The discrepancy we document between the model and the
data in this respect can be viewed as quantifying the "forward-guidance puzzle."
6
Robustness Checks
In this section, we conduct a battery of robustness checks, whose results are
summarized in Table 6. Since our most signi…cant results were for one-year
expectations, we focus on that horizon. The results using the 6- and 11-year
surveys are not shown but are also generally robust. As a summary measure,
for each speci…cation the table reports the estimated e¤ect of the forward guidance scenario, relative to the same path of short rates when it is not announced
in advance— i.e., the di¤erence between the blue and red lines in Figure 6A.
We report the median magnitude of this di¤erence across parameter draws
after both one and …ve years. Asterisks indicate statistical “signi…cance” at
the 5% level— — -that is, whether at least 95% of the parameter draws result
in a positive marginal e¤ect of forward guidance.
First, as stressed for example in Leahy (2013), when it comes to the analysis
of the recent …nancial crisis, two issues that are generally important in macro
modeling, expectations and nonlinearities, become even more crucial. In this
study, we have focused our attention on expectations, but we still use a linear
model. Since the e¤ective zero bound on nominal interest rates could be
the most important source of non-linearity in our sample period, our …rst
robustness check aims at verifying the stability of our results to the exclusion
of the ZLB period. The pre-ZLB sample is also important to consider in order
to show that our results are not just driven by the relatively short period in
which forward rate guidance has been most actively used as a policy tool.
As shown in the second row of Table 6, when we re-estimate the VAR using
only the pre-ZLB period, the estimated impact of forward rate guidance on
in‡ation and output is almost unchanged, and the e¤ect on hours is only
modestly smaller. These results are in contrast to a number of other VAR
studies that exhibit apparent structural breaks at the ZLB (e.g., Baumeister
and Benati, 2013). A likely explanation for the robustness of our results in
this dimension is that direct measures of expectations help with the stability
of the reduced-form parameters in the presence of nonlinearities because those
expectations are not required to be linear functions of the data, even though
the model itself is linear.
Second, we estimated alternative speci…cations of the VAR. In particular,
we increased the number of lags, we used forecasts from the SPF forecasts
26
instead of the BCS, and we included both the one- and six-year horizon BCS
forecasts within the same VAR. As shown in rows 3, 4, and 5 of Table 6, none
of these changes altered the results signi…cantly; although, in some cases the
statistical signi…cance is weaker at the …ve-year horizon. When we use the SPF
data, overall, the impact of forward guidance seems to be a bit larger across
all three key variables. This result could be due in part to the extension of
the sample back to 1981. As shown in Figure 2, 1981 and 1982 are particularly
important for the in‡ation expectations process and the early 1980s is when
the SPF and BCS GDP forecasts diverge the most.
Third, we also considered di¤erent identi…cation schemes for both the expectations shock and the conventional policy shock. In the …rst case, we imposed the sign restriction on the expected real yield rather than the nominal
yield, as discussed in footnote 6 of Section 2.2. (The expected real yield is
calculated as the one-year survey forecast of the average 3-month TBill minus
the one-year forecast of CPI in‡ation.) In the second case, instead of using
standard timing restrictions, we followed Uhlig (2005) by imposing that policy
shocks lower the short rate and raise the CPI for at least …ve quarters. As
shown in rows 6 and 7, these alternative identi…cations left the results very
little changed.
Finally, instead of using a ‡at prior on the VAR parameters we used the
Minnesota prior (row 8), and again we found very similar results, except that
for CPI the cumulative impact of forward guidance becomes a bit larger in
magnitude at both horizons.
Overall, from this set of empirical exercises we conclude that the …ndings
of this study are quite robust.
7
Conclusion
In this paper, we used a survey-augmented VAR with sign restrictions to identify the e¤ects of anticipated monetary policy on the macroeconomy. We
found that, at a one-year horizon, accommodative monetary policy expectations shocks lead to large and rapid increases in both GDP and in‡ation. We
argued that most of this response is likely causal. At longer horizons, the
e¤ects of these shocks are smaller and not always signi…cant. These results
indicate that forward-guidance policies can potentially be quite e¤ective and
that they are likely to have the greatest impact when targeted at shorter horizons.
Our results both support and challenge the conclusions of standard New
Keynesian models that have been used to argue for forward-guidance policy.
On the one hand, we do show that the anticipation channel exist in the data
27
and that, consequently, forward-guidance can have large and immediate e¤ects.
On the other hand, those e¤ects are smaller than most theoretical models
would predict, particularly with respect to in‡ation. They also decrease
with the horizon of the guidance, in contrast to New Keynesian predictions.
Modi…cations like those proposed in McKay et al. (2015) may help to bring the
New Keynesian models into closer alignment with what our results suggest.
Finally, while we think our results are informative for the debate about
monetary-policy tools at the zero-lower bound, some caution is warranted
in interpreting them in that context. Although expectations for short-term
rates are clearly shaped in part by FOMC communications, forward rate guidance has also not historically been a prominent policy instrument. Moreover,
FOMC communications about future short-term rates may not be viewed as
credible, particularly if they are expressed as Committee forecasts rather than
as commitments, and, in this case, they may even have perverse e¤ects on
expectations as suggested by Campbell et al. (2012). Thus, while our …ndings show that forward guidance can be a powerful policy tool under the right
conditions, a variety of institutional impediments may dampen its e¢ cacy in
practice.
28
Appendix: Treatment of the Survey Data
The SPF in quarter t asks respondents for their forecasts in quarters t 1
through t + 4. We thus have one-year forecasts reported quarterly from
1981:4 through 2014:3, as well as "nowcasts" of the contemporaneous data
and "backcasts" of the lagged data. The main issue we face with these data is
transforming the reported forecast growth rates into levels, which we require
for our VAR. Although the SPF does ask for GDP and CPI forecasts in
terms of levels, this is not always useful to the researcher ex post because rebenchmarking introduces discrete breaks in the series. To obtain consistent
series we assume that the average survey backcast of quarter t 1 is correct
in the sense that any di¤erence between this value and the revised value we
observe in the most-recent data is due entirely to rebenchmarking and does
not re‡ect any fundamental change in agents’beliefs about the economy. By
then applying the reported SPF growth rates for the subsequent …ve quarters,
we obtain a forecast for the t + 4 levels of GDP and CPI that are based on
the same indexation as the 2014 data. Finally, for each quarter, we average
the t + 1 through t + 4 forecasts of the T-Bill rate to obtain forecasts of the
average T-Bill rate over the following year.
The same di¢ culty with benchmarking applies to the BCS, but there we
face the added complication that we do not have a backcast for t 1. Therefore,
to index the level in the BCS data, we assume that BCS respondents have the
same estimate of the quarter-t data level as the SPF respondents. (This is
likely a reasonable assumption, given that, as shown below, the SPF and BCS
data are generally quite similar in other respects.)
Apart from this, the BCS data on one-year expectations are reasonably
straightforward, and we construct one-year expectations by averaging the forecasts for quarters t+1 through t+4 in the last month of each quarter. However,
the BCS data also include forecasts at longer horizons, and these involve complications related to the timing and scope of their reporting. To obtain as
much consistency as possible from this information, we build a new dataset of
long-term expectations from the BCS at a quarterly frequency from 1983 to
2014.
Speci…cally, since 1983, the BCS has been providing semiannual long-range
(2- to 6-year and 7- to 11-year) consensus forecasts for various interest rates,
including the 3-month T-Bill rate, as well as real GDP, GDP de‡ator, and CPI.
These long-range consensus forecasts were originally provided every March
and October in both the Blue Chip Economic Indicators (BCEI) and the Blue
Chip Financial Forecasts (BCFF). Starting in 1996, the BCFF switched to
providing these long-range projections in June and December, while the BCEI
continued reporting them in March in October. We thus have observations of
long-term expectations of our main variables of interest twice per year prior to
29
1996 and four times per year after that time. These inconsistent frequencies
and the fact that the observations are not equally spaced across the year mean
that we cannot use these data directly in the VAR.14
We address both of these issues through interpolation. Speci…cally, from
1983 to 1996, when the long-range forecasts are available only in March and
October, we use the results from the BCEI and linearly interpolate to obtain
June, September, and December values. Once the June and December values become directly observable, we interpolate to obtain only the September
value. Interpolation was not necessary for the one-year horizon, because those
are available on a monthly basis from the BCEI. Once we have adjusted the
timing in this way and computed survey expectations for the average values
of variables in the …rst year following the survey, it is possible to compute
medium-term expectations— that is, the expected average value over the next
6 years— by taking the weighted average of the one-year and 2-6-year expectations, and long-term expectations— that is, the expected average value over
the next 11 years— by taking the weighted average of the one-year, 2-6-year,
and 7-11-year expectations, respectively.
14
In the pre-1996 part of the sample, the availability of long-range forecasts in BCFF for
the same months of BCEI allowed us to compare projections for the variables in common
across the two Blue Chip surveys. We found that di¤erences in forecasted values were
very small, indicating that it was not inappropriate to splice together the results from both
surveys from 1996 to 2014.
30
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34
Table 1. Forecast RMSEs
1 Year (Forecasts for 1983 – 2014)
Ave. 3m T Bill
VAR
0.58%
BCS
0.92%
Ave. GDP growth
1.33%
1.93%
Ave. CPI Inflation
1.01%
1.29%
1 Year (Forecasts for 1982 – 2014)
Ave. 3m T Bill
VAR
0.61%
SPF
1.04%
Ave. GDP growth
1.37%
2.19%
Ave. CPI Inflation
0.99%
1.35%
6 Year (Forecasts for 1988 – 2014)
Ave. 3m T Bill
VAR
0.51%
BCS
1.84%
Ave. GDP growth
0.69%
1.21%
Ave. CPI Inflation
0.21%
0.65%
11 Year (Forecasts for 1993 – 2014)
Ave. 3m T Bill
VAR
0.32%
BCS
1.88%
Ave. GDP growth
0.39%
0.83%
Ave. CPI Inflation
0.13%
0.84%
35
Table 2. Baseline identification restrictions on contemporaneous impact of
shocks
Block / Variable
GDP
x1
CPI
Labor productivity
M2 growth
x2
Corporate profits
Longer-term yield
3m T-Bill rate
i
Survey GDP
Survey CPI
ES[xt+h]
Survey 3m T-Bill
Shock
Conventaional
Policy
policy
expectations
(η)
(e)
?
0
?
0
?
0
?
?
?
?
?
?
+
−
+
?
+
?
?
−
Notes: The table shows the restrictions imposed to identify structural shocks in the baseline VAR.
36
Table 3. Response of survey forecasts to expectations shocks
Change in expectations of…
3m T Bill
Log GDP
Implied expected annual growth
Log CPI
Implied expected annual inflation
Implied change in growth following 25 bp exp. sho
Implied change in inflation following 25 bp exp.
shock
Shock to 1-year Shock to 6-year Shock to 11-year
expectations
expectations
expectations
-0.03%
0.0014
0.14%
0.0009
0.09%
1.22%
0.79%
-0.03%
0.0018
0.03%
0.0016
0.03%
0.26%
0.23%
-0.02%
0.0028
0.03%
0.0022
0.02%
0.26%
0.20%
Note: Based on VARs using Blue Chip Survey under the baseline identification. Table shows responses to a one-standard-deviation
shock to policy expectations at different horizons. Responses are reported for survey-based expectations at the same horizon as the shock
in the period when the shock occurs.
37
Table 4. Largest expectations shocks identified in the VAR, 1999 - 2008
Std.
Dev.
FOMC Events
Date
Expected-Easing Shocks
-2.5
2000Q3 "Expansion of aggregate demand may be moderating"
-2.1
2001Q3 [Sept. 11]
-1.1
2001Q1 Balance of risks shifted to downside; easing cycle begins
-1.1
2006Q1 "Some further policy firming may be needed" (rather than likely)
-1.1
2002Q3 Balance of risks shifted to downside
-1.0
2004Q1 "Committee believes it can be patient…"
-1.0
2006Q3 Removal of phrase "some further policy firming may yet be needed"; "Economic growth has moderated"
-1.0
2008Q1
"Economic growth is slowing… Recent developments... have increased the uncertainty surrounding the outlook"; 75 bp
intermeeting cut and downside risks
Expected-Tightening Shocks
1.9
2005Q4 "Committee judges that some further policy firming is likely" (removed "measured pace" language)
1.5
2001Q4 ???
1.5
2004Q3 Started tightening cycle
1.5
2005Q2
1.4
2006Q4 ???
1.3
2002Q2 "Economy is expanding at a significant pace," downside balance of risks removed
50bp tightening. “The Committee is concerned that this disparity in the growth of demand and potential supply will continue, which
2000Q2
could foster inflationary imbalances.”
2007Q1 "Committee's predominant concern remains the risk that inflation will fail to moderate."
1.2
1.2
"Pressures on inflation have picked up in recent months", changed balance of risks from "roughly equal" to "should be kept roughly
equal" with "appropriate monetary policy"
Notes: The table shows the quarters in which the largest expectations shocks occurred during the period 1999 – 2008, as identified by the VAR model. The right-hand column reports changes in
the FOMC statement whose timing corresponded to the timing of those shocks.
38
Table 5. Size of conventional policy shocks required to equal the effect of a -25 bp
one-year expectations shock
Horizon
1Y
2Y
4Y
8Y
Equal cumulative
effect on GDP
-123
-46
-18
-40
Equal cumulative
effect on CPI
---81
-54
Equal cumulative
effect on hours
-210
-61
-4
-6
Notes: The table shows the size of an exogenous shock to the short rate (a “conventional” policy shock) that would be
necessary to equal the effect of a -25bp shock to one-year expectations for the short rate on each of the indicated macro
variables. One- and two-year effects on the CPI are omitted due to the negative estimated sign of the conventional policy
shock at those horizons.
39
Table 6. Marginal effects of one-year forward guidance under alternative
specifications
1Y
GDP
5Y
1Y
CPI
5Y
1Y
Hours
5Y
Baseline
1.2%*
0.9%*
0.9%*
0.7%*
1.3%*
0.6%*
Pre-ZLB period
1.2%*
1.0%*
0.9%*
0.9%*
0.8%*
0.3%
More lags
1.3%*
1.2%
0.9%*
0.7%
1.4%*
0.6%
1.6%*
1.1%*
1.4%*
1.1%*
1.5%*
0.9%*
1.0%*
0.7%
0.9%*
0.7%*
0.7%*
0.1%
1.3%*
1.0%*
0.9%*
0.8%*
1.3%*
0.6%
1.0%*
0.7%
1.0%*
0.7%
1.0%*
0.4%
1.0%*
1.2%
1.4%*
1.1%*
1.4%*
0.8%
SPF instead of BCS
(begins 1981)
1Y and 6Y surveys
both included
Expectations shocks
use sign restriction on
real yield
Policy shocks
identified by sign
restrictions
Minnesota prior
Notes: Under various modeling alternatives, the table reports simulations of a “forward guidance” policy that commits to maintain the shortterm interest rate 25 basis points below the value prescribed by the policy rule for the next four quarters, relative to a situation in which the
central bank adopts this same path for the short rate without announcing it in advance. The columns show the difference between the effects
of these two policies on macroeconomic variables one and five years after the forward-guidance announcement. See Section 5 of the text for
details on the construction of the scenario in the baseline case. GDP and CPI are reported in terms of cumulative log levels. Hours are
reported as forward (non-cumulative) log levels. Numbers in the table are medians across 10,000 parameter-vector draws. Asterisks
indicate that the marginal effects of forward guidance are positive for at least 95% of the draws.
40
Figure 1. Expectations and Forward-Guidance Shocks in the New Keynesian Model
A. Noise shocks (1 period)
Inflation
Output gap
Nominal short rate
Real short rate
4
2
2.0
3
4
1.5
2
0.5
1
1
3
1.0
2
2
4
6
8
2
1
4
6
8
-1
-0.5
0
2
4
6
8
B. News shocks (1 period)
Inflation
4
2
-1.0
Output gap
4
6
8
-2
Nominal short rate
Real short rate
2
2.0
4
3
1.5
0.5
1
1
3
1.0
2
2
2
4
6
8
2
4
6
8
-1
1
-0.5
0
2
4
6
8
-1.0
2
C. Forward-guidance scenario (1 period)
Inflation
4
Output gap
6
-2
8
Nominal short rate
2.0
3
4
Real short rate
4
2
3
1
1.5
1.0
2
2
0.5
1
2
2
4
6
8
1
4
6
8
-1
-0.5
0
2
4
6
8
-1.0
2
D. Forward-guidance scenario (multiple periods)
Inflation
4
Output gap
4
6
-2
8
Nominal short rate
2.0
Real short rate
2
4
1.5
3
2
2
0.5
1
1
3
1.0
2
4
6
8
2
1
2
4
6
8
-1.0
6
8
-1
-0.5
0
4
2
4
6
8
-2
Notes: The figures show the effects of a shock to subjective expectations for monetary policy that is sufficient to lower expectations of the short-term interest rate h
periods ahead by 25 basis points, where h= 1 (green), 2 (yellow), 3 (red), and 4 (blue). In panel A, we the anticipated shock to policy does not materialize. In
panel B, the anticipated shock does materialize and the central bank takes no other actions. In panel C, the anticipated shock materializes and in addition
central bank introduces anticipated policy shocks in order to maintain the nominal short rate at zero in periods t through t+h-1. In panel D, the central bank
holds the short rate at -.25 in periods t+1 through t+h. Responses are shown for the period of the expectations shock and seven subsequent periods. Calibration
of the model is as described in the text. All variables are in percentage points. Inflation and interest rates are expressed as annual rates.
41
1%
0%
Sep-81
Jan-84
42
SPF
0.12
BCS
6%
5%
7%
6%
5%
4%
3%
May-14
0.14
Mar-13
Jan-12
Nov-10
Sep-09
Jul-08
May-07
Mar-06
Jan-05
Nov-03
Sep-02
Jul-01
May-00
Mar-99
Jan-98
Nov-96
9%
Sep-95
Sep-81
Oct-82
Nov-83
Dec-84
Jan-86
Feb-87
Mar-88
Apr-89
May-90
Jun-91
Jul-92
Aug-93
Sep-94
Oct-95
Nov-96
Dec-97
Jan-99
Feb-00
Mar-01
Apr-02
May-03
Jun-04
Jul-05
Aug-06
Sep-07
Oct-08
Nov-09
Dec-10
Jan-12
Feb-13
Mar-14
0.16
Jul-94
May-93
Mar-92
Jan-91
Nov-89
Sep-88
Jul-87
May-86
Mar-85
0%
Sep-81
Nov-82
Jan-84
Mar-85
May-86
Jul-87
Sep-88
Nov-89
Jan-91
Mar-92
May-93
Jul-94
Sep-95
Nov-96
Jan-98
Mar-99
May-00
Jul-01
Sep-02
Nov-03
Jan-05
Mar-06
May-07
Jul-08
Sep-09
Nov-10
Jan-12
Mar-13
May-14
0
Nov-82
Figure 2. Comparison of SPF and BCS one-year forecasts
Average 3m TBill
0.1
0.08
0.06
0.04
0.02
GDP growth
SPF
BCS
4%
3%
2%
1%
Annual CPI Inflation
8%
SPF
BCS
2%
6%
0%
6%
0%
Sep-81
Oct-82
Nov-83
Dec-84
Jan-86
Feb-87
Mar-88
Apr-89
May-90
Jun-91
Jul-92
Aug-93
Sep-94
Oct-95
Nov-96
Dec-97
Jan-99
Feb-00
Mar-01
Apr-02
May-03
Jun-04
Jul-05
Aug-06
Sep-07
Oct-08
Nov-09
Dec-10
Jan-12
Feb-13
Mar-14
0
Sep-81
Oct-82
Nov-83
Dec-84
Jan-86
Feb-87
Mar-88
Apr-89
May-90
Jun-91
Jul-92
Aug-93
Sep-94
Oct-95
Nov-96
Dec-97
Jan-99
Feb-00
Mar-01
Apr-02
May-03
Jun-04
Jul-05
Aug-06
Sep-07
Oct-08
Nov-09
Dec-10
Jan-12
Feb-13
Mar-14
0.12
Sep-81
Oct-82
Nov-83
Dec-84
Jan-86
Feb-87
Mar-88
Apr-89
May-90
Jun-91
Jul-92
Aug-93
Sep-94
Oct-95
Nov-96
Dec-97
Jan-99
Feb-00
Mar-01
Apr-02
May-03
Jun-04
Jul-05
Aug-06
Sep-07
Oct-08
Nov-09
Dec-10
Jan-12
Feb-13
Mar-14
Figure 3. Term structure of BCS forecasts
Aveage 3m TBill
0.1
0.08
1 Year
6 Year
11 Year
0.06
0.04
0.02
Annual GDP growth
5%
1 Year
6 Year
4%
11 Year
3%
2%
1%
CPI Inflation
5%
1 Year
6 Year
4%
11 Year
3%
2%
1%
43
Figure 4. Impulse-response functions
A. Using 1-year expectations
B. Using 6-year expectations
C. Using 11-year expectations
44
Figure 5. Causality bounds on 25-basis-point expectations shocks
A. Using 1-year expectations
GDP
CPI
Labor Productivity
TBill
CPI
Labor Productivity
TBill
100%noise
100%news
B. Using 6-year expectations
GDP
100%noise
100%news
45
Figure 6. The effects of “forward guidance” scenario
A. One-year forward guidance
GDP
CPI
Hours
TBill
CPI
Hours
TBill
Forward guidance
Policy path only
B. Six-year forward guidance
GDP
46
Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Examining Macroeconomic Models through the Lens of Asset Pricing
Jaroslav Borovička and Lars Peter Hansen
WP-12-01
The Chicago Fed DSGE Model
Scott A. Brave, Jeffrey R. Campbell, Jonas D.M. Fisher, and Alejandro Justiniano
WP-12-02
Macroeconomic Effects of Federal Reserve Forward Guidance
Jeffrey R. Campbell, Charles L. Evans, Jonas D.M. Fisher, and Alejandro Justiniano
WP-12-03
Modeling Credit Contagion via the Updating of Fragile Beliefs
Luca Benzoni, Pierre Collin-Dufresne, Robert S. Goldstein, and Jean Helwege
WP-12-04
Signaling Effects of Monetary Policy
Leonardo Melosi
WP-12-05
Empirical Research on Sovereign Debt and Default
Michael Tomz and Mark L. J. Wright
WP-12-06
Credit Risk and Disaster Risk
François Gourio
WP-12-07
From the Horse’s Mouth: How do Investor Expectations of Risk and Return
Vary with Economic Conditions?
Gene Amromin and Steven A. Sharpe
WP-12-08
Using Vehicle Taxes To Reduce Carbon Dioxide Emissions Rates of
New Passenger Vehicles: Evidence from France, Germany, and Sweden
Thomas Klier and Joshua Linn
WP-12-09
Spending Responses to State Sales Tax Holidays
Sumit Agarwal and Leslie McGranahan
WP-12-10
Micro Data and Macro Technology
Ezra Oberfield and Devesh Raval
WP-12-11
The Effect of Disability Insurance Receipt on Labor Supply: A Dynamic Analysis
Eric French and Jae Song
WP-12-12
Medicaid Insurance in Old Age
Mariacristina De Nardi, Eric French, and John Bailey Jones
WP-12-13
Fetal Origins and Parental Responses
Douglas Almond and Bhashkar Mazumder
WP-12-14
1
Working Paper Series (continued)
Repos, Fire Sales, and Bankruptcy Policy
Gaetano Antinolfi, Francesca Carapella, Charles Kahn, Antoine Martin,
David Mills, and Ed Nosal
WP-12-15
Speculative Runs on Interest Rate Pegs
The Frictionless Case
Marco Bassetto and Christopher Phelan
WP-12-16
Institutions, the Cost of Capital, and Long-Run Economic Growth:
Evidence from the 19th Century Capital Market
Ron Alquist and Ben Chabot
WP-12-17
Emerging Economies, Trade Policy, and Macroeconomic Shocks
Chad P. Bown and Meredith A. Crowley
WP-12-18
The Urban Density Premium across Establishments
R. Jason Faberman and Matthew Freedman
WP-13-01
Why Do Borrowers Make Mortgage Refinancing Mistakes?
Sumit Agarwal, Richard J. Rosen, and Vincent Yao
WP-13-02
Bank Panics, Government Guarantees, and the Long-Run Size of the Financial Sector:
Evidence from Free-Banking America
Benjamin Chabot and Charles C. Moul
WP-13-03
Fiscal Consequences of Paying Interest on Reserves
Marco Bassetto and Todd Messer
WP-13-04
Properties of the Vacancy Statistic in the Discrete Circle Covering Problem
Gadi Barlevy and H. N. Nagaraja
WP-13-05
Credit Crunches and Credit Allocation in a Model of Entrepreneurship
Marco Bassetto, Marco Cagetti, and Mariacristina De Nardi
WP-13-06
Financial Incentives and Educational Investment:
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Lisa Barrow and Cecilia Elena Rouse
WP-13-07
The Global Welfare Impact of China: Trade Integration and Technological Change
Julian di Giovanni, Andrei A. Levchenko, and Jing Zhang
WP-13-08
Structural Change in an Open Economy
Timothy Uy, Kei-Mu Yi, and Jing Zhang
WP-13-09
The Global Labor Market Impact of Emerging Giants: a Quantitative Assessment
Andrei A. Levchenko and Jing Zhang
WP-13-10
2
Working Paper Series (continued)
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WP-13-11
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WP-13-12
Rushing into the American Dream? House Prices, the Timing of Homeownership,
and the Adjustment of Consumer Credit
Sumit Agarwal, Luojia Hu, and Xing Huang
WP-13-13
The Earned Income Tax Credit and Food Consumption Patterns
Leslie McGranahan and Diane W. Schanzenbach
WP-13-14
Agglomeration in the European automobile supplier industry
Thomas Klier and Dan McMillen
WP-13-15
Human Capital and Long-Run Labor Income Risk
Luca Benzoni and Olena Chyruk
WP-13-16
The Effects of the Saving and Banking Glut on the U.S. Economy
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti
WP-13-17
A Portfolio-Balance Approach to the Nominal Term Structure
Thomas B. King
WP-13-18
Gross Migration, Housing and Urban Population Dynamics
Morris A. Davis, Jonas D.M. Fisher, and Marcelo Veracierto
WP-13-19
Very Simple Markov-Perfect Industry Dynamics
Jaap H. Abbring, Jeffrey R. Campbell, Jan Tilly, and Nan Yang
WP-13-20
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WP-13-21
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Cash-in-the-Market Pricing in a Model with Money and
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An Interview with Neil Wallace
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WP-13-22
WP-13-23
WP-13-24
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3
Working Paper Series (continued)
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WP-13-27
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WP-14-01
Can Intangible Capital Explain Cyclical Movements in the Labor Wedge?
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WP-14-02
Early Public Banks
William Roberds and François R. Velde
WP-14-03
Mandatory Disclosure and Financial Contagion
Fernando Alvarez and Gadi Barlevy
WP-14-04
The Stock of External Sovereign Debt: Can We Take the Data at ‘Face Value’?
Daniel A. Dias, Christine Richmond, and Mark L. J. Wright
WP-14-05
Interpreting the Pari Passu Clause in Sovereign Bond Contracts:
It’s All Hebrew (and Aramaic) to Me
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AIG in Hindsight
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Human Capital Risk, Contract Enforcement, and the Macroeconomy
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Adverse Selection, Risk Sharing and Business Cycles
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Francesco Bianchi and Leonardo Melosi
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More on Middlemen: Equilibrium Entry and Efficiency in Intermediated Markets
Ed Nosal, Yuet-Yee Wong, and Randall Wright
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Preventing Bank Runs
David Andolfatto, Ed Nosal, and Bruno Sultanum
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The Impact of Chicago’s Small High School Initiative
Lisa Barrow, Diane Whitmore Schanzenbach, and Amy Claessens
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Credit Supply and the Housing Boom
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti
WP-14-21
The Effect of Vehicle Fuel Economy Standards on Technology Adoption
Thomas Klier and Joshua Linn
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What Drives Bank Funding Spreads?
Thomas B. King and Kurt F. Lewis
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Inflation Uncertainty and Disagreement in Bond Risk Premia
Stefania D’Amico and Athanasios Orphanides
WP-14-24
Access to Refinancing and Mortgage Interest Rates:
HARPing on the Importance of Competition
Gene Amromin and Caitlin Kearns
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Private Takings
Alessandro Marchesiani and Ed Nosal
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Momentum Trading, Return Chasing, and Predictable Crashes
Benjamin Chabot, Eric Ghysels, and Ravi Jagannathan
WP-14-27
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Working Paper Series (continued)
Poor (Wo)man’s Bootstrap
Bo E. Honoré and Luojia Hu
WP-15-01
Revisiting the Role of Home Production in Life-Cycle Labor Supply
R. Jason Faberman
WP-15-02
Risk Management for Monetary Policy Near the Zero Lower Bound
Charles Evans, Jonas Fisher, François Gourio, and Spencer Krane
WP-15-03
Estimating the Intergenerational Elasticity and Rank Association in the US:
Overcoming the Current Limitations of Tax Data
Bhashkar Mazumder
WP-15-04
External and Public Debt Crises
Cristina Arellano, Andrew Atkeson, and Mark Wright
WP-15-05
The Value and Risk of Human Capital
Luca Benzoni and Olena Chyruk
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Simpler Bootstrap Estimation of the Asymptotic Variance of U-statistic Based Estimators
Bo E. Honoré and Luojia Hu
WP-15-07
Bad Investments and Missed Opportunities?
Postwar Capital Flows to Asia and Latin America
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WP-15-08
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WP-15-09
What Does Anticipated Monetary Policy Do?
Stefania D’Amico and Thomas B. King
WP-15-10
6
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