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Confidence and the Transmission of Policy Shocks∗
Ruediger Bachmann
University of Michigan and NBER
Eric R. Sims
University of Notre Dame and NBER
August 11, 2010
Abstract
A widespread belief amongst economists, policy-makers, and members of the media
is that the “confidence”of households and firms is a critical component of the trans-
mission of fiscal and monetary policys shocks into economic activity. In this paper
we take this proposition to the data. We use common restrictions from the literature
to identify monetary and fiscal policy shocks in multivariate systems augmented with
measures of consumer and/or business confidence. We then construct counterfactual
impulse responses in which the endogenous response of confidence to policy is “shut
down”. Comparing the unconstrained and counterfactual responses of economic aggre-
gates allows us to determine the importance of confidence as a transmission mechanism
of policy. Typically, the counterfactual responses of GDP are dampened compared to
the unconstrained ones. Confidence, depending on the specification, can account for as
much as 100 percent of the quantitative magnitude of the responses, though often the
counterfactual responses of GDP are insignificantly different from the unconstrained
responses. Confidence appears to be more important in the transmission of tax and
monetary policy shocks than spending shocks, and business confidence is quantitatively
more important than consumer confidence.
∗Contact information: [email protected] and [email protected]. We are grateful to seminar participants atNotre Dame and Rochester for helpful comments and suggestions. Any remaining errors are our own.
“But the hope that monetary and fiscal policies would prevent continued weakness by boosting
consumer confidence was derailed by the recent report that consumer confidence in January
collapsed to the lowest level since 1992.” —Martin Feldstein, Wall Street Journal, February
20, 2008
“Confidence matters independently of fundamentals!”—Roger Farmer, May 29, 2010
1 Introduction
A widespread belief amongst economists, policy-makers, and members of the news media is
that the “confidence” of households and firms is a critical component of the transmission
of policy shocks into economic activity. A sampling of quotes from economists and policy-
makers with wide-ranging economic and political philosophies attest to this fact (see the
Appendix for more). A large literature studies the effects of fiscal and monetary policy
shocks on the real economy, while a smaller though not insubstantial literature examines the
effects of confidence in the economy. To our knowledge, however, no study bridges these two
literatures and explicitly examines the relationship between confidence and the transmission
of policy shocks. This paper fills that void.
We begin by reviewing some of the literature seeking to identify the effects of both fiscal
and monetary policy shocks in the economy. Most of these studies use the structural vector
autoregression (VAR) methodology, sometimes combined with an “event study”component,
to be described in more detail below. For fiscal policy, we examine the identification ap-
proaches of Blanchard and Perotti (2002), and Romer and Romer (2007). Blanchard and
Perotti combine institutional knowledge of the automatic stabilizer features of the tax and
spending system to identify both tax and spending shocks. Romer and Romer (2007) use
a narrative approach to come up with an exogenous tax changes series. For monetary pol-
icy, we use the Romer and Romer (1989) narrative approach, the Christiano, Eichenbaum,
and Evans (1999 and 2005) recursive VAR approaches, and the Romer and Romer (2004)
narrative-based approach.
Our paper is also related to the literature studying the effects of changes in consumer and
business confidence on economic activity. Carroll, Fuhrer, and Wilcox (1994), Matsusaka
and Sbordonne (1994), and Barsky and Sims (2010) try to measure how important changes in
confidence are in predicting the current and future paths of macroeconomic variables. This
literature hypothesizes that shocks to confidence may themselves be a source of economic
fluctuations, whereas in the current paper we study whether or not confidence is an important
source of the transmission and propagation of policy shocks.
1
To shed light on this question, we imbed measures of consumer and business confidence
into the VARs customarily used to identify monetary and fiscal policy shocks. We verify
that the inclusion of confidence in the system does little to alter either the qualitative or
quantitative dynamics of the response of economic activity to policy shocks. Further, con-
fidence typically responds in the way one would expect to policy shocks (i.e. contractionary
monetary policy shocks lead to reduced confidence). Business confidence tends to respond
both earlier and by more than does consumer confidence.
We then construct counterfactual impulse responses in which the level of confidence is held
constant in response to the policy shocks. In effect, this approach “turns off”the endogenous
response of confidence to policy, and in so doing also “turns off”the endogenous response
of activity to the endogenous response of confidence. Our methodology is an application
of an approach originally proposed in Sims and Zha (2006) to study the importance of
the systematic component of monetary policy. It has also been used to study the role of
monetary policy in the transmission of oil price shocks into the real economy (Bernanke,
Gertler, and Watson (1998) and Kilian and Lewis (2010)).
We find that confidence generally plays a modest role in the transmission and propagation
of policy shocks. In particular, “shutting down” the response of confidence to fiscal or
monetary policy shocks generally leads to an impulse response of output that is dampened
compared to the unconstrained impulse response, though the contribution of confidence is
in most cases statistically insignificant. Nevertheless, we find that confidence accounts from
anywhere between 100 percent and nothing of the response of real GDP to policy shocks.
Confidence appears to play a more important role in the transmission of monetary policy and
tax shocks than it does for government spending shocks. CEO confidence is also typically
a more important part of the transmission mechanism than is consumer confidence.
The remainder of the paper is organized as follows. Section 2 discusses potential mecha-
nisms why confidence might matter for the transmission of policy shocks. Section 3 reviews
the literature on identifying policy shocks. It also briefly reviews work examining the role of
confidence shocks in the aggregate economy. Section 4 provides detail on our counterfactual
impulse response methodology. Section 5 presents the results. The final section concludes,
and expounds on possible areas for future research.
2 Why Might Confidence Matter?
Within the conventions of dynamic general equilibrium rational expectations macroeco-
nomics, it is diffi cult to think of realistic theoretical structures which are capable of per-
mitting an important, independent role for “confidence”. The parameters of standard
2
neoclassical models capable of generating multiple equilibria (e.g. degree of returns to scale)
have been judged as empirically unrealistic (Basu and Fernald, 1997). Even under depar-
tures from full information rational expectations, general equilibrium forces and Bayesian
learning render pure confidence or sentiment fluctuations quantitatively irrelevant in many
contexts (Barsky and Sims, 2010). One important purpose of this paper is to attempt to use
minimal theory to inform the extent to which confidence matters, which in turn can guide
the development of more realistic macroeconomic models.
An old idea (Keynes, 1936) that has gained recent attention (Ackerlof and Shiller, 2008) is
that “animal spirits”are central to understanding economic fluctuations. While intriguing,
this idea lacks a coherent theoretical structure, and has met with limited empirical success
(Barsky and Sims, 2010). Loosely speaking, the idea is that aggregate sentiment determines
aggregate spending, which in turn determines aggregate output and employment. Fiscal
or monetary shocks from the government might signal a commitment to aggregate stability,
thereby raising sentiment, stimulating demand, and leading to economic expansion. This
idea is related to the “sunspot”framework popularized by Farmer (1998) and others, which
holds that there are, at any time, multiple aggregate equilibria. Stimulating sentiment could
cause the economy to jump from a “bad”equilibrium to a “good”one.
Another related possibility includes a role for informational frictions and strategic comple-
mentarities. In a world in which households fail to perfectly observe aggregate fundamentals
they may use observed variables (like aggregate output) to form perceptions of the true fun-
damentals (see Lorenzoni, 2009). Following a recession there might be induced sluggishness
—the true fundamentals might have improved but beliefs about the fundamentals are slow
to catch up, hence putting a brake on the recovery. By engaging in expansionary fiscal or
monetary policies, the government may be able to convince agents that fundamentals have
improved, thereby facilitating recovery.
Another possibility is that empirically measured confidence is a measure of a time-varying
discount factor —periods of high confidence are periods in which households do not discount
the future by much, and thus are relatively more willing to spend. If policies can lead to an
increase in confidence, they might therefore stimulate demand over and above what would
happen under normal transmission channels.
3 Related Literature
The purpose of this section is twofold. First, we briefly review some of the standard strategies
to identify both monetary and fiscal policy shocks. Second, we briefly review some of the
literature on confidence and its relation to our work.
3
As most of the identification schemes are (or can be) cast in a vector autoregression
framework, we briefly review some terminology before proceeding. A reduced form VAR
can be written as follows:
A(L)xt = ut
E(utu′t) = Σu 6= I
The vector of endogenous variables, xt, is dimension q×1. There are a variety of methods fordealing with trending variables in VARs, among them linear and quadratic detrending, HP
filtering, first differencing, estimating in levels, and estimating vector error correction models
in which cointegrating relationships are explicitly modeled. We proceed by estimating all
VARs in levels, which is the conservative approach advocated by Hamilton (1994). The
matrix A(L) = I−A1L − A2L2 − ... is a lag polynomial of order p. Under standard
assumptions, its elements can be consistently estimated via OLS. The vector ut is a vector
of innovations or time t forecast errors which may be correlated across equations.
It is assumed that there exists a linear mapping between structural shocks, of which there
are the same number as there are variables in the system, and reduced form innovations.
Structural shocks are defined as mutually uncorrelated innovations. The mapping between
structural and reduced form is:
ut = Bet
Normalizing the diagonal variance-covariance matrix of structural shocks to the identity
matrix, B is the solution toBB′ = Σu. This orthogonalizing matrix is not unique, andq(q−1)2
restrictions must be made in order to completely identify it. It is not necessary to impose
this many restrictions in order to identify only a subset of the shocks, however, which is an
approach frequently made in these literatures. A popular identifying assumption in these
literatures is that there is a recursive structure in which innovations to variables ordered late
in the system impact variables ordered early in the system only with a delay.1
3.1 Monetary Policy
We begin by reviewing popular methods for the identification of monetary policy shocks.
1This is not to say that recursiveness is the only assumption used to identify policy shocks. See Faust(1998) or Uhlig (2005) for alternative approaches to monetary policy identification and Mountford and Uhlig(2008) for fiscal policy.
4
3.1.1 Romer and Romer (1989)
Romer and Romer (1989) peruse the transcripts of FOMC meetings to come up with several
dates at which the US Federal Reserve exogenously pursued a contractionary policy aimed
at reducing inflation at the expense of real output. Focusing on the post-1960 data, these
dates are the fourth quarter of 1968, the second quarter of 1974, the third quarter of 1978,
and the fourth quarter of 1979.2
Let Dt be a dummy indicator taking a value of 1 in these quarters and zeros otherwise.
The vector of variables to be included in the VAR is xt = [yt Dt FFRt]′, where yt is
the log of real GDP and FFRt is the Federal Funds rate expressed at an annualized rate.3
The innovations are orthogonalized in the same order as the variables appear in xt, so that
the Romer dates only affect real GDP with a lag. They find that contractionary monetary
policy shocks are associated with a significant and persistent reduction in economic activity.
3.1.2 Christiano, Eichenbaum, and Evans (1999)
Christiano, Eichenbaum, and Evans (1999, hereafter CEE) estimate a benchmark VAR with
the log of real GDP, the log of the GDP deflator, a measure of commodity prices, the federal
funds rate, non-borrowed reserves, total reserves, and a measure of the money supply (either
M1 or M2). We estimate a variant of this dropping both non-borrowed and total reserves
and using M2 as our measure of the money supply.4 As with Romer and Romer (1989),
the identifying assumption is that monetary policy shocks (measured as innovations in the
federal funds rate), do not affect GDP or prices immediately, though they do affect the
money supply immediately. This amounts to a Choleski decomposition in which the funds
rate is ordered fourth. The remaining structural shocks are left unidentified.
3.1.3 Christiano, Eichenbaum, and Evans (2005)
CEE (2005) estimate a similar system to their 1999 paper. In particular, they include log
real GDP, log real consumption, log real investment, the GDP deflator, the real wage, labor
productivity, the federal funds rate, the growth rate of M2, and corporate profits in their
benchmark system. In addition, we include the same measure of commodity prices as above
2The original paper covers the entire post-war sample, including two additional dates in the 1950s. Westart the sample in the first quarter of 1960 because that is when the confidence data first become available.
3We aggregate the monthly FFR data to quarterly by taking the within quarter average. Alternativefrequency conversions (such as using the last monthly observation of the quarter) produce nearly identicalresults.
4Our measure of commodity prices is the BLS producer price index for intermediate inputs. We dropthe two reserve variables because, in a sample extended from their benchmark (which ends in 1996), thisresults in a high degree of collinearity with M2. The results are qualitatively invariant, however.
5
to help account for the price puzzle. The identifying restriction is the same as in the earlier
paper —most economic aggregates are restricted to not respond on impact to funds rate
innovations. In particular, only money growth and corporate profits are allowed to respond
to funds rate innovations within a quarter. This amounts to a Choleski decomposition in
which the funds rate is ordered eighth.
3.1.4 Romer and Romer (2004)
Romer and Romer (2004) extend the narrative analysis of their earlier paper. They combine
both quantitative and narrative records to form a measure of the exogenous changes in the
FOMC’s target funds rate. This differs from their earlier work in that the measure of
monetary policy here is a quantitative measure and not simply a qualitative dummy indicator.
The estimated system is otherwise similar to above —real GDP, the quantitative measure of
monetary policy shocks, and the fed funds rate. The innovations are orthogonalized such
that the policy shock has no immediate effect on output.5
3.2 Fiscal Policy
In this subsection we review the identification of fiscal shocks using VAR methods. Blan-
chard and Perotti (2002) identify both spending and tax shocks, while Romer and Romer
(2007) identify only tax shocks.6
3.2.1 Blanchard and Perotti (2002)
Blanchard and Perotti (2002) estimate a system featuring real government spending, real
tax collections, and real GDP. They assume that the reduced form innovations (call them
ug,t, uT,t, and uy,t, respectively) can be written as:
uT,t = a1uy,t + a2eg,t + et,t
ug,t = b1uy,t + b2eT,t + eg,t
uy,t = c1uT,t + c2ug,t + ey,t
5The paper uses monthly data with industrial production as the measure of activity. We aggregate theseries to a quarterly frequency by averaging within quarter.
6We also studied the “war date”-identification from Ramey (2009), which in turn is based on Ramey andShapiro (1998). The problem is that the Ramey/Shapiro dates are heavily influenced by the Korean wardate in the 1950s. We do not have confidence data going back this far. Running Ramey’s (2009) exactspecification on post-1960 data reveals that the war dates fail to predict significant changes in spending, sothat the interpretation of a multiplier is precarious at best here.
6
eg,t, eT,t, and ey,t, respectively, are the structural shocks. The coeffi cients a1 and b1 are
meant to capture the “automatic stabilizer”features of tax and spending. Estimates of a1and b1 are obtained outside of the VAR using institutional information. In the benchmark
identification, they impose that a1 = 2.08 and b1 = 0. Given these two restrictions, only one
more restriction is required to fully identify the system. The authors consider restricting
either a2 = 0 or b2 = 0. We consider the case with b2 = 0 as the benchmark, so that
spending does not respond to tax shocks within the quarter.7
3.2.2 Romer and Romer (2009)
Romer and Romer (2009) use the narrative approach to construct a measure of exogenous tax
changes, similarly to their (2007) monetary policy paper. In their benchmark identification,
they simply estimate a bivariate VAR with the tax measure and real GDP and construct the
impulse response of GDP.
3.3 Confidence
Another literature studies the effects of changes in measures of confidence for the evolution
of macroeconomic aggregates. Papers in this literature include Carroll, Fuhrer, and Wilcox
(1994), Matsusaka and Sbordonne (1994), and Barsky and Sims (2010). Carroll, Fuhrer,
and Wilcox (1994) show that changes in confidence have predictive content for future con-
sumption growth. They hypothesize that confidence proxies for current income, and test
whether a simple model with “rule of thumb”(Campbell and Mankiw, 1989) consumers can
account for the correlation. They find that changes in confidence have predictive content
for consumption growth above and beyond that which is contained in income.
Matsusaka and Sbordonne (1994) show that changes in confidence Granger cause changes
in economic aggregates, and argue in favor of models with multiple equilibria. Barsky and
Sims (2010) show that confidence innovations predict prolonged and (loosely speaking) per-
manent changes in both income and consumption. They argue that this result suggests
that confidence likely proxies for information agents have about future income that is other-
wise unavailable to econometricians, though they argue that there is likely not an important
direct causal channel between confidence and activity.
We draw on two sources for data on subjective measures of confidence —one for house-
holds and one for businesses. The first is the Survey of Consumers. Conducted by the
Survey Research Center at the University of Michigan, the survey polls a nationally repre-
sentative sample of households on a variety of questions concerning personal and aggregate
7The alternative assumption, a2 = 0, yields similar results.
7
economic conditions. Most answers are tabulated into qualitative categories —“good”, “neu-
tral”, and “bad”. Scores for each question are then tabulated as the percentage of good
responses minus the percentage of bad responses. These are then aggregated into an index,
which is known as the Index of Consumer Sentiment. Quarterly data from the survey are
publicly available beginning in the first quarter of 1960. The index is weighted to have value
of 100 as the first observation.
We obtain survey data on business confidence from the Conference Board’s CEO Confi-
dence Survey. The Conference Boards surveys CEOs in a variety of industries on current
and future economic conditions. As with the Michigan Survey, answers are tabulated into
qualitative categories —“very good”, “good”,“neutral”,“bad”, and “very bad”. These cate-
gories get a score of 100, 75, 50, 25, and 0, respectively. The aggregate confidence score is
simply the average across the respondents. Data from the survey are available at a quarterly
frequency beginning in 1976.
Figure 1 plots both series against time. The shaded gray areas are recessions as dated by
the National Bureau of Economic Research. Both series undergo repeated dramatic swings
and exhibit some of the properties that one might expect. For example, confidence is low
during recessions and high during booms. The CEO confidence data appear to lead the
business cycle more than do the confidence data. This unconditional feature of the data
turns out to also manifest itself in the conditional impulse responses.
Figure 2 plots impulse responses of real GDP from bivariate VARs with either measure of
confidence and real output, with both series in levels and the confidence innovation ordered
first. The dashed lines are 90 percent confidence intervals from Kilian’s bias-corrected boot-
strap after bootstrap. These figures replicate the essential insight of Barsky and Sims (2010):
confidence innovations, however measured, are associated with movements in output that are
much larger at long horizons than at short ones. The fact that confidence innovations have
a statistically significant effect on economy activity does not imply that confidence shocks
are an independent source of fluctuations; rather it is entirely consistent with confidence
innovations simply revealing information about other structural shocks. Barsky and Sims
(2010) conclude exactly this: changes in confidence are likely not an important independent
source of fluctuations. Their analysis is, however, silent on how confidence factors into the
transmission of other shocks into the economy.
4 Constructing Counterfactuals
We propose measuring the importance of confidence in the transmission of policy shocks by
constructing counterfactual impulse responses of macroeconomic variables to policy shocks
8
in which the endogenous response of confidence to the policy shock is “shut down”. This
necessitates first adding a measure of confidence to the VARs. For all systems, we include a
measure of confidence and order it last in the VARs. Ordering last means that confidence is
allowed to respond immediately to all of the other orthogonal shocks in the system (in par-
ticular policy shocks), while orthogonal innovations to confidence only affect macroeconomic
variables with a lag.
Using the VAR notation from above, the reduced form innovation in confidence (call it
uq+1,t) can be written as a linear combination of the other q orthogonalized shocks in the
system plus its own orthogonal innovation:
uq+1,t =
q∑j=1
bq+1,jej,t + eq+1,t
The bq+1,j are the coeffi cients in the q + 1th row of the orthogonalizing matrix B. If the
policy shock of interest is set to 1, for example, then the impact response of confidence to
policy would be given by bq+1,1.
The impulse response function is simply the structural moving average representation
of the time series. On impact, the impulse response is given by the impact matrix, B.
At subsequent horizons, h > 1, the response depends on both the impact matrix and the
reduced form moving average coeffi cients. Let IRFq+1,t(h) denote the impulse response of
confidence at horizon h to a unit jth orthogonal shock in the system. Write this as:
IRFq+1,j(h) = Cq+1,j(h)bq+1,j for j = 1, ..., q + 1
The elements Cq+1,j(h) are from the matrix polynomial of reduced form moving average
coeffi cients (i.e. C(L) = A(L)−1), with Cq+1,j(1) = 1 and bq+1,q+1 = 1.
The idea of constructing counterfactual impulse responses in a VAR context was first
proposed by Sims and Zha (2006) to determine how important the systematic component
of monetary policy (i.e. the endogenous response of the funds rate to other shocks) is for
the evolution of real variables. It was later also adopted in a similar context by Bernanke,
Gertler, and Watson (1998) to study the role of monetary policy in the transmission of oil
price shocks.
The counterfactual thought experiment can be applied as well to our problem and is both
conceptually and quantitatively straightforward. It involves constructing a time series of
counterfactual orthogonalized confidence shocks, eq+1,t, t = 1, ..., h, so as to set the impulse
response of confidence to a policy shock zero at all horizons. Using the above definition of
the impulse response function of confidence, and assuming that the policy shock is indexed
9
first and takes a value of unity, this amounts to the following recursive calculation of the
sequence of counterfactual orthogonal confidence innovations:
IRFq+1,j(h) = 0 ∀ h
⇔eq+1,1 = −bq+1,1eq+1,2 = − (Cq+1,1(2)bq+1,1 + Cq+1,q+1(2)eq+1,1)
eq+1,3 = − (Cq+1,1(3)bq+1,1 + Cq+1,q+1(3)eq+1,1 + Cq+1,q+1(2)eq+1,2)...
eq+1,h = −(Cq+1,q+1(h)bq+1,1 +
h−1∑j=1
Cq+1,q+1(h− 1− j)eq+1,j
)
Because orthogonal shocks to confidence affect the other variables of the system with a
lag, the counterfactual impulse responses will not only hold confidence fixed but will alter
the impulse responses of the other variables of the system. How much the responses of the
other variables (particularly real output) change when holding confidence fixed is precisely
the object of interest in our analysis.
We now briefly present some model-based evidence that our counterfactual methodology
is appropriate and likely to work well in practice. Formally, we operate under the null
hypothesis that confidence does not “matter”, though it may reflect available information
concerning underlying fundamentals. We study a simple DSGE model with government
spending shocks; similar results obtain in a framework where we study monetary policy
shocks.
Consider a simple version of a real business cycle (RBC) model in which government
spending evolves according to an exogenous, stochastic process and is financed via lump
sum taxation. The equilibrium of the economy can be characterized by the solution to a
planner’s problem:
maxct,kt+1,nt
E0
( ∞∑t=0
βt (ln ct + θ ln(1− nt)))
s.t.
10
ct + gt + kt+1 − (1− δ)kt = atkαt n
1−αt
ln at = ρa ln at−1 + ea,t
ln gt = (1− ρg) ln g∗ + ρg ln gt−1 + eg,t
We assume that the unconditional mean of log TFP is zero, or unity in the level. The
unconditional mean of government spending is g∗. We incorporate “confidence” into the
model in the following way: we assume that it follows a univariate AR(1) process with an
innovation equal to a linear combination of the two structural disturbances and its own
“noise”term:
conft = ρcconft−1 + φ1ea,t + φ2eg,t + ec,t
This specification is similar to the structural model in Barsky and Sims (2010) and is consis-
tent with the null hypothesis presented above: autonomous confidence “shocks”(ec,t) have
no effect on the real economy, but fundamental shocks can impact confidence if φ1 6= 0 orφ2 6= 0.We choose the following standard parameter values: α = 0.33, δ = 0.03, β = 0.98,
ρa = ρg = 0.9, and θ = 3. We then choose g∗ so that government spending is equal to 15
percent of output in steady state; with the parameterization of θ then about 25 percent of the
time endowment is spent working in steady state. We set φ1 = 1, φ2 = −0.5, and ρc = 0.9.We solve the model by linearizing the first order conditions about the non-stochastic steady
state. We set the standard deviations of the shocks to TFP, government spending, and
confidence at 0.007, 0.006, and 0.01, respectively.
We simulate data from the model, drawing shocks from normal distributions. Then for
each simulated data set we estimate a three variable VAR with government spending, output,
and confidence. We estimate the VAR in the levels of the variables with four lags. We
orthogonalize the innovations in that order using a Choleski decomposition. This ordering
is consistent with the implications of the structural model —government spending shocks
affect both output and confidence immediately; TFP shocks affect output and confidence
immediately but not government spending; and confidence shocks affect neither government
spending nor output on impact.
We simulate 500 data sets from the model with 200 observations each. Figure 3 shows
some results. The solid line depicts the theoretical impulse response to a government
spending shock in the model. The dashed line shows the average estimated response to
the identified government spending shock in the VAR across simulations. The shaded gray
11
areas are the 5th and 95th percentiles of the distribution of estimated responses. It is clear
that the VAR does a good job of identifying the model’s impulse responses. The responses
are roughly unbiased and capture well the model’s dynamic implications of a government
spending shock.
In the model as specified confidence declines in response to a government spending shock;
nevertheless, if confidence did not decline the impulse responses of output and government
spending to the government spending shock would be identical. Put differently, applying
our counterfactual methodology to the model-generated data should lead to no change in the
estimated impulse responses of output and government spending to the government spending
shock. Figure 4 shows results from this exercise. The solid line is the theoretical impulse
response; the dashed line is the average estimated response from the unrestricted identified
VAR; the dotted line shows the average responses from the counterfactual VARs; and the
shaded gray areas are the 5th and 95th percentiles of the distribution of unrestricted VAR
impulse responses. Here we see that there is essentially no difference in the unrestricted and
counterfactual impulse responses, which is consistent with the theory. By construction, the
confidence response is “shut down”.
This quantitative example is meant to be illustrative. It does, however, serve a couple
of purposes. First, it illustrates our methodology in a simple and transparent framework,
showing that it is likely to perform well. It also makes the following points clear: for
the counterfactual responses of aggregate variables to differ, (i) confidence must respond to
policy shocks and (ii) confidence innovations ordered last must have non-zero impacts on the
other variables over some horizon.
5 Results
We proceed similarly in this section as in Section 2. We augment the basic VAR systems
with a qualitative measure of confidence. We compute the impulse responses to the identified
shocks and then construct the counterfactual responses in which the confidence response is
“shut down”. We begin with monetary policy and then study fiscal policy.
5.1 Monetary Policy
5.1.1 Romer and Romer (1989)
Figure 5.1 shows impulse responses of GDP, the federal funds rate, and consumer confidence
from a three variable VAR as described above in Section 3.1.1. The sold lines are the unre-
stricted responses to a policy shock, with the shaded gray area representing the 90 percent
12
confidence bands, constructed via Kilian’s (1998) bias-corrected bootstrap after bootstrap.
The dashed lines show counterfactual responses holding confidence fixed. The time units
on the horizontal axis are quarters. The data used in the estimation span the 1960-2007
period.
In the unrestricted case, we see that the Romer “dates”are associated with a large and
persistent increase in the federal funds rate and a large decline in real GDP. The implied
“elasticity”of GDP to the policy shock (defined as the maximum percentage decline in GDP
over all horizons divided by the maximum percentage point increase in the funds rate) is
slightly greater than 1.5. Consumer confidence responds significantly and negatively to the
Romer “dates”, albeit with a lag, with peak negative response of confidence occurring a year
and a half after the shock.
The dashed line show the counterfactual responses. The counterfactual response of
the funds rate itself holding confidence fixed is virtually unchanged. The counterfactual
response of output is qualitatively similar to the unrestricted case but smaller. One is not
able to statistically reject the hypothesis that the responses are the same (i.e. the dashed
line lies within the shaded confidence region), but there is nevertheless some evidence that
confidence matters quantitatively. The “elasticity” (defined similar as above) of GDP to
the monetary policy shock is now -0.87 as opposed to -1.5, suggesting that as much as 40
percent of the response of output is due to consumer confidence.
Figure 5.2 repeats this exercise but uses CEO confidence in place of consumer confidence.
Qualitatively the results are very much the same —the funds rate response is almost identical
and the output response is similar but smaller. One important difference is that CEO
confidence reacts much more quickly to a policy shock than does consumer confidence. Here
the maximum decline in CEO confidence is on impact as opposed to at six quarters in the
case of consumer confidence.
5.1.2 CEE (1999)
In Figure 6.1 we show the responses to a monetary policy shock in CEE’s system augmented
with a measure of consumer confidence. The structure of the figures is the same as above.
In the unrestricted case, output declines significantly in response to the policy shock, albeit
with a substantial delay. Prices decline (but only with a very long delay) and the money
supply declines immediately. As one might expect, confidence declines, though once again
with a delay of several quarters.
The responses of the other variables in the system to the policy shock are very similar
in the counterfactual simulations. The hypothesis of equality between the restricted and
counterfactual responses for output can once again not be rejected at conventional levels of
13
significance. The unrestricted output elasticity with respect to the funds rate is here -0.76;
the elasticity under the counterfactual is -0.64. This suggests that confidence accounts for
only a little more than 10 percent of the response of output to a monetary policy shock.
Figure 6.2 conducts the same analysis using the CEO confidence survey. As was the
case with the Romer dates, CEO confidence falls most dramatically in the same quarter as
the shock, as opposed to several quarters later in the case of consumer confidence. While
still statistically insignificant, the counterfactual response of output to the policy shock is
roughly 50 percent of its value in the unrestricted case.
5.1.3 CEE (2005)
Figures 7.1 and 7.2 shows the analogous set of unrestricted and counterfactual responses for
the CEE (2005) system augmented with consumer and business confidence, respectively. In
the unrestricted case, the response of output looks very similar to the CEE (1999) responses.
In terms of the counterfactual responses, one observes that the responses of the variables
of the model to a monetary policy shock are roughly unchanged when forcing confidence to
not respond, though confidence itself does negatively and significantly respond to the shock
in the unrestricted system. The unrestricted elasticity of output with respect to the policy
shock is -0.9 here and the counterfactual elasticity is -0.68 when consumer confidence is held
fixed, or about 75% of its unrestricted value. In the case of CEO confidence, the results are
very much the same. CEO confidence declines sooner and more significantly does consumer
confidence. The counterfactual elasticity of output with respect the policy shock is also
roughly 75% of its unconstrained value.
5.1.4 Romer and Romer (2004)
In Figures 8.1 and 8.2 we show responses for the Romer and Romer (2004) identification. We
observe again that consumer confidence responds significantly and in the expected direction
to the contractionary monetary policy shock. Output responds less in the counterfactual
response than in the unrestricted response to the monetary policy shock, as we would expect,
though yet again the two responses are statistically indistinguishable. The unrestricted
elasticity of output with respect to money here is -0.57, while the restricted elasticity is -0.4.
The responses with CEO confidence tell much the same story.
5.1.5 Comments
For all four identifications of monetary policy shocks, we find that both consumer and busi-
ness confidence respond negatively and significantly to contractionary policy shocks. In
14
counterfactual simulations in which the response of confidence is held constant at its pre-
shock value, we find that real GDP responds less to the policy shock than in the unrestricted
case. The standard errors are, however, large, and it is not possible to statistically dis-
tinguish between the restricted and the unrestricted responses. The table below lists the
unrestricted and restricted elasticities of output to the federal funds rate, with this elasticity
defined as the maximum negative output response divided by the maximum positive funds
rate response, for both consumer and CEO confidence:
Table 1:Unrestricted and Counterfactual Monetary Policy Multipliers
Unrestricted Restricted Fraction Due to Confidence
Consumer Confidence:
Romer and Romer (1989) -1.5 -0.87 0.42
CEE (1999) -0.76 -0.64 0.16
CEE (2005) -0.90 -0.68 0.25
Romer and Romer (2004) -0.57 -0.40 0.30
CEO Confidence:
Romer and Romer (1989) -1.25 -0.80 0.35
CEE (1999) -0.49 -0.25 0.50
CEE (2005) -0.63 -0.48 0.23
Romer and Romer (2004) -0.58 -0.46 0.20
We can see that, in all four specifications, holding confidence fixed reduces the response
of output to policy shocks. The effects are higher for the two Romer and Romer narrative
identifications than for the recursive assumptions in the two CEE papers. We can con-
clude the confidence is likely a part of the transmission of monetary policy into real GDP,
though the effect is statistically diffi cult to distinguish from zero. There is little significant
quantitative difference between the results with consumer and CEO confidence.
5.2 Fiscal Policy
5.2.1 Blanchard and Perotti (2002)
Figure 9.1 shows the actual and counterfactual responses of the variables of interest to
both a government spending (upper panel) and a tax shock (lower panel) in the benchmark
Blanchard and Perotti (2002) identification in a system augmented to include consumer
confidence. In the unrestricted case, we see that an increase in spending leads to a mod-
est increase in output that is not very persistent. It also leads to a decline in consumer
15
confidence, albeit statistically indistinguishable from zero. This is consistent with the neo-
classical view that increases in spending impose future tax obligations (though not shown in
the impulse responses, the response of taxes turns positive after a number of horizons). The
spending multiplier —defined as the maximum percentage point increase in output divided
by the maximum percentage point increase in government spending, is just below unity at
0.9244.
Given the lack of significance of the response of consumer confidence to the spending
shock, it is unsurprising that the counterfactual simulations show that “shutting down”the
response of confidence does little or nothing to alter the other impulse responses. Indeed,
there is hardly any difference in the implied spending multipliers, with the multiplier 0.9244
in the unconstrained case and 0.9238 in the counterfactual case.
Consistent with other findings throughout this literature, tax shocks appear to have much
larger and more persistent effects on output than do spending shocks. Indeed, there is a
large and persistent decline in output following a positive tax shock. Here we observe that
confidence does respond significantly and negatively to a positive tax shock, albeit with a
significant lag. Confidence does appear to matter more in the case of tax shocks. We observe
that counterfactual response of output to the tax change is significantly different in the
counterfactual. Indeed, the unconstrained tax multiplier is -2.17, while the counterfactual
tax multiplier is only -1.18, suggesting that confidence accounts for roughly 40 percent of
the output response to a tax shock in the data.
The results with CEO confidence are qualitatively fairly similar, albeit CEO confidence
appears quantitatively critical in the transmission of tax shocks. As with consumers, the
unrestricted and counterfactual impulse responses to spending shocks are virtually identical,
with spending multipliers in the neighborhood of unity. CEO confidence declines sharply
and significantly to a tax shock. Further, the counterfactual impulse response of output is
very different than the unrestricted case —the response of output is essentially zero at all
horizons as opposed to sharply negative in the unrestricted case. In this particular instance,
it appears as though CEO confidence is a highly important part of the transmission of tax
shocks.
5.2.2 Romer and Romer (2009)
Figure 10.1 shows the actual and counterfactual responses to the Romer and Romer (2009)
tax shocks using consumer confidence. One observes that confidence responds negatively to
the tax shock as one might expect, though the response is not statistically significant. Not
unsurprisingly, therefore, the difference between the counterfactual and actual responses
of output to the tax shock are not statistically different. The implied tax multiplier in
16
the unconstrained case is -1.88, which is not too different from the Blanchard and Perotti
(2002) results. The counterfactual tax multiplier is estimated to be -1.33, suggesting that
confidence accounts for roughly 30 percent of the response of GDP to a tax shock.
The results are similar in Figure 10.2, which shows unrestricted and counterfactual re-
sponses using the CEO confidence index. As in other figures, CEO confidence responds
sooner to the tax shock than does consumer confidence. The counterfactual response of
output is statistically indistinguishable from the unrestricted case, though confidence ac-
counts for as much as 50% of the quantitative magnitude of the response (particularly at the
long horizons).
5.2.3 Comments
We offer a brief concluding commentary on our counterfactual simulations for fiscal policy.
We find that confidence responds in the expected direction to fiscal shocks, typically much
more significantly to tax shocks than to spending shocks, which appears at first blush consis-
tent with neoclassical theories. We also find that confidence is a more important part of the
transmission mechanism of tax shocks than spending shocks. The table below summarizes
the implied actual and counterfactual multipliers.
Table 2:Unrestricted and Counterfactual Fiscal Policy Multipliers
Unrestricted Restricted Fraction Due to Confidence
Consumer Confidence:
BP (2002) Spending 0.92 0.92 0.00
BP (2002) Tax -2.17 -1.18 0.45
Romer and Romer (2007) Tax -1.88 -1.33 0.29
CEO Confidence:
BP (2002) Spending 1.10 1.09 0.00
BP (2002) Tax -1.51 0.00 1.00
Romer and Romer (2007) Tax -2.03 -1.05 0.50
These results suggest that confidence is potentially important in the transmission of tax
shocks, accounting for between one quarter and all of the output response. Confidence is
evidently completely unimportant in the transmission of spending shocks. We omit multi-
pliers based on Ramey (2009) due to the puzzling features of those responses. We observe
that CEO confidence is quantitatively about twice as important as consumer confidence in
transmission.
17
Many observers feel that fiscal policy is most potent when the economy exhibits extreme
slack (e.g. Christiano, Eichenbaum, and Rebelo, 2009). We experimented with a number of
non-linear specifications in which the impact effect of fiscal or tax shocks is different when
the economy is in a recession or when the unemployment rate is “high”(or more generally
when some cyclical indicator is “poor”). These specifications do not yield vastly different
impulse responses to policy shocks, and so we omit them for space consideration.
6 Conclusion
In this paper we have taken a serious look at an increasingly popular claim —that “confidence”
is an important part of the transmission mechanism between economic policy and economic
outcomes. We address this question by constructing counterfactual impulse responses to
identified policy shocks in which the response of confidence to policy is forced to equal
zero at all time horizons. Comparing the unrestricted and counterfactual responses of
economic variables allows us to quantify the importance of confidence in the transmission
and propagation of policy shocks.
Our results are somewhat mixed. In most cases, the counterfactual impulse responses
are statistically no different than the unrestricted cases. Quantitatively, however, the contri-
bution of confidence often appears large. In the case of tax shocks, confidence does appear
to be an important transmission mechanism, particular CEO confidence.
18
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20
7 Appendix: Quotes“We must be certain that programs to solve the current financial and economic crisis are
large enough, and targeted broadly enough, to impact public confidence.”—Robert Shiller
“Yale’s Bob Shiller argues that confidence is the key to getting the economy back on track.
I think a lot of economists would agree with that.” —N. Gregory Mankiw
“Enacting such a conditional stimulus would have two desirable effects. First, it would im-
mediately boost the confidence of households and businesses since they would know that a
significant slowdown would be met immediately by a substantial fiscal stimulus.” —Martin
Feldstein
“But the hope that monetary and fiscal policies would prevent continued weakness by boosting
consumer confidence was derailed by the recent report that consumer confidence in January
collapsed to the lowest level since 1992.” —Martin Feldstein
“The economy is stagnant because of a lack of confidence in the future.” —Russell Roberts
“ . . . that at some point, people could lose confidence in the U.S. economy in a way that
could actually lead to a double-dip recession." —President Barack Obama
“The stimulus was too small, and it will fade out next year, while high unemployment is
undermining both consumer and business confidence.” —Paul Krugman
“It’s only an attempt to perhaps provide a bit of additional confidence, a bit of additional
assurance or a bit of additional certainty to the markets about the Federal Reserve’s long-term
objective.” —Ben Bernanke
“Economic activity in the United States turned up in the second half of 2009, supported by
an improvement in financial conditions, stimulus from monetary and fiscal policies, and a
recovery in foreign economies. These factors, along with increased business and household
confidence, appear likely to boost spending and sustain the economic expansion.” — Ben
Bernanke
“Confidence today will be enhanced if we put measures in place that assure that the coming ex-
pansion will be more sustainable and fair in the distribution of benefits than its predecessor.”
—Larry Summers
“President Obama’s top priority has been to stop the vicious cycle of economic and financial
collapse, stem the historic rate of job loss, restore confidence and put the economy on a path
21
to recover.”—Larry Summers
“Others say that we should have a fiscal stimulus to ’give people confidence,’even if we have
neither theory nor evidence that it will work.” —John Cochrane
“The subsequent global sell-off in equity markets suggested that governments would need to
take action with more immediate impact to restore confidence in the markets.” — James
Bullard
22
23
Figure 1: Consumer and Business Confidence Data
50
60
70
80
90
100
110
120
60 65 70 75 80 85 90 95 00 05 10
Index of Consumer Sentiment
20
30
40
50
60
70
80
1980 1985 1990 1995 2000 2005 2010
CEO Confidence
Note: Shades areas are recessions as defined by the NBER.
Figure 2 Impulse Responses to Confidence Innovations in Bivariate VAR
0 5 10 15 20 25 30 35 400.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
0.022GDP to Consumer Conf.
0 5 10 15 20 25 30 35 400
0.002
0.004
0.006
0.008
0.01
0.012
0.014GDP to CEO Conf.
Note: These are IRFs from two variable VARs with confidence and real GDP (with the confidence variable ordered first).
24
Figure 3 Impulse Response from Theoretical Model and Simulations
Note: the solid line shows the model impulse response to a government spending shock; the dashed line shows the average VAR estimated IRF across 500 simulations; and the shaded gray area shows the 5th and 95th percentiles of the distribution of estimated responses.
Figure 4 Counterfactual Impulse Responses from Model Simulation
Note: the dotted line shows the average estimated counterfactual impulse responses across the 500 simulations; see also the note to Figure 3.
25
Figure 5.1
Counterfactual Responses to Monetary Policy Shock: Romer and Romer (1989): Consumer
Figure 5.2
Counterfactual Responses to Monetary Policy Shock: Romer and Romer (1989): CEO
26
Figure 6.1 Counterfactual Responses to Monetary Policy Shock: CEE (1999): Consumer
Figure 6.2
Counterfactual Responses to Monetary Policy Shock: CEE (1999): CEO
27
Figure 7.1 Counterfactual Response to Monetary Policy Shock: CEE (2005): Consumer
Figure 7.2
Counterfactual Response to Monetary Policy Shock: CEE (2005): CEO
28
Figure 8.1 Counterfactual Response to Monetary Policy Shock: Romer and Romer (2004): Consumer
Figure 8.2
Counterfactual Response to Monetary Policy Shock: Romer and Romer (2004): CEO
29
Figure 9.1 Counterfactual Responses to Fiscal Policy: Blanchard and Perotti (2002): Consumer
Figure 9.2
Counterfactual Responses to Fiscal Policy: Blanchard and Perotti (2002): CEO