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Federal Reserve Bank of Chicago

Signaling Effects of Monetary Policy
Leonardo Melosi

September 2016
WP 2016-14

Signaling E¤ects of Monetary Policy
Leonardo Melosiy
Federal Reserve Bank of Chicago
September 2016
Abstract
We develop a dynamic general equilibrium model in which the policy rate signals the central
bank’s view about macroeconomic developments to price setters. The model is estimated with likelihood methods on a U.S. data set that includes the Survey of Professional Forecasters as a measure
of price setters’in‡ation expectations. This model improves upon existing perfect information models
in explaining why, in the data, in‡ation expectations respond with delays to monetary impulses and
remain disanchored for years. In the 1970s, U.S. monetary policy is found to signal persistent in‡ationary shocks, explaining why in‡ation and in‡ation expectations were so persistently heightened. The
signaling e¤ects of monetary policy also explain why in‡ation expectations adjusted more sluggishly
than in‡ation after the robust monetary tightening of the 1980s.
Keywords: disanchoring of in‡ation expectations, heterogeneous beliefs, endogenous signals,
Bayesian VAR, Bayesian counterfactual analysis, Delphic e¤ects of monetary policy.
JEL classi…cation: E52, C11, C52, D83.

y Correspondence

to: lmelosi@frbchi.org. I thank Francesco Caselli and three anonymous referees for very
helpful comments and suggestions. I also wish to thank Gadi Barlevy, Francesco Bianchi, Larry Christiano,
Harold Cole, Marco Del Negro, Martin Eichenbaum, Renato Faccini, Jesus Fernandez-Villaverde, Cristina
Fuentes Albero, Luca Gambetti, Bianca Giannini, Dirk Krueger, Alejandro Justiniano, Bartosz Maćkowiak,
James Nason, Kristo¤er Nimark, Luigi Paciello, Giorgio Primiceri, Ricardo Reis, Lucrezia Reichlin, Helene Rey,
Frank Schorfheide, Paolo Surico, and Mirko Wiederholt for their very helpful comments and discussion. I want
to particularly thank Athanasios Orphanides for sharing the real-time data set on in‡ation and the output gap
he constructed from the Federal Reserve Greenbook. I thank participants at several conferences and seminars
for their helpful comments. Justin Bloesch, Todd Messer, Christopher Russo, and Jakob Weber provided excellent research assistance. The views in this paper are solely the responsibility of the author and should not be
interpreted as re‡ecting the views of the Federal Reserve Bank of Chicago or any other person associated with
the Federal Reserve System.

1

Introduction

A salient feature of economic systems is that information is dispersed across market participants
and policymakers. Dispersed information implies that the publicly observed decisions implemented by these policymakers convey information to market participants. A canonical example
is the interest rate set by a central bank. Such an information transfer may strongly in‡uence
the transmission of monetary impulses and the central bank’s ability to stabilize the economy.
Consider the case in which a central bank expects that an exogenous disturbance will raise
in‡ation in the next few quarters. On the one hand, as predicted by standard macroeconomic
models, tightening monetary policy has the e¤ect of mitigating the in‡ationary e¤ects of the
shock. On the other hand, raising the policy rate might also cause higher in‡ation if this action
signals to unaware market participants that an in‡ationary shock is about to hit the economy.
While the …rst type of monetary transmission has been intensively investigated in the economic
literature, the signaling e¤ects of monetary policy have received far less attention.
This paper develops a dynamic general equilibrium model to study the empirical relevance
of the signaling e¤ects of monetary policy and their implications for the propagation of policy and non-policy disturbances. In the model, price-setting …rms face nominal rigidities and
dispersed information. Firms observe their own speci…c technology conveying noisy private
information about aggregate technology shocks that in‡uence the future dynamics of …rms’
nominal marginal costs. Furthermore, price setters observe a noisy private signal about disturbances a¤ecting households’discount factor (henceforth, demand shocks) as well as the policy
rate set by the central bank according to a Taylor-type reaction function. The policy signal
provides public information about the central bank’s view on current in‡ation and the output
gap to …rms. The central bank is assumed to have imperfect information and thereby can make
errors in forecasting the targeted macroeconomic aggregates. We call this model the dispersed
information model (DIM).
The DIM features two channels of monetary transmission. The …rst channel is based on
the central bank’s ability to a¤ect the real interest rate because of both nominal rigidities and
dispersed information. Changes in the real interest rate induce households to intertemporally
adjust their consumption. The second channel arises because the policy rate signals nonredundant information to …rms and hence directly in‡uences their beliefs about macroeconomic
developments. We label this second channel the signaling channel of monetary transmission.
The signaling e¤ects of monetary policy on the propagation of shocks critically depend on how
price setters interpret changes in the policy rate. For instance, raising the policy rate can be
interpreted by price-setting …rms in two ways. First, a monetary tightening might be read as
the central bank responding to an exogenous deviation from its monetary policy rule; that is, a
contractionary monetary shock or an overestimation of the rate of in‡ation or the output gap.

1

Second, a higher interest rate may also be interpreted as the response of the central bank to
in‡ationary non-policy shocks, which, in the model, are an adverse aggregate technology shock
or a positive demand shock. If the …rst interpretation prevails among price setters, tightening
(easing) monetary policy curbs (raises) …rms’in‡ation expectations and hence in‡ation. If the
second interpretation prevails, raising (cutting) the policy rate induces …rms to expect higher
(lower) in‡ation, and hence in‡ation rises (falls).
The model is estimated through likelihood methods on a U.S. data set that includes the
Survey of Professional Forecasters (SPF) as a measure of price setters’in‡ation expectations.
The data range includes the 1970s, which were characterized by one of the most notorious
episodes of heightened in‡ation and in‡ation expectations in recent U.S. economic history. In
the estimated model, …rms mostly rely on private signals to learn about aggregate technology
shocks. Conversely, …rms receive fairly inaccurate private signals about demand shocks, forcing
them to look at the policy signal to learn about these shocks. Nevertheless, the policy signal
turns out to be equally informative about demand shocks and exogenous deviations from the
monetary policy rule, making it hard for …rms to tell these two shocks apart.
This information structure has important implications for the propagation of aggregate
disturbances in the DIM. The signaling e¤ects of monetary policy bring about de‡ationary
pressures in the aftermath of a positive demand shock. When the Federal Reserve raises the
interest rate in response to a positive demand shock, …rms attach some probability that both a
contractionary monetary shock and a persistent overestimation of the output gap by the central
bank might have occurred. These beliefs lower price setters’in‡ation expectations and hence
in‡ation. Thus, the signaling channel makes demand shocks look like supply shocks that move
prices and quantities in opposite directions. Unlike the technology shocks, these arti…cial supply
shocks imply a negative comovement between the federal funds rate and the rate of in‡ation
as well as between the federal funds rate and in‡ation expectations. This property of these
arti…cial supply shocks helps the model …t the 1970s, when the nominal federal funds rate was
kept relatively low and in‡ation expectations attained fairly high levels.
We estimate a benchmark vector autoregressive (VAR) model to show that in the data
in‡ation expectations respond to monetary impulses with delays and remain disanchored for
more than …ve years.1 State-of-the-art perfect information models are shown to have too weak
a propagation mechanism to explain this pattern. In contrast, the estimated DIM accounts for
these empirical facts remarkably well. The monetary tightening that immediately follows a contractionary monetary shock signals positive demand shocks that give rise to upward pressures
on in‡ation expectations. These signaling e¤ects substantially dampen the short-run response
1

The VAR model is agnostic about economic theories and broadly parameterized. Therefore, this model is
often used to obtain an accurate representation of the data. See Christiano, Eichenbaum and Evans (2005) and
Del Negro et al. (2007), among many others.

2

of in‡ation expectations. In the longer run, the monetary tightening ends up signaling persistent underestimation of potential output by the central bank, leading to a disanchoring of
in‡ation expectations that is remarkably similar to what is observed in the data. Furthermore,
while the DIM can explain the large and persistent conditional forecast errors that are observed
in the data, perfect information models cannot. In fact, a general property of perfect information models is that conditional forecast errors are always equal to zero. This property arises
because the nature and the magnitude of the initial shock are perfectly known by all agents.
This property is not shared by the dispersed information model in which rational agents are
confused about the nature and the magnitude of realized shocks.
Furthermore, we show that the signaling e¤ects of monetary policy signi…cantly contribute to
explaining why in‡ation and, especially, in‡ation expectations were so persistently heightened in
the 1970s. The Federal Reserve’s response to two large negative demand shocks that occurred in
1974 ended up signaling both expansionary monetary shocks and an overestimation of potential
output by the central bank. These signaling e¤ects signi…cantly raised in‡ation expectations
and hence in‡ation throughout the second half of that decade. This econometric evaluation
of the signaling e¤ects controls for the in‡ationary pressures owing to the persistently large
overestimation of potential output by the Federal Reserve in the 1970s, which is documented in
the Federal Reserve’s Greenbook.2 We also show that the realization of the two large negative
demand shocks in 1974 is supported by the VAR evidence once the identi…cation of those shocks
is corrected for the signaling e¤ects of monetary policy.
This is the …rst paper that provides an econometric analysis of the signaling e¤ects of
monetary policy based on a microfounded dynamic general equilibrium model. Using a reducedform model, Romer and Romer (2000) …nd evidence of signaling e¤ects of monetary policy in
the U.S. Moreover, Nakamura and Steinsson (2015) and Campbell et al. (forthcoming) assess
the macroeconomic e¤ects of the Federal Open Market Committee’s (FOMC) announcements
about the likely future evolution of the federal funds rate (FOMC forward guidance). They …nd
that FOMC forward guidance conveys the FOMC’s private information to market participants
and this information transfer has large macroeconomic e¤ects.3
The idea that the monetary authority sends public signals to an economy in which agents
have dispersed information was pioneered by Morris and Shin (2002, 2003). The model studied
in this paper is built on Nimark (2008). A particularly useful feature of Nimark’s model is
that the supply side of the model economy can be analytically worked out and characterized by
an equation that nests the standard New Keynesian Phillips curve. The model studied in this
paper shares this feature. Nonetheless, in Nimark (2008) the signaling channel does not arise
2

Orphanides (2001, 2002, 2003) argues that the Federal Reserve’s persistent overestimation of potential
output in the 1970s led to overexpansionary policies, which ultimately resulted in high in‡ation.
3
Campbell et al. (2012) dubbed these e¤ects Delphic forward guidance.

3

because assumptions about the Taylor-rule speci…cation imply that the policy rate conveys only
redundant information to price setters. We introduce a method to solve the DIM that belongs
to the more general class of solution methods introduced by Nimark (2011). Our solution
method improves upon the one used by Nimark (2008) in that it does not require numerically
solving any nonlinear equations.
This paper is also related to a quickly growing empirical literature that uses the SPF to
study the response of public expectations to monetary policy decisions. Del Negro and Eusepi
(2011) evaluate the ability of the imperfect information model developed by Erceg and Levin
(2003) to …t the SPF in‡ation expectations. There are two main di¤erences between that paper and this one. First, in that paper monetary policy does not have signaling e¤ects besides
transferring information about the central bank’s in‡ation target. Second, in our settings,
price setters have heterogeneous beliefs. Furthermore, Coibion and Gorodnichenko (2012b)
…nd that the Federal Reserve raises the policy rate more gradually if the private sector’s in‡ation expectations are lower than the Federal Reserve’s forecasts of in‡ation. This empirical
evidence can be rationalized in a model in which monetary policy has signaling e¤ects and
the central bank acts strategically to stabilize private sector’s in‡ation expectations. Coibion
and Gorodnichenko (2012a) use the SPF to document robust evidence in favor of models with
informational rigidities.
This paper also belongs to a quite thin literature that carries out likelihood-based analyses on models with dispersed information. Nimark (2014b) estimates an island model built
on Lorenzoni (2009) and augmented with man-bites-dog signals, which are signals that are
more likely to be observed when unusual events occur. Maćkowiak, Moench, and Wiederholt
(2009) use a dynamic factor model to estimate impulse responses of sectoral price indexes to
aggregate shocks and to sector-speci…c shocks for a number of models, including a rational inattention model. Melosi (2014) conducts an econometric analysis of a stylized dynamic general
equilibrium model with dispersed information à la Woodford (2002).
Bianchi and Melosi (2014a) develop a dynamic general equilibrium model that features waves
of agents’ pessimism about how aggressively the central bank will react to future changes in
in‡ation to study the welfare implications of monetary policy communication. Gorodnichenko
(2008) introduces a model in which …rms make state-dependent decisions on both pricing and
acquisition of information and shows that this model delivers a delayed response of in‡ation
to monetary shocks. Trabandt (2007) analyzes the empirical properties of a state-of-the-art
sticky-information model à la Mankiw and Reis (2002) and compares them with those of a
state-of-the-art model with sticky prices à la Calvo.
The paper is organized as follows. In Section 2, we describe the dispersed information
model, in which monetary policy has signaling e¤ects, as well as a model in which …rms have
perfect information. In Section 3, we present the empirical analysis of the paper, including the
4

econometric evaluation of the signaling e¤ects of monetary policy. In Section 4, we assess the
robustness of our …ndings. We present our conclusions in Section 5.

2

Models

Section 2.1 introduces the model with dispersed information and signaling e¤ects of monetary
policy. In Section 2.2, we present the time protocol of the model. Section 2.3 presents the
problem of households. Section 2.4 presents …rms’price-setting problem. In Section 2.5, the
central bank’s behavior and government’s behavior are modeled. In Section 2.6, we introduce
the information set available to …rms. Section 2.7 deals with the log-linearization and the solution of the dispersed information model. Finally, Section 2.8 presents the perfect information
model, which will be used to evaluate the empirical signi…cance of the dispersed information
model.

2.1

The Dispersed Information Model (DIM)

The economy is populated by a continuum (0; 1) of households, a continuum (0; 1) of monopolistically competitive …rms, a central bank (or monetary authority), and a government (or …scal
authority). A Calvo lottery establishes which …rms are allowed to reoptimize their prices in any
given period t (Calvo 1983). Households consume the goods produced by …rms, demand government bonds, pay taxes to or receive transfers from the …scal authority, and supply labor to
the …rms in a perfectly competitive labor market. Firms sell di¤erentiated goods to households.
The …scal authority has to …nance maturing government bonds. The …scal authority can issue
new government bonds and can either collect lump-sum taxes from households or pay transfers
to households. The central bank sets the nominal interest rate at which the government’s bonds
pay out their return.

2.2

The Time Protocol

Any period t is divided into three stages. All actions that are taken in any given stage are
simultaneous. At stage 0, the central bank sets the interest rate for the current period t using
a Taylor-type reaction function and after observing an imperfect measure of current in‡ation
and the output gap. At stage 1, …rms update their information set by observing (i) their
idiosyncratic technology, (ii) a private signal about the demand shocks, and (iii) the interest
rate set by the central bank. Given these observations, …rms set their prices. At stage 2,
households learn about the realization of all the shocks in the economy and therefore become
perfectly informed. Households then decide their consumption, Ct ; their demand for one-period
nominal government bonds, Bt ; and their labor supply, Nt . At this stage, …rms hire labor and
5

produce so as to deliver the demanded quantity at the price they have set at stage 1. The …scal
authority issues bonds and collects taxes from households or pays transfers to households. The
markets for goods, labor, and bonds clear.

2.3

Households

Households have perfect information,4 and hence, we can use the representative household to
solve their problem at stage 2 of every period t:
max

Ct+s ;Bt+s ;Nt+s

Et

1
X

t+s

gt+s [ln Ct+s

n Nt+s ] ;

s=0

where is the deterministic discount factor and gt is an exogenous variable in‡uencing this
factor. The logarithm of this exogenous variable follows an autoregressive process: ln gt =
g ln gt 1 + g "g;t with Gaussian shocks "g;t v N (0; 1). We refer to gt as demand conditions
and to the innovation "g;t as the demand shock. Disutility from labor linearly enters the period
utility function. The parameter n a¤ects the marginal disutility of labor.
The ‡ow budget constraint of the representative household in period t is given as follows:
Pt Ct + Bt = Wt Nt + Rt 1 Bt

1

+

t

Tt ;

(1)

where Pt is the price level of the composite good consumed by households and Wt is the (competitive) nominal wage, Rt stands for the nominal (gross) interest rate, t is the (equally
shared) dividends paid out by the …rms, and Tt stands for the lump-sum transfers/taxes. Com1
R1
1
,
posite consumption in period t is given by the Dixit-Stiglitz aggregator Ct = 0 Cj;t dj
where Cj;t is consumption of the good produced by …rm j in period t and is the elasticity of
substitution between consumption goods.
At stage 2 of every period t, the representative household chooses its consumption of the
good produced by …rm j, labor supply, and bond holdings subject to the sequence of the ‡ow
budget constraints and a no-Ponzi-scheme condition. The representative household takes as
given the nominal interest rate, the nominal wage rate, nominal aggregate pro…ts, nominal
lump-sum transfers/taxes, and the prices of all consumption goods. It can be shown that the
demand for the good produced by …rm j is:
Cj;t =

Pj;t
Pt

Ct ;

(2)

4
The main results of the paper are unlikely to change if one assumes that households also have dispersed
information. This point is discussed in the online appendix.

6

where the price level of the composite good is given by Pt =

2.4

R

1

(Pj;t )1

di

1

.

Firms’Price-Setting Problem

Firms are endowed with a linear technology Yj;t = aj;t Nj;t , where Yj;t is the output produced
by …rm j at time t, Nj;t is the amount of labor employed by …rm j at time t, and aj;t is the
…rm-speci…c level of technology that can be decomposed into a level of aggregate technology
(at ) and a white-noise …rm-speci…c component ("aj;t ). More speci…cally,
ln aj;t = ln at + ea "aj;t ;

iid

(3)

where "aj;t v N (0; 1) and at stands for the level of aggregate technology that evolves according
iid

to the autoregressive process ln at = a ln at 1 + a "a;t with Gaussian innovations "a;t v N (0; 1).
We refer to the innovation "a;t as the (aggregate) technology shock.
Following Calvo (1983), we assume that a fraction of …rms are not allowed to reoptimize
the price of their respective goods at stage 1 of any period. Those …rms that are not allowed
to reoptimize are assumed to index their price to the steady-state in‡ation rate. Let us denote
the (gross) steady-state in‡ation rate as , the nominal marginal costs for …rm j as M Cj;t =
Wt =aj;t , the time t value of one unit of the composite consumption good in period t + s to
the representative household as tjt+s , and the expectation operator conditional on …rm j’s
information set Ij;t as Ej;t . The information set contains both private and public signals and
will be de…ned in Section 2.6. At stage 1 of every period t, an arbitrary …rm j that is allowed
to reoptimize its price Pj;t solves
max Ej;t
Pj;t

"

1
X

(

)s

tjt+s

( s Pj;t

s=0

#

M Cj;t+s ) Yj;t+s ;

subject to Yj;t = Cj;t (i.e., …rms commit themselves to satisfying any demanded quantity that
will arise at stage 2), to …rm j’s speci…c demand in equation (2), and to the linear production
function. When solving the price-setting problem at stage 1, …rms have to form expectations
about the evolution of their nominal marginal costs, which will be realized in the next stage
of the period (i.e., stage 2), using their information set Ij;t . At stage 2, …rms produce and
deliver the quantity the representative household demands for their speci…c goods at the prices
they set in the previous stage 1. At stage 2 we assume that …rms do not receive any further
information or any additional signals to what they have already observed at stage 1.

7

2.5

The Monetary and Fiscal Authorities

The monetary authority sets the nominal interest rate according to a Taylor-type reaction
function: Rt = (r ) (~ t = ) (~
xt ) x m;t , where r is the steady-state real interest rate and
~ t is the in‡ation rate observed by the central bank at stage 0 of time t when it has to set
the interest rate Rt . We assume that the central bank knows the current in‡ation rate t
up to the realization of a random variable that follows an autoregressive process ln ;t =
iid

ln ;t 1 + " ;t with Gaussian innovations " ;t v N (0; 1). This exogenous process captures
the central bank’s nowcast errors for the in‡ation rate. In symbols, ~ t = t ;t . We will refer to
the process ;t as the central bank’s measurement error for in‡ation. Analogously, x~t denotes
the output gap when the central bank is called to set the policy rate at stage 0.5 We assume
that the central bank knows the current output gap xt up to the realization of a random variable
that follows an autoregressive process ln x;t = x ln x;t 1 + x "x;t with Gaussian innovations
iid

"x;t v N (0; 1). This exogenous process captures the central bank’s nowcast errors for the
output gap. We will refer to the process x;t as the central bank’s measurement error for the
output gap. In symbols, x~t = xt x;t . Furthermore, the process m;t is an exogenous random
variable that is driven by the following autoregressive process: ln m;t = m ln m;t 1 + m "m;t ,
iid

with Gaussian innovations "m;t v N (0; 1). We will refer to the process m;t as the state of
monetary policy and to the innovation "m;t as the monetary policy shock.
It should be noted that we model policy inertia as a persistent monetary policy shock rather
than adding a smoothing component. Rudebusch (2002, 2006) uses term-structure data to argue
that monetary policy inertia likely re‡ects omitted variables in the rule and that such policy
inertia can be adequately approximated by persistent shocks in the rule. Furthermore, this
modeling choice serves the purpose of solving the dispersed information model fast enough to
allow likelihood estimation.
The policy rule can then be rewritten as follows:
Rt = (r

t

)

xt x

r;t ;

(4)

x
where r;t
m;t ;t x;t captures the exogenous deviations of the interest rate from the monetary
policy rule. These deviations may occur as a result of monetary policy shocks "m;t or as a result
of measurement errors by the central bank, " t and "x;t . We will refer to the process r;t as the
exogenous deviation from the policy rule.
The budget constraint of the …scal authority in period t is represented as follows Rt 1 Bt 1
Bt = Tt . The …scal authority …nances maturing government bonds by either collecting lump-

5

The output gap is the di¤erence between current output and potential output, which is de…ned as the level
of output that would arise under perfectly ‡exible prices ( = 0) and perfect information.

8

sum taxes or issuing new government bonds. The aggregate resource constraint implies Yt = Ct .

2.6

Firms’Information Set

Firms have imperfect knowledge about the history of shocks that have hit the economy. More
speci…cally, it is assumed that …rms’information set includes the history of …rm-speci…c technology ln aj;t and the history of a private signal gj;t on the demand conditions gt , which evolves
iid
according to the following process: ln gj;t = ln gt + eg "gj;t ; where "gj;t v N (0; 1). Moreover, …rms
observe the history of the nominal interest rate Rt set by the central bank, as well as the history
of their own prices.6 To sum up, the information set Ij;t of …rm j at time t is given by
Ij;t

fln aj; ; ln gj; ; R ; Pj; :

tg :

(5)

Firms receive the signals in Ij;t at the price-setting stage 1. We assume that …rms know the
structural equations of the model and its parameters. For tractability, …rms use the log-linear
approximation to the model’s structural equations around its steady-state equilibrium to solve
their signal extraction problem.7 Finally, we assume that …rms have received an in…nitely long
sequence of signals at any time t. This assumption substantially simpli…es the task of solving
the model by ensuring that the Kalman gain matrix is time invariant and the same across …rms.
We follow the imperfect-common-knowledge literature (Woodford, 2002; Adam, 2007; Nimark, 2008) in modeling the highly complex process of acquiring the relevant information by
price setters, which includes information about endogenous variables other than the policy rate,
such as the quantities sold by …rm j (Cj;t ), NIPA statistics with some lags, etc., using a set
of exogenous private signals (b
aj;t and g^j;t ).8 These exogenous signals are assumed to be idiosyncratic to capture the idea that price setters may pay attention to di¤erent indicators. We
partially depart from this literature as we do not allow …rms to observe private signals on all
…ve exogenous state variables, which also include the three subcomponents ( m;t , ;t , and x;t )
of the overall state of monetary policy r;t . Allowing …rms to observe speci…c exogenous signals
6

Observing the history of their own price fPj; :
tg conveys only redundant information to …rms because
their price is either adjusted to the steady-state in‡ation rate, which is known by …rms, or a function of the
history of the signals that have been already observed. Thus, this signal does not play any role in the formation
of …rms’expectations and will be called the redundant signal. Henceforth, when we refer to signals, we mean
only the non-redundant signals.
7
The log-linearized equations will be shown in the next section.
8
In this respect, an important advantage of the rational inattention literature (e.g., Sims 2003, 2006, 2010;
Mackowiak and Wiederholt 2009, 2015; Paciello and Wiederholt 2014; and Matejka 2016) is to go beyond this
reduced-form approach by allowing agents to optimally choose their signal structure under an informationprocessing constraint that limits the overall amount of information the signals can convey. Nonetheless, estimating a rational inattention model is not feasible at this stage because solving the problem of how …rms allocate
their attention optimally would increase even more the already heavy computational burden that characterizes
the solution of the DIM.

9

on the central bank’s measurement errors (i.e., ;t and x;t ) would imply allowing …rms to
have an information advantage about the central bank’s measurement errors over the central
bank itself. This assumption is clearly controversial. In the model price setters know the law of
motion of the central bank’s measurement errors but they have to learn the magnitude thereof
in every period. A less controversial assumption is to endow the …rms also with a private
signal about the exogenous deviations from the policy rule r;t . However, the estimated value
for the noise variance of this additional private signal turns out to be so large as to become
non-identi…able. The presence of a non-identi…able parameter also a¤ects the convergence of
the estimation procedure for the other parameters. Thus, we did not include this additional
signal to …rms’information set.
It should also be emphasized that our information structure follows Woodford (2002) in
assuming that …rms observe truth-plus-white-noise type of signals with serially uncorrelated
noise shocks. This signal structure is arguably quite restrictive parametrically. However, these
restrictions are crucial to avoid weak identi…cation of the model parameters.
A novel ingredient of the model is to allow …rms to perfectly observe the interest rate
set by the central bank Rt . This assumption is based on the fact that the monetary policy
rate is measured very accurately in real time and is subject neither to revisions nor to delays
in reporting. These features do not extend to other aggregate endogenous variables, such as
in‡ation or output (e.g., GDP). This assumption is also supported by …ndings in Andrade et al.
(2014), who document that the Blue Chip Financial Forecasts show very small disagreement on
the next quarter’s federal funds rate compared with other leading macroeconomic aggregates,
such as in‡ation and GDP.
In Section 4, we will show that the maintained information structure in (5) delivers quite
plausible dynamics for in‡ation nowcast errors in the estimated DIM. Furthermore, we will also
show that assuming that …rms observe other endogenous variables, such as the quantity …rms
have sold, turns out to substantially deteriorate the …t of the dispersed information model.

2.7

Log-linearization and Model Solution

We solve the …rms’and households’problems, described in Sections 2.3 and 2.4, and obtain the
consumption Euler equation and the price-setting equation. We denote the log-deviation of an
arbitrary (stationary) variable xt from its steady-state value as x
bt . As in Nimark (2008), we
obtain the imperfect-common-knowledge Phillips curve that is given as follows:9
^ t = (1

) (1

)

1
X

(1

k 1

)

k=1

9

(k)
mc
c tjt

+

1
X

(1

k=1

A detailed derivation is in an appendix, which is available upon request.

10

)k

1

(k)

bt+1jt :

(6)

(k)

In this equation, bt+1jt denotes the average k-th order expectations about the next period’s
Z
Z
(k)
in‡ation rate, bt+1 , that is, bt+1jt
Ej;t : : : Ej;t bt+1 dj:::dj, for any integer k > 1. Moreover,
|
{z
}
k

(k)

mc
c denotes the average k-th order expectations about the real aggregate marginal costs mc
ct
R tjt
(k)
(k)
(k 1)
mc
c j;t dj, which evolve according to the equation mc
c tjt = y^tjt
b
atjt
for any integer k > 1.
The imperfect-common-knowledge Phillips curve makes it explicit that price setters forecast the
forecasts of other price setters (Townsend 1983a, 1983b). The Calvo parameter determines
P
(k)
the structure of weights for the higher-order expectations in the averages 1
)k 1 mc
c tjt
k=1 (1
P
(k)
and 1
)k 1 bt+1jt . The smaller the Calvo parameter, the more the model dynamics
k=1 (1
are a¤ected by the average expectations of relatively higher orders.
The log-linearized Euler equation is standard and reads as follows:
gbt

ybt = Et gbt+1

Et ybt+1

^t =
R

x

^t;
Et ^ t+1 + R

(7)

where Et ( ) denotes the expectation operator conditional on the complete information set. The
log-linearized central bank’s reaction function (4) can be written as follows:
^t +

(^
yt

b
at ) + br;t ;

(8)

where y^t b
at is equal to the output gap.
The demand conditions evolve according to gbt = g gbt 1 + g "g;t . The process for aggregate
technology becomes b
at = a b
at 1 + a "a;t . The exogenous process that leads the central bank to
deviate from the monetary rule is de…ned as br;t = bm;t + b ;t + xbx;t . The subcomponents
of br;t evolve as follows: bi;t = ibi;t 1 + i "i;t with i 2 fm; ; xg. We log-linearize the signal
equation concerning the level of aggregate technology (3) and obtain b
aj;t = b
at + ea "aj;t : The
signal equation concerning the demand conditions is the following: gbj;t = gbt + eg "gj;t . The policy
^ t evolves according to equation (8).
signal R
A detailed description of how we solve the model is provided in the online appendix. The
proposed solution algorithm improves upon the one used in Nimark (2008) as our approach
does not require solving a system of nonlinear equations.10 When the model is solved, the law
10

Nimark (2014a) introduces a method to improve the e¢ ciency of these types of solution methods for dispersed information models in which agents (e.g., …rms) use lagged endogenous variables to form their beliefs. An
alternative solution algorithm based on rewriting the equilibrium dynamics partly as a moving-average process
and setting the lag with which the state is revealed to be a very large number is analyzed by Hellwig (2002)
and Hellwig and Vankateswaran (2009). Rondina and Walker (2012) study a new class of rational expectations
equilibria in dynamic economies with dispersed information and signal extraction from endogenous variables.

11

of motion of the endogenous variables st

h
i0
bt reads as follows:
ybt ; bt ; R
(0:k)

st = v0 Xtjt ;

(9)

h
i0
(s)
(s)
(s) (s) b(s)
b
b
where
b
atjt ; gbtjt ; m;tjt ; ;tjt ; x;tjt : 0 s k is the vector of the average expectations of any order from zero through the truncation k > 0 about the exogenous state variables
Xt = b
at ; gbt ; ^m;t ; ^ ;t ; ^x;t . The average s-th order expectations about the level of aggregate
(0:k)
Xtjt

(s)

technology, b
atjt , are de…ned as the integral of …rms’expectations about the average (s 1)-th
R
(s)
(s 1)
order expectations across …rms. In symbols, this is given as follows: b
atjt = Ej;t b
atjt
dj,
(0)

for 1
s
k, where conventionally b
atjt = b
at . The average expectations about the demand
conditions (^
gt ), the state of monetary policy (^m;t ), and the central bank’s measurement errors
for in‡ation (^ ;t ) and for the output gap (^x;t ) are analogously de…ned. Note that in order to
keep the dimensionality of the state vector …nite, we truncate the in…nite hierarchy of average higher-order expectations. The vector of average expectations about the exogenous state
(0:k)
variables Xtjt is assumed to follow a VAR model of order one:11
(0:k)

Xtjt

(0:k)
1jt 1

= MXt

+ N"t :

(10)

We solve the model by guessing and verify the dynamics of higher-order beliefs (i.e., the
matrices M and N). However, solving for higher-order expectations is nothing other than a
particular solution method in the context of this paper. There exist other approaches that
rely on the fact that average …rst-order expectations about the endogenous variables can be
computed given the guessed laws of motion of the endogenous variables by using the assumption
of rational expectations. In this case, the problem of solving the model boils down to …nding
a …xed point over the parameters that characterizes the laws of motion for the endogenous
variables of interest. See Maćkowiak and Wiederholt (2009) for an example of how this type
of solution method works. When applied to our model, that approach turns out to be harder
to combine with the estimation procedure (i.e., the Metropolis-Hastings posterior simulator),
which requires a high degree of automatization of the solution routine. Furthermore, studying
the higher-order beliefs helps interpret some of the predictions of the model.
11

As is standard in the literature (e.g., Woodford 2002), we focus on equilibria where the higher-order expectations about the exogenous state variables follow a VAR model of order one. To solve the model, we also
assume common knowledge of rationality. See Nimark (2008, Assumption 1, p. 373) for a formal explanation
of this assumption.

12

2.8

The Perfect Information Model (PIM)
(k)

If …rms were perfectly informed, higher-order uncertainty would fade away (i.e., Xtjt = Xt for
any integer k > 0) and the linearized model would boil down to a prototypical three-equation
New Keynesian dynamic stochastic general equilibrium (DSGE) model (e.g., Rotemberg and
Woodford 1997; Lubik and Schorfheide 2004; Rabanal and Rubio-Ramírez 2005). Unlike in
the dispersed information model, we add an exogenous process a¤ecting the price markup
so as to avoid stochastic singularity of this model, which would preclude estimation.12 The
exogenous markup evolves according to the autoregressive process bp;t = pbp;t 1 + p "p;t with
iid

Gaussian innovations "p;t v N (0; 1). The New Keynesian Phillips curve is given as follows:
^ t = pc mc
c t + Et bt+1 + bp;t , where pc
(1
) (1
) = with the real marginal costs
given by mc
c t = y^t b
at . To improve the empirical performance of this alternative model,
we assume that households’ utility is a¤ected by consumption habits. The Euler equation
^t +
for consumption is as follows: c^t = h (1 + h) 1 c^t 1 + (1 + h) 1 Et c^t+1 (1 h) (1 + h) 1 R
(1 h) (1 + h) 1 Et ^ t+1 + (1 h) (1 + h) 1 1
^t . The Taylor rule is the same as in the
g g
dispersed information model. We call this prototypical New Keynesian DSGE model the perfect
information model (PIM).

3

Empirical Analysis

This section contains the econometric analysis of the model and the signaling channel of monetary policy. In Section 3.1, we present the data set. In Section 3.2, we discuss the prior and
posterior distribution for the model parameters. In Section 3.3, we evaluate the ability of the
DIM to …t the data relative to that of the PIM. In Section 3.4, we assess the relative ability of
the DIM to replicate a few stylized empirical facts about the propagation of monetary impulses.
In Section 3.5, we study the propagation of the structural disturbances in the estimated DIM.
In Section 3.6, we run a Bayesian counterfactual experiment to assess the empirical relevance
of the signaling e¤ects of monetary policy.

3.1

The Data

The model is estimated using a data set that comprises the following seven observable variables
for the U.S. economy: the Hodrick-Prescott (HP) output gap,13 the in‡ation rate (GDP de‡a12

In our estimation we use data on both the output gap and in‡ation. In the absence of price markup shocks,
it is well-known that the three-equation perfect information model features almost perfect correlation between
the output gap and in‡ation, causing the model to be stochastically singular. Adding a markup shock loosens
this tight relation between the output gap and in‡ation, allowing us to estimate the perfect information model.
13
The results are robust if one computes the potential output using a quadratic trend or uses the output gap
computed by the Congressional Budget O¢ ce.

13

tor), the federal funds rate, one-quarter-ahead and four-quarters-ahead in‡ation expectations
from the Survey of Professional Forecasters (SPF), the real-time output gap, and real-time in‡ation from the Federal Reserve’s Greenbook.14 The data are quarterly and run from 1970:Q3
through 2007:Q4. The measurement equations and more details on how the observables are
constructed are available in the online appendix. We use the SPF data to inform the average
(1)
(1)
…rst-order expectations about in‡ation; that is, ^ t+1jt and ^ t+4jt . To avoid stochastic singularity, we assume that the two series for in‡ation expectations are observed with i.i.d. Gaussian
measurement errors. We use the real-time data on the output gap and in‡ation to inform the
central bank’s perceived output gap y^t a
^t + ^x;t and its perceived in‡ation rate ^ t + ^ ;t , respectively. These series were constructed by Orphanides (2004) until 1995:Q4. We have completed
the series using the tables kept by the Federal Reserve Bank of Philadelphia after harmonizing
it.15

3.2

Bayesian Estimation

As is standard, we …x the value for so that the steady-state real interest rate is broadly
consistent with its sample average. The prior and posterior statistics for the model parameters
are reported in Table 1. As will become clear, the degree of persistence of signaling e¤ects
ultimately hinges on the persistence of the shocks that the monetary authority signals to …rms
by changing the policy rate. Therefore, the priors for the autoregressive parameters a ; g , m ,
, and x are set to be broad enough to accommodate a wide range of persistence degrees for
the …ve exogenous processes. The values of the volatilities for the structural innovations ( a ;
, and x ) are also crucial as they a¤ect …rms’signaling extraction problem. Hence,
g , m;
we select quite broad priors for those volatilities. The noise variances of the exogenous private
signals regarding aggregate technology and demand conditions (ea and eg ) are crucial for the
macroeconomic implications of the signaling channel as they a¤ect the accuracy of private
information and, hence, to what extent …rms rely on the policy signal to learn about these
non-policy shocks. To avoid determining a-priori how strongly the signaling channel in‡uences
…rms’ beliefs, we set a loose prior over these parameters. Finally, the prior means for the
measurement errors associated with in‡ation expectations are set so as to match the variance
of in‡ation expectations reported in the Livingston Survey following the practice of Del Negro
and Schorfheide (2008).
We combine the prior distribution for the parameters of the two models (i.e., the DIM
14

A detailed description of the data set is provided in Section E of the online appendix.
The Federal Reserve Bank of Philadelphia computes the real-time output gap as percent deviations of output
Yt from its potential Yt (i.e., 100 (Yt Yt ) =Yt ). Therefore, these data must be adjusted so as to make them
consistent with the data set constructed by Orphanides (2004) for the earlier quarters and with the model’s
concept of the output gap (i.e., 100 (ln Yt ln Yt )). Analogous transformation is made for the real-time in‡ation
rate.
15

14

Name

x

DIM - Posterior
Mean
5%
95%
0:3608 0:3137 0:4112
1:6782 1:4454 2:1392
0:6731 0:4898 0:7917

h
a
g
m

x

0:9764
0:9038
0:9468
0:3411
0:9541

0:9635
0:8663
0:8807
0:2472
0:9311

0:9897
0:9207
0:9748
0:4577
0:9812

1:4208
2:6068
3:6786
34:884
0:8474
0:2686
1:0448

0:9764
1:5364
2:8764
34:240
0:6866
0:2415
0:9278

2:0395
3:3252
4:0607
35:522
0:9842
0:3043
1:1762

p

100 a
100ea
100 g
100eg
100 m
100
100 x
100 p
100 1
100 2
100ln

0:1226 0:1088 0:1388
0:1087 0:0963 0:1215
0:6532 0:5661 0:7482

PIM - Posterior
Mean
5%
95%
0:4622 0:4061 0:5135
1:5560 1:3770 1:7519
0:0092 0:0004 0:0195
0:1629 0:0795 0:2414
0:6043 0:5112 0:6886
0:9721 0:9576 0:9873
0:3483 0:2834 0:4112
0:2357 0:1409 0:3276
0:9641 0:9379 0:9897
0:9952 0:9897 0:9995
0:5197 0:4437 0:5980
1:4489

0:6312

2:5713

0:5266
0:2567
1:0442
0:5796
0:1109
0:0658
0:8651

0:4545
0:2284
0:9260
0:4226
0:0959
0:0540
0:7562

0:6030
0:2849
1:1633
0:7605
0:1262
0:0788
0:9591

Prior
Type Mean
B
0:50
G
1:50
G
0:50
B
0:50
B
0:50
B
0:50
B
0:50
B
0:50
B
0:50
B
0:50
IG
0:80
IG
0:80
IG
0:80
IG
0:80
IG
0:80
IG
0:80
IG
0:80
IG
0:80
IG
0:10
IG
0:10
N
0:65

Std.
0:30
0:40
0:40
0:20
0:20
0:20
0:20
0:20
0:20
0:20
1:50
1:50
1:50
1:50
1:50
1:50
1:50
1:50
0:08
0:08
0:10

Table 1: Prior and posterior statistics for the parameters of the dispersed information model (DIM) and the
perfect information model (PIM)
and the PIM) with their likelihood function and conduct Bayesian inference. As explained in
Fernández-Villaverde and Rubio-Ramírez (2004) and An and Schorfheide (2007), a closed-form
expression for the posterior distribution is not available, but we can approximate the moments of
the posterior distribution via the Metropolis-Hastings algorithm. We obtain 250,000 posterior
draws for the dispersed information model and 1,000,000 draws for the perfect information
model. As far as the DIM is concerned, the posterior mean for the Calvo parameter implies
very ‡exible prices, with implied duration of roughly half a year.
The posterior mean for the in‡ation coe¢ cient of the Taylor rule ( ) is higher than its
prior mean and quite similar across models. The output gap coe¢ cient in the Taylor rule x
is substantially larger in the DIM than in the PIM. Since the Taylor rule also plays the role of
signaling equation in the DIM, a higher value for this parameter raises, all other things equal,
the amount of information conveyed by the policy rate about the central bank’s estimates
of the output gap. On the contrary, the federal funds rate is found to respond very weakly
to the output gap in the PIM. The other Taylor rule’s parameters are very similar across
the two models with the only exception of the persistence of monetary shocks m , which is
15

substantially larger in the DIM. Note that highly persistent monetary shocks have the e¤ect
of increasing the persistence of the signaling e¤ects of monetary policy on the macroeconomy
insofar as changes in the policy rate signal this type of shock. It should also be noted that the
autoregressive parameter for the price markup p in the PIM is estimated to be very close to
unity, highlighting serious shortcomings of the PIM when it comes to endogenously accounting
for the persistent dynamics of in‡ation in the data. This is a point to which we will return in
the next section.16
The posterior mean for the variance of the …rm-speci…c technology shock ea implies that the
posterior mean of the signal-to-noise ratio a =ea is 0:54. The posterior mean for the signal-tonoise ratio g =eg is extremely small, suggesting that …rms’private information is less accurate
about demand shocks than about aggregate technology shocks. The posterior distribution
implies that the following properties characterize the estimated information structure. First,
…rms learn mostly about aggregate technology from their private signal: the posterior median
for the ratio of private information to public information about the aggregate technology is
bt to learn about the demand
88 percent.17 Second, …rms largely rely on the policy signal R
conditions gbt , since the private signal conveys only 21 percent of the overall information …rms
gather about this exogenous state variable. Third, the policy signal conveys roughly the same
amount of information about the demand shocks (^"g;t ) and the exogenous deviations from the
policy rule (^"m;t ; ^" ;t ; ^"x;t ). The second property of the estimated information structure implies
that …rms rely mostly on the public signal to learn about the demand shocks and the exogenous
deviations from the Taylor rule. However, the third property implies that …rms …nd it hard
to tell whether observed changes in the policy rate are due to exogenous deviations from the
policy rule or are instead due to the central bank’s response to demand shocks. This feature is
crucial to understanding most of the analysis that follows.
16

Interestingly, the estimated steady-state in‡ation rate
is 20 basis points lower in the DIM than in the
PIM. In both models, steady-state in‡ation a¤ects only the intercept of the measurement equations for the
following observables: in‡ation, the federal funds rate, one-quarter-ahead and four-quarters-ahead in‡ation
expectations, and real-time in‡ation. As a result, this parameter is most likely informed by the sample mean of
those observables. Since we use the same data set to estimate the two models, a 20-basis-point di¤erence in the
estimated value for steady-state in‡ation is striking. While it is very challenging to disentangle the exact reasons
behind this result, it is conceivable that the highly sluggish responses of in‡ation and in‡ation expectations to
shocks to the output gap mismeasurement (^x;t ) in the DIM, along with the persistent overestimation of potential
output observed in the 1970s and in the 1980s, have a¤ected the estimation of this parameter. This …nding
is quite interesting in light of the growing theoretical literature on trend in‡ation (e.g., Ascari and Sbordone
2014.)
17
We show how to use entropy-based measures to assess how much information is conveyed by signals to …rms
in the online appendix. These measures quantify information ‡ows following a standard practice in information
theory (Cover and Thomas 1991).

16

3.3

The Empirical Fit of the DIM

The objective of this section is to validate the DIM as a reliable modeling framework for
macroeconomic analysis. To this end, we compare the goodness of …t of the DIM relative to
that of the PIM, which is a prototypical New Keynesian DSGE model that has been extensively
used for monetary policy analysis (e.g., Rotemberg and Woodford 1997; Clarida, Galí, and
Gertler 2000; Lubik and Schorfheide 2004; Coibion and Gorodnichenko 2011).
In Bayesian econometrics, non-nested model comparison is based on computing the posterior
probability of the two candidate models. The marginal likelihood is the appropriate density
for updating prior probabilities over a set of models.18 Since the marginal likelihood penalizes
for the number of model parameters (An and Schorfheide 2007), it can be applied to gauge the
relative …t of models that feature di¤erent numbers of parameters, such as the DIM and the
PIM. The DIM has a log marginal likelihood equal to -319.89, which is higher than that of the
PIM (-334.95). It follows that starting with a 50 percent prior probability over each of the two
competing models, the posterior probability of the DIM turns out to be extremely close to one.
Since the PIM has one more aggregate shock than the DIM, this result has to be interpreted
as fairly strong evidence in favor of the ability of the DIM to …t the data relatively well.

3.4

VAR Evidence

To further investigate the empirical performance of the DIM relative to perfect information
models, we evaluate the relative ability of this structural model to account for some key empirical facts regarding the transmission of monetary disturbances to in‡ation and in‡ation
expectations. We use a VAR model to establish these facts.19 We perform Bayesian estimation
of this VAR model with four lags by using the data set described in Section 3.1. The results
that follow are robust to adopting the larger data set used in the in‡uential study by Christiano,
Eichenbaum and Evans (2005) along with the SPF in‡ation expectations described in Section
3.1. We use a unit-root prior (Sims and Zha 1998) for the parameters of this Bayesian VAR with
a presample of six quarters. As is standard, the number of lags and the …ve hyperparameters
pinning down the prior are chosen so as to maximize the marginal likelihood.
The upper graphs of Figure 1 show the response of in‡ation expectations to monetary
shocks identi…ed with sign restrictions (Uhlig 2005); that is, contractionary monetary shocks
move output and in‡ation down and the federal funds rate up for the …rst …ve quarters. The
lower graphs show the implied one-quarter-ahead (left plot) and four-quarters-ahead (right
18

Furthermore, Fernández-Villaverde and Rubio-Ramírez (2004) show that the marginal likelihood allows
the researcher to select the best model to approximate the true probability distribution of the data-generating
process under the Kullback-Leibler distance.
19
The VAR model unsurprisingly attains a higher marginal likelihood than that of the two structural models,
which validates the VAR model as the benchmark model from a Bayesian perspective (Schorfheide 2000).

17

Inflation

0

FFR

Infl Expect (1Q)

Infl Expect (4Q)

0.05
-0.05

0.2

0.05

0

-0.1

0

-0.05
0

-0.15

-0.05

-0.1

-0.2

-0.1

-0.15
-0.15

-0.2
0

10

20

0

10

20

0

Inflation Forecast Errors (1Q)

10

20

0

10

20

Inflation Forecast Errors (4Q)

0

0

-0.1

-0.1
-0.2

-0.2
-0.3
0

5

10

15

20

0

5

10

15

20

Figure 1: VAR Impulse Responses to a Monetary Shock and Conditional Forecast Errors. Upper graphs:
Impulse response functions to a monetary policy shock identi…ed with sign restrictions. Lower graphs: The
implied one-quarter-ahead (left plot) and four-quarters-ahead (right plot) in‡ation forecast errors conditional
on the monetary shock. Solid lines denote posterior median responses. Shaded areas denote the 70 percent
posterior credible set. All numbers are annualized and in percent.

plot) in‡ation forecast errors conditional on the monetary shock. The gray areas denote the
70 percent posterior credible sets and the solid line the posterior median. Three facts have
to be emphasized. First, in‡ation forecast errors conditional on monetary shocks are fairly
persistent. In the aftermath of a monetary tightening, the lower graphs of Figure 1 show that
the posterior median (the solid line) of the one-quarter-ahead and four-quarters-ahead in‡ation
forecast errors are larger than zero for almost …ve years. The 70 percent posterior upper bound
for these forecast errors stays in negative territory for at least three years. Second, in‡ation
expectations barely move immediately after a monetary shock. Third, the responses of both
in‡ation and in‡ation expectations to monetary shocks exhibit a great deal of persistence, with
a half life20 exceeding 20 quarters. This last fact suggests that in‡ation expectations remain
disanchored for a few years after a monetary contraction.
While, as we shall see, the DIM can explain large and persistent conditional forecast errors
through signaling e¤ects, perfect information models cannot. Indeed, the …rst fact is a conun20

Half life is de…ned as the number of quarters after the initial shock it takes for the largest e¤ect of a shock
to reduce to half.

18

F o u r-Q u a rte r-A h e a d In fla tio n E xp e cta tio n s

Contemporaneous Response to Contractionary Monetary Shocks
Smets and Wouters (2007) Model
VAR

0

-0.5

70% Prior Credible Interval

-1

90% Prior Credible Interval
-1.5
-2

-1.8

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

One-Quarter-Ahead Inflation Expectations

Figure 2: Smets and Wouter (2007) Model’s Prior Predictive Checks (1,000,000 draws). The dark and light
gray areas denote the 70 percent and 90 percent prior interquantile ranges for the contemporaneous response of
in‡ation expectations at one-quarter and four-quarters horizons to contractionary monetary shocks that raise
the interest rate by 0.25 percent in the model developed by Smets and Wouters (2007). The rectangle marks
the 70 percent posterior interquantile range of the VAR-implied contemporaneous responses to an equivalently
scaled monetary shock. All numbers are in percent. The red dashed lines are the zero axes.

drum for every perfect information model. A general property of perfect information models
is that the response of h-period-ahead expectations turns out to be identical to the response
of the actual variable h periods after the shock was realized. To put it di¤erently, perfect information models predict conditional forecast errors to be always equal to zero. This property
of perfect information models arises because the nature and the magnitude of the initial shock
are perfectly known by the agents in every period. This property is not shared by the dispersed
information model in which rational agents are confused about the nature and the magnitude
of realized shocks.
State-of-the-art perfect-information models also struggle to explain the second and the third
fact. Figure 2 illustrates this point. The gray areas denote the prior interquantile ranges for the
contemporaneous response of in‡ation expectations in the Smets and Wouters (2007) model,
which is a state-of-the-art New Keynesian DSGE model with perfect information.21 These areas
21

We use this relatively large-size DSGE model, instead of the PIM introduced in Section 2.8, to give perfect
information models the best chance to replicate the VAR impulse response functions. That model features a lot

19

are computed by simulating 1 million parameter draws from the prior distribution described
in Tables 1A and 1B in Smets and Wouters (2007).22 In Bayesian econometrics this analysis
is called a prior predictive check (Geweke 2005, p. 262; An and Schorfheide 2005; Del Negro
and Schorfheide 2011; Leeper et al. 2015) and is often an important ex-ante speci…cation check
to assess whether a given model has any chance to replicate an empirical …nding of interest.
The rectangle with black solid edges marks the 70 percent posterior interquantile ranges for
the contemporaneous response of the in‡ation expectations implied by the VAR in Figure 1.
This plot shows that the VAR-implied contemporaneous responses lie far in the right tail of
the prior distribution implied by the model developed by Smets and Wouters (2007). This
means that for plausible parameterizations, the Smets and Wouters model cannot explain why
the observed in‡ation expectations do not contemporaneously respond to monetary shocks. In
particular, this structural model …nds it very hard to rationalize contemporaneous drops in the
one-quarter-ahead in‡ation expectations that are smaller than 10 basis points while the VAR
analysis suggests that the 70 percent lower-bound fall is about 6 basis points.
As far as the third fact is concerned, we compute the prior distribution for the half life of
the response of in‡ation and in‡ation expectations to a monetary shock in the model developed
by Smets and Wouters (2007).23 The prior medians for the half life of the response of in‡ation,
one-quarter-ahead in‡ation expectations, and four-quarters-ahead in‡ation expectations are …ve
quarters, four quarters, and two quarters, respectively.24 These numbers are way below what
the VAR evidence suggests in Figure 1. The 90th percentile of these prior distributions is seven
quarters for the response of in‡ation, six quarters for the response of one-quarter-ahead in‡ation
expectations, and three quarters for the response of four-quarters-ahead in‡ation expectations.
Such low percentiles suggest that the Smets and Wouters (2007) model is unable to adequately
explain the large degree of persistence that characterizes the VAR response of in‡ation and
in‡ation expectations to monetary innovations. In the next section, we will show that the DIM
is signi…cantly more successful at replicating this piece of VAR evidence.

3.5

Impulse Response Functions

In this section, we study the propagation of shocks in the estimated DIM. In Section 3.5.1,
we analyze the propagation of monetary shocks. We deal with the transmission of non-policy
of mechanisms to …t the persistence in the data and has been found to …t the data well relative to a Bayesian
VAR model (Del Negro et al. 2007 ). Note that the Smets and Wouters (2007) model features serially correlated
monetary shocks as we assume in the DIM.
22
We rescale the monetary shocks so that the contemporaneous response of the interest rate in the model is
equal to 25 basis points, and we discard those prior draws that imply a drop in the interest rate immediately
after a contractionary monetary shock.
23
Plots of these prior distributions are reported in the online appendix.
24
The prior means are very close to the prior medians.

20

Output

0

Inflation

0

0.4

Interest Rate

-1

0.05

1Q Infl.Expect

0
-0.2

-0.4

-0.6
5 10 15 20

0

0

-0.05

-0.05

-0.1

-0.1

0

-3

0

4Q Infl.Expect

0.2

-2

-4

0.05

-0.2
0

5 10 15 20

1

-0.15
0

5 10 15 20

0.08

-0.05

-0.15
0

5 10 15 20

0.02

0

5 10 15 20

0

5 10 15 20

0.08

0.06

0.06
0.01

-0.1

0.5

0.04

0.04
0

-0.15

0.02

-0.2

0
0

5 10 15 20

0.02

0
0

5 10 15 20

-0.01
0

5 10 15 20

0
0

5 10 15 20

Figure 3: Impulse Response Functions to a Contractionary Monetary Shock. Upper graphs: Impulse response
functions to a monetary shock that raises the federal funds rate by 25 bps. Responses are in percentage
deviations from their steady-state values. The responses of in‡ation, the interest rate, and in‡ation expectations
are annualized. The solid lines denote the posterior median of the responses. The gray areas denote 90 percent
credible sets. The horizontal axis in all graphs measures the number of quarters after the shock. Lower graphs:
Responses of average expectations about the …ve exogenous state variables in percentage deviations from their
steady-state level. Black solid lines denote the average …rst-order expectations. Dashed black lines denote the
average second-order expectations. Dashed-dotted lines denote the average third-order expectations.

shocks (i.e., demand shocks and aggregate technology shocks) in Section 3.5.2.
3.5.1

Propagation of Monetary Shocks

Figure 3 shows the impulse response functions (and their 90 percent posterior credible sets
in gray) of the level of real output (GDP), the in‡ation rate, the federal funds rate, onequarter-ahead in‡ation expectations, and four-quarters-ahead in‡ation expectations to a monetary shock that raises the interest rate by 25 basis points. Four features of these impulse
response functions have to be emphasized. First, the DIM delivers impulse response functions
of in‡ation expectations that look remarkably similar to those implied by the VAR model introduced in Section 3.4 (Figure 1). This similarity is striking if one takes into account that the DIM
is a small-scale model. As discussed in Section 3.4, even a state-of-the-art perfect-information
model, such as the one developed by Smets and Wouters (2007), has a hard time explaining the
high degree of persistence that characterizes the response of in‡ation and in‡ation expectations

21

to monetary shocks implied by the VAR model. Second, the responses of in‡ation and in‡ation
expectations are very sluggish, even though the estimated degree of nominal rigidities is quite
small. These persistent patterns are in line with the VAR evidence introduced in Section 3.4.
Third, the DIM predicts fairly strong real e¤ects of money. Sluggish adjustments in prices
imply a lower path for households’in‡ation expectations. Consequently, the expected path of
the real interest rate shifts upward after the contractionary monetary shock, leading the Euler
equation (7) to predict a large drop in real activity.25 Fourth, …rms’ in‡ation expectations
respond positively to contractionary monetary shocks with some posterior probability, which is
also consistent with the VAR evidence depicted in Figure 1.
In the lower graphs of Figure 3, we report the response of the average higher-order expectations (from the …rst order up to the third order). Notice that the signaling channel induces
…rms to partially believe that the rise in the interest rate is due to either a positive demand
shock or a negative technology shock or an overestimation of the output gap by the central
bank. These signaling e¤ects are not surprising given the poor quality of the private signal
about the demand conditions relative to the public signal and the information mix conveyed by
the policy signal, as discussed in Section 3.2. Furthermore, note that the average expectations
about the state of monetary policy ^m;t and those about the central bank’s measurement error
for the output gap ^x;t virtually respond in the same fashion to monetary shocks. The only
^ t . Therefore,
thing …rms observe about these two exogenous processes is the interest rate R
…rms can rationally tell the two processes apart only if these processes have di¤erent statistical properties. For instance, …rms understand that a persistent change in the interest rate
is relatively more likely to be explained by the more persistent shock. Nevertheless, the statistical properties of these two exogenous processes turn out to be almost identical (Table 1).
Notice that, for given parameters of the monetary policy rule, the in-sample dynamics of these
two exogenous processes and hence their statistical properties are exactly determined by the
actual and real-time output gap as well as actual and real-time in‡ation, which are observable
variables in our estimation.
Figure 3 shows that a contractionary monetary shock causes in‡ation expectations (i) to
barely move upon impact and (ii) to remain persistently away from their steady-state value
(disanchoring). In Section 3.4, we showed that these patterns are observed in the data. The
DIM o¤ers a structural interpretation of these patterns. To this end, we show the contribution
(0:k)
of the average expectations Xtjt about the …ve exogenous state variables to the response of
in‡ation and in‡ation expectations in Figure 4. The vertical bars show the response of in‡ation
(left graph) and in‡ation expectations (middle and right graphs) to a contractionary monetary
25
It should be noted that the log-linearized Euler equation (7) can be expanded forward to obtain x
bt =
P1
n
n
b
b
Et bt+k rbt+k , where Rt Et bt denotes the real interest rate and rbt denotes the natural
k=0 Rt+k
rate, which is a function of aggregate technology shocks and demand shocks.

22

Inflation

1Q Inflation Expectations

4Q Inflation Expectations

0.15
0.1

0.2
0.1
0.1

0.05

0.05
0

0

0
-0.1
-0.05

-0.05

-0.2
-0.1
-0.1

-0.3
-0.15

-0.15

-0.4
-0.2
-0.5

-0.2

-0.25
5

10

15

20

5

10

15

20

5

10

15

20

Figure 4: Contributions of Average Expectations to the Response of In‡ation and In‡ation Expectations to
Monetary Shocks. Parameter values are set equal to the posterior mean. The monetary shock is rescaled so
as to raise the interest rate by 0.25 percent. The solid red line captures the response of in‡ation (left graph),
one-quarter-ahead in‡ation expectations (middle graph), and four-quarters-ahead in‡ation expectations (right
graph). The vertical bars capture the contribution of the true shock as well as that of the average expectations
to these responses.

shock obtained by simulating the DIM using only one of the …ve exogenous state variables and
the associated average expectations. The sum of the …ve vertical bars equals the response of
in‡ation and in‡ation expectations (i.e., the solid red line) evaluated at the posterior mean
reported in Table 1. Upon impact, the contribution of the average expectations about positive
demand shocks (the dark gray bars lying in positive territory) almost perfectly o¤sets the
contribution of the average expectations about the exogenous deviations from the monetary
policy rule (the light gray and white bars lying in negative territory). This …nding implies
that the monetary tightening owing to the policy shock immediately signals that the central
bank is responding to a demand shock, exerting upward pressures on in‡ation expectations.
These signaling e¤ects explain why in‡ation expectations hardly move as the monetary shock
hits and then evolve sluggishly. The persistent disanchoring of in‡ation expectations observed
in the longer run is explained by the fact that the monetary tightening ends up signaling the
central bank’s persistent mistakes in measuring the output gap, as captured by the white bars
lying in negative territory. These signaling e¤ects raise the half life of the response of in‡ation

23

expectations in line with what is observed in the data.26
The DIM seems to overstate the persistence of in‡ation forecast errors after a monetary
policy shock compared with the predictions of the VAR model introduced in Section 3.4. In
this respect, we should not forget that the DIM is a very small-scale model. While a more sophisticated version of the DIM could do better at reproducing the persistence of the conditional
forecast errors implied by the VAR model, no perfect information model can for the reasons
explained in Section 3.4.
The real e¤ects of money in the estimated DIM are stronger than what the VAR literature
typically …nds. While introducing consumption habits is likely to dampen the response of output, this extension would substantially complicate the solution of the DIM, preventing Bayesian
estimation. Incomplete information on the side of households could also cause consumption and
output to respond more sluggishly to monetary shocks. This extension is discussed in the online appendix. Nakamura and Steinsson (2015) use unexpected changes in interest rates over
a 30-minute window surrounding scheduled Federal Reserve announcements to identify monetary policy shocks in a reduced-form model. These scholars …nd that the response of in‡ation
is small and delayed. They use this evidence to estimate the key parameters of a workhorse
perfect-information New Keynesian model and …nd that the implied real e¤ects of money are
quantitatively larger than what is usually found by the VAR literature.
As shown in the lower graphs of Figure 3, some average expectations respond very sluggishly
to monetary shocks. While these persistent adjustments are crucial for the model to deliver
a degree of persistence in line with the data, they may also raise concerns about what may
appear to be an implausibly long period for …rms to learn the true value of the exogenous state
variables. These concerns will be addressed in Section 4. Shocks to the central bank’s forecast
errors regarding the output gap "x;t propagate across the macroeconomy almost identically to
the monetary shocks "m;t , and hence, their analysis is omitted.27
26

To understand why signaling an adverse technology shock has de‡ationary consequences (black bars in
Figure 4), recall that the average expectations about the real marginal cost in the imperfect-common-knowledge
(k)
(k)
(k 1)
Phillips curve (6) are given by mc
c tjt = y^tjt
b
atjt ; k
1. Note that shocks are orthogonal and hence
(0)

@b
atjt =@"m;t = @b
at =@"m;t = 0. Since expecting an adverse technology shock leads …rms to expect a fall in output
(1)

(1)

(1)

(^
ytjt ), the average …rst-order expectations about the real marginal costs mc
c tjt = y^tjt
down in‡ation and in‡ation expectations. If this …rst-order e¤ect
(k)
(mc
c tjt

(k)
y^tjt

(k 1)
b
atjt ;

(1)
(mc
c tjt

=

(1)
y^tjt

b
at would fall, driving

b
at ) dominates the higher-order

e¤ects
=
k 2), then expecting a negative technology shock will bring about de‡ationary
pressures.
27
The propagation of real-time measurement errors regarding in‡ation is less interesting from the perspective
of this paper and is omitted. The results are available upon request.

24

Output

0.8
0.6

Inflation

0

1

Interest Rate

-0.2

0.1

1Q Infl.Expect

0.1

0

0

-0.1

-0.1

-0.2

-0.2

4Q Infl.Expect

0.5
0.4

-0.4

0.2

-0.6

0

0

-0.8
0

5 10 15 20

0.2

-0.5
0

5 10 15 20

3

-0.3
0

5 10 15 20

0.1

-0.3
0

5 10 15 20

0.06

0

0

5 10 15 20

0

5 10 15 20

0.1

0.04
2

-0.2

0.05

0.02

0.05

1
-0.4

0

-0.6

0
0

5 10 15 20

0
0

5 10 15 20

-0.02
0

5 10 15 20

0
0

5 10 15 20

Figure 5: Impulse Response Functions to a Positive Demand Shock. Upper graphs: Impulse response functions
to a one-standard deviation positive demand shock. Responses are in percent deviation from steady-state values.
The responses of in‡ation, the interest rate, and in‡ation expectations are annualized. The solid lines denote
posterior median of the responses. The gray areas denote 90 percent posterior credible sets. The horizontal axis
in all graphs measures the number of quarters after the shock. Lower graphs: Responses of average expectations
about the …ve exogenous state variables in percentage deviations from their steady-state value. Black solid lines
denote the average …rst-order expectations. Dashed black lines denote the average second-order expectations.
Dashed-dotted lines denote the average third-order expectations.

3.5.2

Propagation of Non-Policy Shocks

The propagation of a one-standard-deviation positive demand shock is described in Figure
5. This …gure shows the responses of output in percentage deviations from its steady state.
The responses of in‡ation, the federal funds rate, and in‡ation expectations are expressed in
annualized percentage deviations from their steady-state value. Interestingly, in‡ation and
in‡ation expectations respond negatively to demand shocks, while output responds positively.
Note that the central bank raises its policy rate in the aftermath of a positive demand shock
leading to two types of signaling e¤ects. First, the monetary tightening induces …rms to believe
that a contractionary deviation from the monetary policy rule has happened. Second, the
observed rise in the federal funds rate induces …rms to believe that a negative technology
shock might have occurred. Figure 6 shows that both of these e¤ects push in‡ation down,28
28

Signaling adverse technology shocks brings about de‡ationary pressures because it leads …rms to anticipate
a fall in output, as discussed in Section 3.5.1.

25

Inflation

1Q Inflation Expectations

0.6

0.3

0.4

0.2

0.2

0.1

0

0

-0.2

-0.1

4Q Inflation Expectations

0.2

-0.4

0.1

0

-0.1

-0.2
-0.2

-0.6

-0.3

-0.8

-0.4

-1

-0.5

-0.3

-0.4

-1.2
5

10

15

20

5

10

15

20

5

10

15

20

Figure 6: Contributions of Average Expectations to the Response of In‡ation and In‡ation Expectations to
Demand Shocks. Parameter values are set equal to the posterior mean. The shock is positive and its size equals
one standard deviation. The solid red line captures the response of in‡ation (left graph), one-quarter-ahead
in‡ation expectations (middle graph), and four-quarters-ahead in‡ation expectations (right graph). The vertical
bars capture the contribution of the true shock as well as that of the average expectations to these responses.

countering the rise in in‡ation due to the positive demand shock, which is captured by the gray
bars. While the second e¤ect (captured by the black bars in Figure 6) has quantitatively a fairly
small impact on in‡ation expectations, the …rst e¤ect (captured by the white bars) appears to
substantially contribute to pushing in‡ation expectations down. Furthermore, the second e¤ect
is generally shorter lived than the …rst one. The …rst e¤ect is very persistent indeed, re‡ecting
the following two facts. First, …rms …nd it hard to disentangle whether changes in the policy
rate are due to exogenous deviations from the monetary rule or are instead due to demand
shocks for reasons that were analyzed in Section 3.2. Second, in the aftermath of a positive
demand shock, monetary policy ends up signaling persistent contractionary deviations from
the monetary rule. The high persistence of the exogenous state variables bm;t and bx;t in the
estimated DIM (Table 1) clearly drives this result because rational …rms know that when the
central bank deviates from the rule, this behavior is expected to last for a fairly long time.
Quite interestingly, the signaling channel transforms demand shocks (^"g;t ) into supply shocks
that move output and in‡ation in opposite directions. Unlike technology shocks, this arti…cial
supply shock implies a negative comovement between the federal funds rate and the rate of
in‡ation, as well as between the interest rate and in‡ation expectations. This property is likely
26

to help the model …t the 1970s, when the policy rate was relatively low while in‡ation and
in‡ation expectations attained quite high values.
A drop in the policy rate owing to a positive technology shock induces …rms to believe
that the central bank is responding to either an expansionary deviation from the monetary
policy rule (bm;t < 0 and bx;t < 0) or a negative demand shock. These two signals turn out to
have almost perfectly o¤setting e¤ects on in‡ation and in‡ation expectations. The fairly high
accuracy of private information about aggregate technology clearly contributes to this result.
Because of this, the propagation of technology shocks is qualitatively the same as that in perfect
information models, with output responding positively and in‡ation responding negatively. The
propagation of technology shocks is analyzed in greater detail in the online appendix.

3.6

The Signaling E¤ects of Monetary Policy

In this section, we use the DIM to empirically assess the signaling e¤ects of monetary policy on
in‡ation and in‡ation expectations. To this end, we run a Bayesian counterfactual experiment
using an algorithm that can be described as follows. In Step 1, for every posterior draw of
the DIM parameters, we obtain the model’s predicted series for the …ve structural shocks (the
aggregate technology shock "a;t , the demand shock, "g;t , the monetary shock "m;t , and the
shocks to the central bank’s measurement errors " ;t and "x;t ) using the two-sided Kalman …lter
and the seven observable variables introduced in Section 3.1. In Step 2, these …ltered series of
shocks are used to simulate the rate of in‡ation and in‡ation expectations from the following
two models: (i) the DIM and (ii) the counterfactual DIM, in which monetary policy has no
signaling e¤ects. The latter model is obtained from the DIM by assuming that …rms do not
^ t . This assumption implies that the signaling channel
observe the history of the policy rate R
is inactive, so …rms form their expectations by using only their private information (i.e., the
history of the signals b
aj;t and gbj;t ). In Step 3, we compute the mean of the simulated series
across posterior draws for the two models.
The shocks are …ltered in Step 1 by using the data set used for estimation and described
in Section 3.1. Since this data set includes both the …nal (HP-…lter-based) output gap and the
real-time output gap from the Greenbook, the errors made by the central bank in measuring
the current output gap ^x;t are identical to the errors measured by Orphanides (2004). This
feature allows us to evaluate the signaling e¤ects of monetary policy after controlling for the
in‡ationary e¤ects due to the Federal Reserve’s persistent mismeasurement of the output gap
in the 1970s, which Orphanides (2001, 2002, 2003) advocate as one of the leading reasons why
in‡ation was so heightened in that decade.29
The solid blue line in the upper graphs of Figure 7 denotes the in‡ation rate (left graph)
29

The series of the real-time output gap is plotted in the online appendix.

27

Inflation

1Q-ahead Infl.Expect.

signaling
no signaling

10

4Q-ahead Infl.Expect.

8
6
6

5

0
1975

1985

1995

4

4

2

2

2005

1975

1985

1995

2005

1985

1995

2005

1975

1985

1995

2005

3

6

3

4

2

2

2

1

1

0

0

0

-2

1975

-1
1975

1985

1995

2005

1975

1985

1995

2005

Figure 7: Signaling E¤ects of Monetary Policy on In‡ation and In‡ation Expectations. Upper graphs: Solid
blue line: in‡ation rate (left graph) and in‡ation expectations (middle and right graphs) simulated from the
estimated dispersed information model (DIM) using the two-sided …ltered shocks from the estimated DIM. Red
dashed line: simulation from the counterfactual DIM, in which the signaling channel is shut down, using the
two-sided …ltered shocks from the estimated DIM. All numbers are annualized and in percent. Lower graphs:
The signaling e¤ects of monetary policy on in‡ation (left graph) and in‡ation expectations (middle and right
graphs). All numbers are annualized and in percent.

and the in‡ation expectations (middle and right graphs) simulated from the DIM using the
two-sided …ltered shocks from the estimated DIM.30 The red dashed line denotes the series
of in‡ation (left graph) and in‡ation expectations (middle and right graphs) simulated from
the counterfactual DIM, in which the signaling channel is shut down. The vertical di¤erence
between the two simulated series in the upper graphs captures the signaling e¤ects of monetary
policy over the sample period and is reported in the lower graphs.
In the model, signaling e¤ects on in‡ation are particularly strong in the 1970s, adding up to
6:4 percentage points to the rate of in‡ation in that decade. Speci…cally, signaling e¤ects play
an important role in explaining why in‡ation was persistently heightened in the second half
of the 1970s. These e¤ects are even more pronounced when one looks at the signaling e¤ects
of monetary policy on in‡ation expectations. The signaling e¤ects on in‡ation expectations
30

The simulated series of in‡ation is by construction the same as in the data. The simulated series of in‡ation
expectations do not exactly replicate the actual data because of the measurement errors we attribute to the
observed in‡ation expectations. However, the discrepancy between these two series is rather minuscule, since
i:i:d: measurement errors just smooth out the simulated series slightly.

28

6

6

4

4

2

2

0

0

-2
-2
1975

1980

1985

1990

1995

2000

2005

1975

1980

1985

1990

1995

2000

2005

1975

1980

1985

1990

1995

2000

2005

4
0

2

-2

0
-2

-4
-4
1975

1980

1985

1990

1995

2000

2005

Figure 8: Contributions of Shocks to Signaling E¤ects on In‡ation Upper graphs: The solid blue line captures
the signaling e¤ects of monetary policy on in‡ation. Red dashed line captures the signaling e¤ects on in‡ation
when only one shock is used for simulation. Left graphs: only aggregate technology shocks are used. Right
graphs: only demand shocks are used. Lower graphs: the two-sided …ltered dynamics of aggregate technology
ât (left) and demand conditions ĝt (right) used for simulation. All numbers are annualized and in percent.

are always positive until the end of the 1990s, largely explaining why in the data in‡ation
expectations were almost always above the rate of in‡ation from 1981:Q2 through the end of
the 1980s.31
To shed light on the origin of the estimated signaling e¤ects on in‡ation, in Figure 8 we
compare the dynamics of the signaling e¤ects on in‡ation (the solid blue line) with the signaling
e¤ects (the red dashed line) that are driven only by technology shocks (upper left graph) and
only by demand shocks (upper right graph).32 In the lower graphs of Figure 8, we show
the two-sided …ltered series of the two exogenous state variables a
^t (left graph) and g^t (right
graph) obtained in Step 1 of the Bayesian counterfactual experiment. We observe that most
of the signaling e¤ects on in‡ation in the 1970s are due to negative demand shocks because
the signaling e¤ects driven only by these shocks (the red dashed line) are similar to the overall
31

In that period, observed one-quarter-ahead (four-quarters-ahead) in‡ation expectations have been 70 basis
points (40 basis points) higher on average than the in‡ation rate.
32
These counterfactual series are obtained by simulating the estimated DIM by using only the two-sided
…ltered estimate of technology and demand shocks. The larger …gure reporting the contribution to signaling
e¤ects of all …ve shocks is available upon request. The omitted shocks are found to contribute only marginally
to the signaling e¤ects of monetary policy on in‡ation and in‡ation expectations.

29

1Q-ahead Infl.Expect.

Only Technology Shocks

Only Demand Shocks

3

3

2

2

1

1

0

0
-1

-1

4Q-ahead Infl.Expect.

1975 1980 1985 1990 1995 2000 2005

1975 1980 1985 1990 1995 2000 2005

3

3

2

2

1

1

0

0

1975 1980 1985 1990 1995 2000 2005

1975 1980 1985 1990 1995 2000 2005

Figure 9: Contributions of Shocks to Signaling E¤ects on In‡ation Expectations. Solid blue line: signaling
e¤ects of monetary policy on in‡ation expectations. Red dashed line: signaling e¤ects on in‡ation expectations
when only one shock is used for simulation. Left graphs: only aggregate technology shocks are used. Right
graphs: only demand shocks are used. Upper graphs: One-quarter-ahead in‡ation expectations. Lower graphs:
Four-quarters-ahead in‡ation expectations.
signaling e¤ects (the blue solid line) in that decade. In particular, two large negative demand
shocks that occurred in 1974 explain the large and positive signaling e¤ects on in‡ation in the
second half of the 1970s. As shown in Section 3.5.2, negative demand shocks prompted the
Federal Reserve to lower the policy rate, which signaled both persistent expansionary monetary
shocks and long-lasting nowcast errors in measuring the output gap by the policymaker. In
Section 4, we will show that there is strong VAR evidence supporting the realization of these
two large demand shocks in 1974 once the signaling e¤ects of monetary policy are taken into
account for identifying these shocks.
Signaling e¤ects associated with positive technology shocks contributed to raising in‡ation
by up to 3 percentage points in 1975-1976. However, this contribution was quite short-lived
because of the predominance of negative technology shocks in the 1970s, which brought about
de‡ationary signaling e¤ects, as shown in the upper left graph of Figure 8. According to
the model, in the 1980s and in the early 1990s, the signaling e¤ects of monetary policy on
in‡ation are predominantly driven by aggregate technology shocks. Improvements in aggregate
technology during this period induced the Federal Reserve to carry out a monetary policy that
ended up signaling expansionary deviations from the monetary policy rule.
30

Inflation

1Q Infl.Expect.

4Q Infl.Expect.

1.5

0.2

0.4

0.18
1

0.16

0.3

0.14
0.2

0.5

0.12
0.1

0

5

10

15

20

0

5

10

15

20

0

5

10

15

20

-0.6
-0.8

0.05

0.05

1

0.04
0.04

0.04
0.03

0.03

0.03

-1

0.5

0.02

0.02

0.01

0.01

0.01

0

0

0

0.02

-1.2
0

-1.4
0

5

10

15

20

0

5

10

15

20

0

5

10

15

20

0

5

10

15

20

0

5

10

15

20

Figure 10: Signaling E¤ects on In‡ation and In‡ation Expectations due to Negative Technology Shocks. Upper
graphs: Response of in‡ation (the left graph), one-quarter-ahead in‡ation expectations (the mddle graph), and
four-quarters-ahead in‡ation expectations (the right graph) to a one-standard-deviation negative technology
shock in the estimated DIM, with signaling e¤ects (the solid blue line) and in the counterfactual DIM, with no
signaling e¤ects (the red dashed line). Lower graphs: Black circles denote the response of the true exogenous
state variables to a negative technology shock. The solid blue line denotes the response of the average …rstorder expectations in the estimated DIM, with signaling e¤ects. The red dashed line denotes the response of
the average …rst-order expectations in the counterfactual DIM, with no signaling e¤ects.

The main drivers of signaling e¤ects on in‡ation expectations are shown in Figure 9. These
graphs compare the dynamics of the signaling e¤ects on in‡ation expectations (blue solid line)
with the technology-driven (left graphs) and the demand-driven (right graphs) signaling e¤ects
on the one-quarter-ahead (upper graphs) and four-quarters-ahead (lower graphs) in‡ation expectations, which are denoted with the red dashed line. Similar to the signaling e¤ects on
in‡ation, the signaling e¤ects on in‡ation expectations during the 1970s are largely driven by
demand shocks (see the right plots). The red dashed line in the left graphs of Figure 9 shows
that technology-driven signaling e¤ects on in‡ation expectations started building up slowly in
the 1970s, which was a decade characterized by large and repeated negative technology shocks.
This slow-moving pattern suggests that technology shocks bring about delayed signaling e¤ects
on in‡ation expectations. This pattern is fairly di¤erent from the dynamics that characterized
the technology-driven signaling e¤ects on in‡ation, which move around the zero line during the
1970s in the upper left graph of Figure 8. The improvements in aggregate technology observed

31

from 1982 through the early 1990s slowly bring about a downward trend in the technology-driven
signaling e¤ects on in‡ation expectations. However, these e¤ects are delayed and signaling effects on in‡ation expectations remain positive until the mid-1990s. Thus, technology-driven
signaling e¤ects contribute to explaining why in‡ation expectations were higher on average
than in‡ation throughout the 1980s.
Why do negative technology shocks raise in‡ation expectations through the signaling channel with delays? To investigate this question, in Figure 10 we show the response of in‡ation
(the upper left graph) and in‡ation expectations (the upper middle and right graphs) to a
one-standard-deviation negative technology shock in the estimated DIM (the solid blue line)
and in the counterfactual DIM with no signaling e¤ects (the red dashed line). The di¤erence
between these two lines captures the signaling e¤ects due to negative technology shocks. Two
features deserve to be emphasized. First, while signaling e¤ects associated with technology
shocks predominantly a¤ect in‡ation at short horizons, in‡ation expectations are primarily in‡uenced at longer horizons. Second, signaling e¤ects on in‡ation and in‡ation expectations
switch in sign and become in‡ationary a few quarters past the shock. This happens because six
quarters after a negative technology shock, …rms consider the policy rate to be lower than the
level that they would have expected based on their beliefs about in‡ation and the output gap.
Consequently, monetary policy starts signaling long-lasting expansionary deviations from the
monetary rule (^m;t < 0 and ^x;t < 0), as shown in the lower graphs of Figure 10. This suggests
that large negative technology shocks that occurred in the late 1970s and early 1980s brought
about signaling e¤ects of monetary policy that contributed to slowly raising in‡ation expectations well into the 1980s. Conversely, improvements in technological conditions throughout the
1980s caused signaling e¤ects on in‡ation expectations to slowly fall from mid-1980s through
the end of the 1990s, as shown in the left graphs of Figure 9.

4

Discussion

The sluggish dynamics of beliefs in the DIM seem to be quite successful in explaining the persistent macroeconomic dynamics of in‡ation and in‡ation expectations. However, one may argue
that such persistent dynamics of beliefs imply that …rms are implausibly confused about the
aggregate state of the economy. To mitigate this concern, we have included one-quarter-ahead
and four-quarters-ahead in‡ation expectations in our data set for estimation. In addition, an
important check to assess the plausibility of the information set is to compare the nowcast errors
(1)
for in‡ation predicted by the DIM (bt btjt ) to those measured by the Survey of Professional
Forecasters. Figure 11 shows this comparison. The two nowcast errors exhibit a great deal of
comovement with a correlation coe¢ cient of 0:82. Furthermore, the mean of the absolute nowcast errors for in‡ation is 0.79 in the model vis-a-vis 0.81 in the data. This result suggests that
32

Inflation Nowcast Errors
4
Model-Implied Nowcast Errors
SPF Nowcast Error

3

2

1

0

-1

1975

1980

1985

1990

1995

2000

2005

Figure 11: In‡ation Nowcast Errors. The model-implied nowcast errors are obtained by subtracting the
(0)
smoothed estimates of …rms’in‡ation nowcasts (i.e., ln + ^ tjt ) from the realized in‡ation rate. Smoothed
estimates are obtained by setting the value of the DIM parameters to their posterior mean. Nowcast errors are
reported in percentage points of annualized rates.

the degree of information incompleteness in the estimated DIM is not implausible. It should
also be noted that perfect information models predict that nowcasts errors are counterfactually
equal to zero.
We showed that the signaling channel explains the heightened in‡ation and in‡ation expectations observed in the 1970s because of two large negative demand shocks that occurred in
1974. These two shocks caused the Federal Reserve to lower the policy rate, signaling expansionary monetary shocks and the central bank’s mismeasurement of the output gap. Were there
negative demand shocks in 1974? Recall that the signaling channel mutes the propagation of
demand shocks so that they look like supply shocks, moving output and in‡ation in opposite
directions (Figure 5). We know that traditional demand shocks were not so important in the
1970s. But what about demand shocks after controlling for the signaling e¤ects of monetary
policy? Is there any evidence that these demand shocks in disguise actually occurred in the
1970s and, more speci…cally, in 1974? The answer to this question is yes. To reach this conclusion, we estimate a Bayesian VAR model using a large data set that includes GDP growth,
consumption growth, investment growth, the growth rate of real compensation per hour, the
growth rate of money (M2), the federal funds rate, the in‡ation rate, the growth rate of labor

33

Demand Shocks
3
2
1
0
-1
-2
-3

1975

1980

1985

1990

1995

2000

2005

Figure 12: The solid black line denotes the posterior median of the demand shocks identi…ed using the VAR
model. The red dashed line captures the posterior median of the smoothed estimates of demand shocks implied
by the dispersed information model.

productivity, one-quarter-ahead (SPF) in‡ation expectations, and four-quarters-ahead (SPF)
in‡ation expectations. Apart from the last two observables, this data set is very similar to the
one used in the in‡uential study by Christiano, Eichenbaum and Evans (2005).
Demand shocks are identi…ed by using sign restrictions that are consistent with the DIM
(Figure 5); that is, positive demand shocks are assumed to lower in‡ation and to raise output as
well as the federal funds rate for the …rst …ve quarters. Figure 12 shows the historical sequence
of demand shocks identi…ed by applying these sign restrictions to the VAR (solid black line)
along with the two-sided estimate of these innovations identi…ed using the estimated DIM (red
dashed line). First, the two series seem to be quite positively correlated, with the VAR estimates
being less volatile. The coe¢ cient of correlation is slightly below 0.60. Second, judging from
the series of the demand innovations implied by the VAR model (i.e., the red dashed line), it
looks like the …rst half of the 1970s was characterized by a few of these large negative demand
shocks in disguise. Third, consistent with the estimated DIM, the VAR model suggests that
the two largest negative demand shocks of the sample were realized in 1974, which is marked
by the gray area in the plot. According to the estimated DIM, these two large negative demand
shocks gave rise to sizable signaling e¤ects on in‡ation and in‡ation expectations throughout
the second half of the 1970s.

34

On the narrative side, 1974 was a year of high political uncertainty in the U.S. because of
the unraveling of the so-called Watergate scandal, which led the House of Representatives to
open the impeachment process against President Richard Nixon that year. The scandal started
in 1972 but it arguably became a major constitutional crisis starting on February 6, 1974, when
the House of Representatives approved a resolution giving the Judiciary Committee authority to
investigate impeachment of the President.33 On July 27, 1974, the House Judiciary Committee
voted to recommend the …rst article of impeachment against the President: obstruction of
justice. The House recommended the second article, abuse of power, on July 29, 1974. The
next day, on July 30, 1974, the House recommended the third article: contempt of Congress.
On August 9, 1974, President Richard Nixon resigned. These events undoubtedly marked a
period of high political uncertainty for U.S. households that might well have had an impact on
how they discounted future events.
Another concern has to do with the assumption that …rms observe only one endogenous
variable, the interest rate, and all the remaining private information comes from exogenous signals. As discussed in Section 2.6, our information structure is built on the imperfect-commonknowledge literature (Woodford 2002; Adam 2007; Nimark 2008). However, one may be reasonably concerned that …rms are not allowed, for instance, to use information about the quantities
they sell for price-setting decisions. The log-linear approximation to Equation (2) implies that
observing the quantities sold would be one additional endogenous signal that would perfectly
reveal nominal output to …rms. We …nd that estimating a DIM in which …rms perfectly observe nominal output would deliver a substantially lower marginal likelihood (-586.76<-319.89),
suggesting that this alternative speci…cation of the DIM …ts the data rather poorly. Allowing
…rms to perfectly observe nominal output ends up endowing them with too much information,
critically weakening the ability of the dispersed information model to generate macroeconomic
‡uctuations with the right degree of persistence. This is particularly true for the case of the
federal funds rate and for the observed in‡ation expectations. This empirical shortcoming of
the DIM in which …rms observe nominal output cannot be …xed by simply dropping the exogenous signals aj;t and gj;t from …rms’information set. This …nding suggests that …rms may
not pay attention to nominal output when making their price-setting decisions, even though
information about this variable is arguably quite cheap to obtain. This result is in line with
the empirical study by Andrade et al. (2014), who use the Blue Chip Financial Forecasts to
document that disagreement about in‡ation and GDP is quite high at short horizons.
33

Even though many resolutions to impeach the President were submitted in 1972 and 1973, the Judiciary
Committee always refused to take up the case.

35

5

Concluding Remarks

This paper introduces a dynamic general equilibrium model in which information is dispersed
across price setters and the interest rate set by the central bank has signaling e¤ects. In
this model, monetary impulses propagate through two channels: (i) the channel based on
the central bank’s ability to a¤ect the real interest rate due to price stickiness and dispersed
information and (ii) the signaling channel. The latter arises because changing the policy rate
conveys information about the central bank’s assessment of in‡ation and the output gap to
price setters.
We …t the model to a data set that includes the Survey of Professional Forecasters as a
measure of price setters’in‡ation expectations. We perform an econometric evaluation of the
model with signaling e¤ects of monetary policy, showing that this model can closely replicate
the response of in‡ation expectations to monetary shocks implied by a VAR model. We also
…nd that the signaling channel makes demand shocks look like supply shocks that move in‡ation
and output in opposite directions. Moreover, we show that the signaling e¤ects of monetary
policy can account for why in‡ation and in‡ation expectations were so persistently heightened
in the 1970s.34
While there exist several channels through which central banks can communicate with markets nowadays, our paper focuses on interest-rate-based communication. Interest-rate-based
communication was virtually the only form of the central bank’s communication until February
1994 in the U.S. (Campbell et al., 2012). The importance of this type of communication has
been growing in recent years. See, for instance, the widespread endorsement of the practice of
providing information about the likely future path of the policy rate, which goes by the name
of forward guidance. While we do not study the e¤ects of forward guidance in this paper, we
have shown how to formalize interest-rate-based communication in dynamic general equilibrium
models and how to use these models to formally evaluate the macroeconomic e¤ects of this type
of communication.
Changes in the Federal Reserve’s attitude toward in‡ation stabilization have been documented by Davig and Leeper (2007), Justiniano and Primiceri (2008), Fernández-Villaverde,
Guerrón-Quintana and Rubio-Ramírez (2010) and Bianchi (2013). Time-varying model parameters allow us to study how the signaling e¤ects of monetary policy on the macroeconomy
have changed over time. This fascinating topic is left for future research.
34

Other popular theories for why in‡ation rose in the 1970s are (i ) the bad luck view (e.g., Cogley and Sargent
2005; Sims and Zha 2006; Primiceri 2005; and Liu, Waggoner, and Zha 2011), (ii ) the lack of commitment view
(e.g., Chari, Christiano, and Eichenbaum 1998; Christiano and Gust 2000), (iii ) the policy mistakes view
(e.g., Sargent 2001; Clarida, Galí, and Gertler 2000; Lubik and Schorfheide 2004; Primiceri 2006; Coibion and
Gorodnichenko 2011), and (iv ) the …scal and monetary interactions view (e.g., Sargent, Williams, and Zha 2006;
Bianchi and Ilut 2016; Bianchi and Melosi, 2014b).

36

References
Adam, Klaus. 2007. “Optimal Monetary Policy with Imperfect Common Knowledge.”Journal
of Monetary Economics, 54(2): 267–301.
Andrade, Philippe, Richard K. Crump, Stefano Eusepi, and Emanuel Moench. 2014.
“Noisy information and fundamental disagreement.”Society for Economic Dynamics Meeting
Papers 797.
An, Sungbae, and Frank Schorfheide. 2007. “Bayesian Analysis of DSGE Models.”Econometric Reviews, 26(2-4): 113–172.
Ascari, Guido, and Argia M. Sbordone. 2014. “The Macroeconomics of Trend In‡ation.”
Journal of Economic Literature, 52(3): 679–739.
Bianchi, Francesco. 2013. “Regime Switches, Agents’Beliefs, and Post-World War II U.S.
Macroeconomic Dynamics.”Review of Economic Studies, 80(2): 463–490.
Bianchi, Francesco, and Cosmin Ilut. 2016. “Monetary/Fiscal Policy Mix and Agents’
Beliefs.”National Bureau of Economic Research, Inc Working Papers 20194.
Bianchi, Francesco, and Leonardo Melosi. 2014a. “Constrained Discretion and Central
Bank Transparency.”National Bureau of Economic Research Working Paper 20566.
Bianchi, Francesco, and Leonardo Melosi. 2014b. “Dormant Shocks and Fiscal Virtue.”
In NBER Macroeconomics Annual 2013, Volume 28. NBER Chapters, 1–46. National Bureau
of Economic Research, Inc.
Calvo, Guillermo A. 1983. “Staggered Prices in a Utility Maximizing Framework.” Journal
of Monetary Economics, 12(3): 383–398.
Campbell, Je¤rey R., Charles L. Evans, Jonas D.M. Fisher, and Alejandro Justiniano. 2012. “Macroeconomic E¤ects of Federal Reserve Forward Guidance.” Brookings
Papers on Economic Activity, 44(1 (Spring)): 1–80.
Campbell, Je¤rey R., Jonas D.M. Fisher, Alejandro Justiniano, and Leonardo
Melosi. forthcoming. “Forward Guidance and Macroeconomic Outcomes Since the Financial
Crisis.”In NBER Macroeconomics Annual 2016.
Chari, Varadarajan V., Lawrence J. Christiano, and Martin Eichenbaum. 1998.
“Expectation Traps and Discretion.”Journal of Economic Theory, 81(2): 462–492.

37

Christiano, Lawrence J., and Christopher Gust. 2000. “The expectations trap hypothesis.”Economic Perspectives, 21–39.
Christiano, Lawrence J., Martin Eichenbaum, and Charles L. Evans. 2005. “Nominal
Rigidities and the Dynamic E¤ects of a Shock to Monetary Policy.” Journal of Political
Economy, 113(1): 1–45.
Clarida, Richard, Jordi Gali, and Mark Gertler. 2000. “Monetary Policy Rules and
Macroeconomic Stability: Evidence and Some Theory.”The Quarterly Journal of Economics,
115(1): 147–180.
Cogley, Timothy, and Thomas J. Sargent. 2005. “Drift and Volatilities: Monetary Policies
and Outcomes in the Post WWII U.S.”Review of Economic Dynamics, 8(2): 262–302.
Coibion, Olivier, and Yuriy Gorodnichenko. 2011. “Monetary Policy, Trend In‡ation,
and the Great Moderation: An Alternative Interpretation.” American Economic Review,
101(1): 341–370.
Coibion, Olivier, and Yuriy Gorodnichenko. 2012a. “What Can Survey Forecasts Tell Us
about Information Rigidities?” Journal of Political Economy, 120(1): 116 –159.
Coibion, Olivier, and Yuriy Gorodnichenko. 2012b. “Why Are Target Interest Rate
Changes So Persistent?” American Economic Journal: Macroeconomics, 4(4): 126–162.
Cover, Thomas M., and Joy A. Thomas. 1991. Elements of Information Theory. New
York: Wiley.
Davig, Troy, and Eric M. Leeper. 2007. “Generalizing the Taylor Principle.” American
Economic Review, 97(3): 607–635.
Del Negro, Marco, and Frank Schorfheide. 2008. “Forming Priors for DSGE Models (and
How It A¤ects the Assessment of Nominal Rigidities).” Journal of Monetary Economics,
55(7): 1191–1208.
Del Negro, Marco, and Frank Schorfheide. 2011. “Bayesian macroeconometrics.” The
Oxford Handbook of Bayesian Econometrics, 293–389.
Del Negro, Marco, and Stefano Eusepi. 2011. “Fitting observed in‡ation expectations.”
Journal of Economic Dynamics and Control, 35(12): 2105–2131.
Del Negro, Marco, Frank Schorfheide, Rafael Smets, and Wouters. 2007. “On the Fit
of New Keynesian Models.”Journal of Business and Economic Statistics, 25(2): 123–143.
38

Erceg, Christopher J., and Andrew T. Levin. 2003. “Imperfect Credibility and In‡ation
Persistence.”Journal of Monetary Economics, 50(4): 915–944.
Fernández-Villaverde, Jesús, and Juan F. Rubio-Ramírez. 2004. “Comparing Dynamic
Equilibrium Models to Data: A Bayesian Approach.”Journal of Econometrics, 123(1): 153–
187.
Fernández-Villaverde, Jesús, Pablo Guerrón-Quintana, and Juan F. RubioRamírez. 2010. “Fortune or Virtue: Time-Variant Volatilities Versus Parameter Drifting
in U.S. Data.”National Bureau of Economic Research, Inc NBER Working Papers 15928.
Geweke, John. 2005. Contemporary Bayesian Econometrics and Statistics. Hoboken: John
Wiley and Sons.
Gorodnichenko, Yuriy. 2008. “Endogenous Information, Menu Costs and In‡ation Persistence.”National Bureau of Economic Research, Inc NBER Working Papers 14184.
Hellwig, Christian. 2002. “Public Announcements, Adjustment Delays and the Business
Cycle.”Mimeo.
Hellwig, Christian, and Venky Vankateswaran. 2009. “Setting the Right Price for the
Wrong Reasons.”Journal of Monetary Economics, 56(S): S57–S77.
Justiniano, Alejandro, and Giorgio E. Primiceri. 2008. “The Time-Varying Volatility of
Macroeconomic Fluctuations.”American Economic Review, 98(3): 604–641.
Leeper, Eric M., Nora Traum, and Todd B. Walker. 2015. “Clearing Up the Fiscal
Multiplier Morass.”National Bureau of Economic Research Working Paper 17444.
Liu, Zheng, Daniel F. Waggoner, and Tao Zha. 2011. “Sources of Macroeconomic Fluctuations: A Regime-Switching DSGE Approach.”Quantitative Economics, 2(2): 251–301.
Lorenzoni, Guido. 2009. “A Theory of Demand Shocks.” American Economic Review,
99(5): 2050–2084.
Lubik, Thomas A., and Frank Schorfheide. 2004. “Testing for Indeterminacy: An Application to U.S. Monetary Policy.”American Economic Review, 94(1): 190–217.
Maćkowiak, Bartosz, and Mirko Wiederholt. 2009. “Optimal Sticky Prices under Rational Inattention.”American Economic Review, 99(3): 769–803.
Maćkowiak, Bartosz, and Mirko Wiederholt. 2015. “Business Cycle Dynamics under
Rational Inattention.”Review of Economic Studies, 82(4): 1502–1532.
39

Maćkowiak, Bartosz, Emanuel Moench, and Mirko Wiederholt. 2009. “Sectoral Price
Data and Models of Price Settings.”Journal of Monetary Economics, 56(S): S78–S99.
Mankiw, Gregory N., and Ricardo Reis. 2002. “Sticky Information versus Sticky Prices:
A Proposal to Replace the New Keynesian Phillips Curve.”Quarterly Journal of Economics,
117(4): 1295–1328.
Matejka, Filip. 2016. “Rationally Inattentive Seller: Sales and Discrete Pricing.”The Review
of Economic Studies, 83(3): 1125–1155.
Melosi, Leonardo. 2014. “Estimating Models with Dispersed Information.” American Economic Journal: Macroeconomics, 6(1): 1–31.
Morris, Stephen, and Hyun S. Shin. 2002. “Social Value of Public Information.”American
Economic Review, 92(5): 1521–1534.
Morris, Stephen, and Hyun S. Shin. 2003. “Global Games: Theory and Applications.”In
Advances in Economics and Econometrics, the Eighth World Congress. , ed. M. Dewatripont,
L. Hansen and S. Turnovsky. Cambridge: Cambridge University Press.
Nakamura, Emi, and Jon Steinsson. 2015. “High Frequency Identi…cation of Monetary
Non-Neutrality.”National Bureau of Economic Research, Inc NBER Working Papers 19260.
Nimark, Kristo¤er. 2008. “Dynamic Pricing and Imperfect Common Knowledge.” Journal
of Monetary Economics, 55(2): 365–382.
Nimark, Kristo¤er. 2011. “Dynamic Higher Order Expectations.”Mimeo.
Nimark, Kristo¤er. 2014a. “A Low Dimensional Kalman Filter for Systems with Lagged
Observables.”Mimeo.
Nimark, Kristo¤er P. 2014b. “Man-Bites-Dog Business Cycles.”American Economic Review,
104(8): 2320–2367.
Orphanides, Athanasios. 2001. “Monetary Policy Rules Based on Real-Time Data.”American Economic Review, 91(4): 964–985.
Orphanides, Athanasios. 2002. “Monetary-Policy Rules and the Great In‡ation.”American
Economic Review, 92(2): 115–120.
Orphanides, Athanasios. 2003. “The Quest for Prosperity Without In‡ation.” Journal of
Monetary Economics, 50(3): 633–663.

40

Orphanides, Athanasios. 2004. “Monetary Policy Rules, Macroeconomic Stability, and In‡ation: A View from the Trenches.”Journal of Money, Credit and Banking, 36(2): 151–175.
Paciello, Luigi, and Mirko Wiederholt. 2014. “Exogenous Information, Endogenous Information, and Optimal Monetary Policy.”Review of Economic Studies, 81(1): 356–388.
Primiceri, Giorgio E. 2005. “Time Varying Structural Vector Autoregressions and Monetary
Policy.”Review of Economic Studies, 72(3): 821–852.
Primiceri, Giorgio E. 2006. “Why In‡ation Rose and Fell: Policymakers’ Beliefs and US
Postwar Stabilization Policy.”The Quarterly Journal of Economics, 121: 867–901.
Rabanal, Pau, and Juan F. Rubio-Ramírez. 2005. “Comparing New Keynesian models of
the business cycle: A Bayesian approach.”Journal of Monetary Economics, 52: 1151–1166.
Romer, David H., and Christina D. Romer. 2000. “Federal Reserve Information and the
Behavior of Interest Rates.”American Economic Review, 90(3): 429–457.
Rondina, Giacomo, and Todd B. Walker. 2012. “Information Equilibria in Dynamic
Economies with Dispersed Information.”Mimeo.
Rotemberg, Julio, and Michael Woodford. 1997. “An Optimization-Based Econometric
Framework for the Evaluation of Monetary Policy.”In NBER Macroeconomics Annual 1997,
Volume 12. NBER Chapters, 297–361. National Bureau of Economic Research, Inc.
Rudebusch, Glenn D. 2002. “Term structure evidence on interest rate smoothing and monetary policy inertia.”Journal of Monetary Economics, 49(6): 1161–1187.
Rudebusch, Glenn D. 2006. “Monetary Policy Inertia: Fact or Fiction?” International
Journal of Central Banking, 2(4).
Sargent, Thomas J. 2001. The Conquest of American In‡ation. Princeton NJ: Princeton
University Press.
Sargent, Thomas, Noah Williams, and Tao Zha. 2006. “Shocks and Government Beliefs:
The Rise and Fall of American In‡ation.”American Economic Review, 96(4): 1193–1224.
Schorfheide, Frank. 2000. “Loss Function-Based Evaluation of DSGE Models.” Journal of
Applied Econometrics, 15(6): 645–670.
Sims, Christopher A. 2003. “Implications of Rational Inattention.” Journal of Monetary
Economics, 50(3): 665–690.

41

Sims, Christopher A. 2006. “Rational Inattention: Beyond the Linear Quadratic Case.”
American Economic Review, 96(2): 158–163.
Sims, Christopher A. 2010. “Rational Inattention and Monetary Economics.” In Handbook
of Monetary Economics. Vol. 3A, , ed. Benjamin Friedman and Michael Woodford. New
York: North Holland.
Sims, Christopher A., and Tao Zha. 2006. “Were There Regime Switches in U.S. Monetary
Policy?” American Economic Review, 96(1): 54–81.
Sims, Christopher Albert, and Thao Zha. 1998. “Bayesian Methods For Dynamic Multivariate Models.”International Economic Review, 39(4): 949–968.
Smets, Frank, and Rafael Wouters. 2007. “Shocks and Frictions in US Business Cycles: A
Bayesian DSGE Approach.”American Economic Review, 97(3): 586–606.
Townsend, Robert M. 1983a. “Equilibrium Theory with Learning and Disparate Information.” In Individual Forecasting and Aggregate Outcomes. , ed. R. Frydman and Edmund S.
Phelps, 169–197. Cambridge:Cambridge University Press.
Townsend, Robert M. 1983b. “Forecasting the Forecasts of Others.” Journal of Political
Economy, 91(4): 546–588.
Trabandt, Mathias. 2007. “Sticky Information vs. Sticky Prices: A Horse Race in a DSGE
Framework.”Sveriges Riksbank (Central Bank of Sweden) Working Paper Series 209.
Uhlig, Harald. 2005. “What are the E¤ects of Monetary Policy on Output? Results from an
Agnostic Identi…cation Procedure.”Journal of Monetary Economics, 52: 381–419.
Woodford, Michael. 2002. “Imperfect Common Knowledge and the E¤ects of Monetary
Policy.”In Knowledge, Information, and Expectations in Modern Macroeconomics: In Honor
of Edmund S. Phelps. , ed. Philippe Aghion, Roman Frydman, Joseph Stiglitz and Michael
Woodford, 25–58. Princeton: Princeton University Press.

42

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.
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:
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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

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Marco Bassetto, Marco Cagetti, and Mariacristina De Nardi

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Lisa Barrow and Cecilia Elena Rouse

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WP-13-13

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WP-13-22

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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
Mark L. J. Wright

WP-14-06

AIG in Hindsight
Robert McDonald and Anna Paulson

WP-14-07

On the Structural Interpretation of the Smets-Wouters “Risk Premium” Shock
Jonas D.M. Fisher

WP-14-08

Human Capital Risk, Contract Enforcement, and the Macroeconomy
Tom Krebs, Moritz Kuhn, and Mark L. J. Wright

WP-14-09

Adverse Selection, Risk Sharing and Business Cycles
Marcelo Veracierto

WP-14-10

Core and ‘Crust’: Consumer Prices and the Term Structure of Interest Rates
Andrea Ajello, Luca Benzoni, and Olena Chyruk

WP-14-11

The Evolution of Comparative Advantage: Measurement and Implications
Andrei A. Levchenko and Jing Zhang

WP-14-12

Saving Europe?: The Unpleasant Arithmetic of Fiscal Austerity in Integrated Economies
Enrique G. Mendoza, Linda L. Tesar, and Jing Zhang

WP-14-13

Liquidity Traps and Monetary Policy: Managing a Credit Crunch
Francisco Buera and Juan Pablo Nicolini

WP-14-14

Quantitative Easing in Joseph’s Egypt with Keynesian Producers
Jeffrey R. Campbell

WP-14-15

3

Working Paper Series (continued)
Constrained Discretion and Central Bank Transparency
Francesco Bianchi and Leonardo Melosi

WP-14-16

Escaping the Great Recession
Francesco Bianchi and Leonardo Melosi

WP-14-17

More on Middlemen: Equilibrium Entry and Efficiency in Intermediated Markets
Ed Nosal, Yuet-Yee Wong, and Randall Wright

WP-14-18

Preventing Bank Runs
David Andolfatto, Ed Nosal, and Bruno Sultanum

WP-14-19

The Impact of Chicago’s Small High School Initiative
Lisa Barrow, Diane Whitmore Schanzenbach, and Amy Claessens

WP-14-20

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

WP-14-22

What Drives Bank Funding Spreads?
Thomas B. King and Kurt F. Lewis

WP-14-23

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

WP-14-25

Private Takings
Alessandro Marchesiani and Ed Nosal

WP-14-26

Momentum Trading, Return Chasing, and Predictable Crashes
Benjamin Chabot, Eric Ghysels, and Ravi Jagannathan

WP-14-27

Early Life Environment and Racial Inequality in Education and Earnings
in the United States
Kenneth Y. Chay, Jonathan Guryan, and Bhashkar Mazumder

WP-14-28

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

4

Working Paper Series (continued)
Risk Management for Monetary Policy Near the Zero Lower Bound
Charles Evans, Jonas Fisher, François Gourio, and Spencer Krane
Estimating the Intergenerational Elasticity and Rank Association in the US:
Overcoming the Current Limitations of Tax Data
Bhashkar Mazumder

WP-15-03

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

WP-15-06

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
Lee E. Ohanian, Paulina Restrepo-Echavarria, and Mark L. J. Wright

WP-15-08

Backtesting Systemic Risk Measures During Historical Bank Runs
Christian Brownlees, Ben Chabot, Eric Ghysels, and Christopher Kurz

WP-15-09

What Does Anticipated Monetary Policy Do?
Stefania D’Amico and Thomas B. King

WP-15-10

Firm Entry and Macroeconomic Dynamics: A State-level Analysis
François Gourio, Todd Messer, and Michael Siemer

WP-16-01

Measuring Interest Rate Risk in the Life Insurance Sector: the U.S. and the U.K.
Daniel Hartley, Anna Paulson, and Richard J. Rosen

WP-16-02

Allocating Effort and Talent in Professional Labor Markets
Gadi Barlevy and Derek Neal

WP-16-03

The Life Insurance Industry and Systemic Risk: A Bond Market Perspective
Anna Paulson and Richard Rosen

WP-16-04

Forecasting Economic Activity with Mixed Frequency Bayesian VARs
Scott A. Brave, R. Andrew Butters, and Alejandro Justiniano

WP-16-05

Optimal Monetary Policy in an Open Emerging Market Economy
Tara Iyer

WP-16-06

Forward Guidance and Macroeconomic Outcomes Since the Financial Crisis
Jeffrey R. Campbell, Jonas D. M. Fisher, Alejandro Justiniano, and Leonardo Melosi

WP-16-07

5

Working Paper Series (continued)
Insurance in Human Capital Models with Limited Enforcement
Tom Krebs, Moritz Kuhn, and Mark Wright

WP-16-08

Accounting for Central Neighborhood Change, 1980-2010
Nathaniel Baum-Snow and Daniel Hartley

WP-16-09

The Effect of the Patient Protection and Affordable Care Act Medicaid Expansions
on Financial Wellbeing
Luojia Hu, Robert Kaestner, Bhashkar Mazumder, Sarah Miller, and Ashley Wong

WP-16-10

The Interplay Between Financial Conditions and Monetary Policy Shock
Marco Bassetto, Luca Benzoni, and Trevor Serrao

WP-16-11

Tax Credits and the Debt Position of US Households
Leslie McGranahan

WP-16-12

The Global Diffusion of Ideas
Francisco J. Buera and Ezra Oberfield

WP-16-13

Signaling Effects of Monetary Policy
Leonardo Melosi

WP-16-14

6