ISSN (Print) 0473-453X

Discussion Paper No. 1135

ISSN (Online) 2435-0982

WHAT ISCONSUMER CONFIDENCE?
Jean-Paul L'Huillier
Robert Waldmann
Donghoon Yoo

Revised December 2021
June 2021

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

What Is Consumer Confidence?*
Jean-Paul L’Huillier, Robert Waldmann, Donghoon Yoo
December 2, 2021

Abstract
This paper offers a structural interpretation of survey measures of consumer confidence. To this end, we consider a simple consumption model withnoisy information about fundamentals and estimate a model-based measureof consumer confidence using national accounts. We show that the modelimplied measure corresponds well to fluctuations in confidence survey data
for the U.S. and a host of European countries. Our analysis provides aninformational mechanism to conceptualize and interpret these widely usedconfidence indices.

Keywords: Aggregate spending, confidence indices, noisy information
JEL Classification Codes: E21, E32, E66* This paper has previously circulated under the title “Confidence, Fundamentals, and Con-

sumption”. We benefited from very useful feedback from Gene Ambrocio, Kenza Benhima,
Nicholas Bloom, Jan Magnus, Yuta Takahashi, Daniele Terlizzese, and Takayuki Tsuruga. We
also thank for seminar and conference participants at the Asian Econometric Society Meetingin Seoul, Hanyang University, Humboldt University of Berlin, ISER-Osaka University, Yonsei
University, and Korean Economic Review International Conference for helpful suggestions. Yoo
acknowledges financial support from the Joint Usage/Research Center at ISER, Osaka University
and Grant-in-aid for Research Activity, Japan Society for the Promotion of Science (21H04397,
20K01546, 20H05631).
 Brandeis University, email: jplhuillier2010@gmail.com; University of Rome “Tor Vergata”,
email: robert.waldmann@gmail.com; Institute of Social and Economic Research (ISER), Osaka
University, email: donghoonyoo@gmail.com.

1

1

Introduction

Measuring how confident consumers are about the overall state of the economy,
consumer confidence is widely discussed in the popular press and in economic
commentaries.1 While unambiguously reflecting prevailing economic and businessconditions perceived by survey respondents, interpreting “consumer confidence”
has been the subject of discussion in the academic literature. On the one hand,consumer confidence measures have an important prognostic and causal role inunderstanding the business cycle fluctuations. To the other extreme, there exists
the view that expectations held by consumers are little more than uninformed
guesses. Regardless of the role of consumer confidence in macroeconomics, we often
observe a close relationship between aggregate trends in the measure of confidencewith the corresponding trends in the aggregate quantities. It is then natural to
ask if we can characterize the mechanism by which consumers’ attitudes influence
aggregate fluctuations.
In this paper, we evaluate what consumer confidence represents from the lensof business cycle models. We argue that rationally processing and learning from
economic information can provide a platform for researchers and practitioners to
identify and understand measures of consumer confidence. Specifically, we provide
an informational mechanism to conceptualize and interpret consumer confidence
measurements using national accounts.2
In Section 2, we lay out a foundation to measure model-based consumer confidence. We assume that consumers cannot perfectly forecast the future and proceed to estimate confidence given a structural interpretation of the economy. Our
simple learning model based on Blanchard, L’Huillier, and Lorenzoni (2013) and
L’Huillier and Yoo (2017) captures the idea that waves of optimism and pessimism
are related to the dynamics of spending (relative to productivity). There, agents’
perception of the future changes due to fundamentals and information unrelated
1
These survey-based indices include the Index of Consumer Sentiment (ICS), produced by
the Survey Research Center of the University of Michigan, and the Consumer Confidence Index,
issued by the Conference Board among others. Appendix A provides a more detailed descriptionof these indices.
2
Since its inception in 1953, the System of National Accounts constitutes, in principle, aninternationally consistent and coherent measure of activity. Thus, a structural method basedon national accounts could provide a measure for consumer confidence that is internationallyconsistent.

2

to present (or past) fundamentals.
In our attempt to estimate consumer confidence, we make two strong assumptions. First, agents’ information structure is given by the combination of thepermanent-transitory productivity decomposition and the signal on the permanent
component. Second, a structural model describing agents’ consumption behavior
follows the permanent income logic. Productivity is determined exogenously by
a combination of a permanent and a temporary shock. Consumers receive noisy
signals about the permanent productivity of the economy. According to the permanent income hypothesis, consumers choose spending based on their expected future
income. Thus, estimating the parameters of the model and making inferences is
feasible by looking at productivity and consumption trends. We, as econometricians, are able to estimate consumers’ beliefs about the future and underlying
structural shocks.
Our set of assumptions and approach can reasonably achieve our goals for thefollowing reasons. First, our simple model achieves identification. Second, themodel fits the data well. Third, the information structure captures well the roleof belief-driven fluctuations. Fourth, more complex models need a fixed rate to fit
the data. (We elaborate on these points in Section 2.1.)
While incorporating noisy news in a standard model has recently been discussed,3 our focus is on extracting the evolution of agents’ perception about thestate of the economy. We isolate the contribution of noisy signals to current consumption, effectively capture the role of agents’ additional information beyond thefundamental, which we call our model-based consumer confidence index.
Having estimated consumer confidence, we proceed to compare it with a surveybased confidence index in Section 3. We use U.S. national accounts data to obtain the model-based consumer confidence index and compare it with the Index
of Consumer Sentiment (ICS). A key contribution of our paper is to show thatmodel-based consumer confidence matches the ICS quite well, with a statisticallysignificant (< 1%) correlation of 0.52 between the two. Smoothing out highfrequency noise using a band-pass filter delivers a higher correlation with the ICS(0.79)We also compare our model-based consumer confidence with the Consumer
3

See, for example, Blanchard et al. (2013), Boz, Daude, and Durdu (2011), and Cao and
L’Huillier (2018).

3

Confidence Index (CCI) in fourteen European countries in Section 4. Our results
show that model-based measures are estimated to be highly correlated with thesurvey-based measure for nine out of fourteen countries in our sample (even if we
do not use the survey measures as an input).
At the same time, there exists a great deal of heterogeneity such that for
countries like Austria, Belgium, Germany, Finland, and Sweden, our measure fails
to match the CCI. We show that, to some degree, this observed heterogeneity
is driven by the survey measure not being able to track observed consumption
contemporaneously.

Relation to the literature
The crucial ingredient of our model is an information structure where agents receive
noisy information of permanent productivity of the economy, discussed in Boz,
Daude, and Durdu (2011), Lorenzoni (2009), Blanchard, L’Huillier, and Lorenzoni(2013), Cao and L’Huillier (2018), and Rousakis (2013), among others. While
sharing similar information structures, we attempt to solve a signal extraction
problem sequentially, as in L’Huillier and Yoo (2017) and Yoo (2019), disentangling
the effects of different signals on aggregate fluctuations.
Using consumer confidence to forecast aggregate quantities, Batchelor and Dua(1998) show that paying attention to the sharp fall in consumer confidence would
have helped predict the 1991 recession. However, consumer confidence would havenot been helpful in forecasting recessions in other years. Howrey (2001) showsthat the U.S.’s ICS is a statistically significant predictor for forecasting the nearterm probability of a recession when used independently or in conjunction withother indicators.4 In addition, Lahiri, Monokroussos, and Zhao (2015) consider
a more realistic and general context to analyze the predictive power of consumer
confidence by using monthly and real-time data along with a large number of
explanatory variables and show that measures of consumer confidence provide a
positive contribution in forecasting consumption expenditure. Barsky and Sims(2012) use structural estimation to assess the impact of consumer confidence in amodel that features signal extraction.
4

These indicators include the spread between long and short-term interest rates, the New York
Stock Exchange composite price index, and the Conference Board index of leading indicators.

4

Our modeling approach originates in the contributions by Beaudry and Portier(2004, 2006). They were the first to point out that news shocks offer a useful interpretation of macroeconomic data. Schmitt-Grohé and Uribe (2012) also investigated the impact of news shocks in a structural model. More recently, Chahrour
and Jurado (2018, 2021) have made important advances in the identification ofthis type of shocks.
Other parallel strands of the literature investigate the impact of swings inprivate sector confidence but use very different models. For example, Ilut andSchneider (2014) use ambiguity. Angeletos and La’O (2013) use shocks akin tosunspots that operate in unique equilibrium models. Angeletos, Collard, and Dellas (2018) use a tractable form of higher order belief dynamics. To the best of
our knowledge, none of these strands of the literature has taken a similar focus on
survey data as we do.

5

2

Confidence in a simple consumption model

We extract consumer confidence from a simple consumption model with the learning problem of a consumer who forms beliefs about the future path of productivity.
In this economy, consumption is the only endogenous variable and the behavior
of consumption is described by a random walk:ct = Et [ct+1 |It ]which can be derived from first principles. For details, see Blanchard et al. (2013).
There is no capital, and output is completely determined by the demand side
where consumption is the only determinant of demand:y t = ct
Simplifying the supply side, we assume that the role of labor input is to adjust
to the current productivity level at and to produce output yt :yt = at + nt
Given that the output in the long-run returns to its natural level
lim Et [ct+j − at+j ] = 0

j→∞current spending ct is defined by
ct = lim Et [at+j ]
j→∞

(1)

such that Equation (1) suggests consumption depends on the consumers’ long-run
productivity expectation.
We consider a single, representative information set by Blanchard et al. (2013)
where fundamentals are stochastic processes describing exogenous changes in productivity or income summarized by at . Productivity is characterized by the sum

6

of two components - a permanent component xt and a transitory component zt :at = xt + zt
where two components are respectively defined by∆xt = ρx ∆xt−1 + ϵtzt = ρz zt−1 + ηt
The permanent component xt follows a randomly changing trend due to apermanent shock ϵt , and the transitory component follows the stationary processwith a transitory shock ηt . Two productivity shocks ϵt and ηt are assumed to bei.i.d. Gaussian with variances σϵ2 and ση2 . The coefficients ρx and ρz are in [0, 1).
We assume that the univariate process for at is a random walk:E[at+1 |at , at−1 , ...] = at(2)

Blanchard et al. (2013) show that this random walk representation is analyticallyconvenient and is also broadly in line with actual productivity data. This impliesthatρx = ρz = ρ
Also, the variances need to satisfy the restrictions:σu2 =(1 − ρ)2
ρ2ϵ

(3)

ρ
ση2

(4)

and
σu2 =

where σu is the standard deviation of ∆at . Appendix F relaxes these parametricrestrictions.
A key assumption regarding the productivity processes is while agents observe
productivity at as a whole, they do not observe the components xt and zt separately. This informational assumption is important since agents choose their7

current spending using their expectations about future productivity.5 Since thetransitory productivity process zt+∞ dies out in the long-run, just observing thewhole productivity process at is not sufficient to predict the future state of theeconomy. Thus, agents would need to update their expectations about the future
productivity. We assume they do so using the Kalman filter.
Considering the idea that agents have more information than merely about
productivity, agents observe a noisy signal st about permanent productivity:s t = x t + νt

(5)where νt is an i.i.d Gaussian shock with mean zero and variance σν2 , and the shockνt is a noise shock because it affects agents’ beliefs but is independent of fundamentals. This noisy signal denotes the additional informative signal that agentsreceive which is a straightforward interpretation of Equation (5). Ultimately, thepresence of this noisy information helps the econometrician make inferences about
the (unobserved) long-term productivity trend by looking at the behavior of consumption.

2.1

How valid is this approach?

Our model assumptions are reasonable and useful for the application at hand forthe following reasons.
First, our simple model is identified, as a number of recent works have demonstrated (see Blanchard et al. 2013, L’Huillier and Yoo 2019, Cao and L’Huillier
2018, among others).
Second, this model fits the data well. Chahrour and Jurado (2018) show that
their version of Blanchard et al. (2013) fits better than alternative specifications
according to the Bayesian information criterion (BIC).6
Third, the information structure captures the role of belief-driven fluctuations.
Combined with the forward-looking consumption decision, it generates empiricallyrealistic co-movement patterns in response to a noise shock, as empirically evident
by Chahrour and Jurado (2018).
5
6

For the rest of the paper we use the terms agents and consumers interchangeably.
See Table 4 and the subsequent discussion on pp. 1731-1732 of Chahrour and Jurado (2018).

8

Fourth, more complex models need a fixed rate to fit the data, as shown in
Blanchard et al. (2013) and Cao and L’Huillier (2018). The general equilibrium
effects of real interest rate changes on consumption are almost entirely neutralized
in the class of medium-scale DSGE models that are most often used in quantitative
macroeconomic work. See L’Huillier and Yoo (2019) for a detailed investigationof this. Thus, our main economic mechanism (the permanent income assumption) provides a good approximation to this richer class of models. Moreover, theestimation based only on the assumption of permanent income behavior using consumption and productivity data is less likely to be subject to misspecification thanis the estimation of bigger models using more data.
Overall, our economic mechanism and information structure provide a good
approximation of consumption behavior and effectively capture the role of beliefdriven fluctuations, both of which are essential for our application to estimate
consumer confidence.

2.2

Solving the model

Solving the model for consumption is a direct implementation of the Kalman filter
to solve a signal extraction problem for the expectation about future productivity
at+∞ . First, solving Equation (1), we get
ct =


1
xt|t − ρxt−1|t
1−ρ

(6)

where xt|t ≡ E[xt |It ] ≡ Et [xt ] and xt−1|t ≡ E[xt−1 |It ] ≡ Et [xt−1 ] represent agents’beliefs about current and lagged permanent productivity, respectively. Here, theagents’ information set at time t, It , includes current productivity, at , a noisy
signal, st , and lagged information, It :It = (at , st , It−1 )where I0 = (a0 , s0 ).
Second, agents’ beliefs about the permanent state of the economy (xt|t and
xt−1|t ) can be obtained by solving a signal extraction problem where an unobservable state vector xt is given by xt = (xt , xt−1 , zt )′ , and an observable vector is9

given by st = (at , st )′ :
xt|t = I − κ × C Axt−1|t−1 + κ × st(7)

where xt|t = (xt|t , xt−1|t , zt|t )′ and xt−1|t−1 = (xt−1|t−1 , xt−2|t−1 , zt−1|t−1 )′ areagents’ beliefs about xt at time t and xt−1 at time t − 1 respectively, κ is a vectorof steady-state Kalman gains, A and C are the functions of underlying parametersof the model, and I is the 3 × 3 identity matrix.
Thus, substituting xt|t and xt−1|t obtained in Equation (7) onto Equation (6),
we can easily solve the model for consumption.7

2.3

A mechanism to extract consumer confidence

To measure consumer confidence in this framework, we exploit “additional information beyond fundamentals” conveyed to consumers. This additional information
is useful to understand what consumer confidence is.
To begin with, we exploit the fact that the signal extraction problem discussed
in the last section can also be solved sequentially as in L’Huillier and Yoo (2017)
and Yoo (2019). By doing so, we disentangle the effects of two signals, productivity
at and a noisy signal st , on consumption fluctuations.
The following procedure details how we obtain our measure of consumer confidence. Denote agents’ expectations about a state vector xt with current productivity and lagged information by
xt|at = E[xt |Ωt ]where Ωt = (at , It−1 ) such that Ωt ∈ It and Ωt ∪ st ≡ It .
Conditional on agents’ beliefs at time t − 1, xt−1|t−1 , where agents’ information
set includes only productivity at (other than those available at time t − 1), thebelief updating is given by
xt|at = Axt−1|t−1 + H(at − at|t−1 )(8)where xt|at = (xt|at , xt−1|at , zt|at )′ and H is the steady state Kalman gain for ob7

See Appendix D.1 for a detailed derivation of the model solution.

10

serving productivity at .
Moreover, conditional on agents’ beliefs xt|at , updating beliefs with the noisy
signal st leads toxt|t = xt|at + G(st − st|t−1 )To study the role of information unrelated to present (or past) fundamentals
in consumption dynamics, we consider the level of spending agents would havechosen with the information set Ωt denoted by ct|at :ct|at =

1
xt|at − ρxt−1|at1−ρsuch that without observing a noisy signal st , agents choose spending as a functionof their beliefs about the current and lagged permanent productivity with theinformation set Ωt .
We further define ∆ct|st as consumption changes at time t due to information
unrelated to present (or past) fundamentals:
∆ct|st =




1
1
2
G − ρGst − xt|at1−ρ

where G1 and G2 are the first and second components of the steady-state Kalman
gain G and represent respectively the gain of observing noisy signals on xt and
xt−1 . Thus, when st is greater than xt|at , ∆ct|st would be positive. Intuitively,
when agents receive good information about the state of the economy (st > xt|at ),they would be willing to increase spending.
It is straightforward to show thatct = ct|at + ∆ct|st
We can also define ∆ct|at , consumption changes at time t from the previous
period’s consumption due to fundamentals:∆ct|at = ct|at − ct−1

11

From Equation (8) and the definition of ct−1 , we have
∆ct|at =




1
1
2
H − ρHat − xt|t−11−ρwhere H 1 and H 2 are the first and second components of the steady-state Kalman
gain H and represent respectively the gain of observing productivity on xt and xt−1 .
Whenever at is greater than xt|t−1 , the last period’s forecast on the permanent
productivity component, ∆ct|at is positive and vice versa: When agents receive
good information compared to a benchmark (in this case, the last period’s estimate
on xt ), they would increase spending.
We have thus successfully disentangled changes in consumption into changes
due to fundamentals and changes due to information unrelated to present (or past)
fundamentals:∆ct = ∆ct|st + ∆ct|atsuch that we can easily decompose the rate of consumption growth into two subcomponents.
We define our measure of consumer confidence as follows.
Definition 1 The Model-Based Consumer Confidence Index (MB-CCI) at time t
is given by
Model-Based Consumer Confidence Indext = ŝt − x̂t|at

where ŝt and x̂t|at are the estimated noisy signal and beliefs about the permanent
productivity component conditional on productivity at time t. Both of these series
are estimated using the Kalman smoother.
The Model-Based Consumer Confidence Index (MB-CCI) retrieves the contribution of additional information unrelated to present (or past) fundamentals
on actual consumption changes. Our interpretation of consumer confidence emphasizes that it is a relative measure. Confidence is inherently related to agents’
information, but we are being careful to distinguish the sources of information

12

when measuring confidence.8
Throughout the paper, we also consider the medium-frequency MB-CCI, which
we obtain by applying a band-pass filter at 32-200 frequencies. The main purposeof exploiting this medium frequency measure is to clearly visualize the slow-moving
dynamics of MB-CCI, which is highly volatile due to the presence of noise shocks
in our model.
In the next section, we estimate the MB-CCI and compare it against the surveybased counterpart for the U.S.

8

Our confidence index is not simply related to news-type ingredients (a long-run productivity
innovation ϵt or a long-run productivity process xt ) nor to the animal spirit-type ingredients (apure noise shock νt or a noisy signal st ). Rather, our confidence measure is related to additional
information available to consumers beyond the information pertained in productivity/income.

13

3

Results for the U.S.

As discussed in the previous section, we solve the model sequentially and proceed
to estimation. As econometricians, we can represent the dynamics of the modelin a state-space form with the appropriate observation equations, which in thiscase includes productivity and consumption. Consumers’ expectations are part of
the unobserved state vector of the econometrician. The econometrician’s Kalmanfilter is used to construct the likelihood function and to estimate the parametersof the model. The model is estimated by maximum likelihood. In Appendix D.2
we show how to compute the likelihood function for a general representative-agent
model with signal extraction where the signal is delivered sequentially.
We first solve the consumers’ Kalman filter. We then build the econometrician’s
filter taking into account consumers’ expectations. (Consumers’ expectations areincluded in the list of unobservable state variables.) Our estimation includes thedemeaned first differences of the logarithm of labor productivity and of the logarithm of per-capita consumption as observables. The simplicity of this modelallows extracting a significant amount of information using only these two series.
We use a Kalman smoother to estimate the shocks to the permanent and thetransitory component of productivity, the noise shock, and the unobservable statevariables. Our sample is given by the period 1976:II–2019:III, which includes therecent Great Recession.

3.1

Data

Our dataset includes series on labor productivity and per capita real consumption
expenditure. To construct a series for labor productivity (real GDP divided by
the labor input), we use a quarterly real gross domestic product (GDPC1) fromthe U.S. Bureau of Economic Analysis and employment (LNS12000000Q) from the
U.S. Bureau of Labor Statistics. Similarly, to construct a series for per capita realconsumption expenditure (real consumption expenditure divided by the total population), we use a quarterly real personal consumption expenditure (PCECC96)
and population (LNS10000000Q) where the first series was taken from the Bureau of Economic Analysis and the second series from the U.S. Bureau of Labor
Statistics. Recession indicators for the United States are based on NBER-defined14

recessions. For the consumer confidence index, we use the Index of Consumer
Sentiment (ICS) from the University of Michigan.

3.2

Model estimation

We first present the estimation results for the model in Section 2. Table 1 reports
the estimation results. The results show that the persistence parameter ρ is estimated to be highly persistent. Due to this high persistence, the standard deviation
for permanent productivity shocks is very small. The standard deviation for noisy
shocks is estimated to be large.
Table 1: Parameter Estimates, US 1976:II–2019:III
Parameter

ρ
σu
σϵ
ση
σν

Description
Persistence productivity
Std dev. productivity
Std dev. permanent shock (implied)
Std dev. transitory shock (implied)
Std dev. noise shock

Value
0.96130.00580.00020.00570.0121s.e.
0.00680.00020.0036

Notes: The parameter σu defined by the standard deviation of ∆at . Given the random walk Assumption (2) for
at , σϵ and ση are determined by ρ and σu . As they are indirectly recovered, no standard errors are given.

Figure 1 reports impulse responses of productivity and consumption following
three exogenous shocks. We use the estimated parameters in Table 1. Due toa high productivity persistence, productivity gradually builds up (in the case ofpermanent technology shock) and slowly declines after an initial increase (in thecase of transitory technology shock). A noise shock does not affect productivity.
Following a permanent productivity shock, consumption gradually increases. Dueto large volatilities in transitory and noise shocks, consumers cannot immediately
recognize the permanent shock and adjust consumption slowly. In response toa transitory productivity shock, consumption initially increases but returns tonormal over time. Following a noise shock, consumption initially increases andslowly declines.
Figure 2 reports the implications of the estimated parameters in Table 1 forthe variance decomposition of consumption, summarizing the contribution of thethree shocks to the forecast error variance. We observe that noise shocks are a very
15

Figure 1: Impulse responses
Transitory

Permanent
0.01

Noise

0.01

0.01
consumption
productivity

0.005

0.005

0

0.005

0
0

5

10

15

20

0
0

5

10

15

20

0

5

10

15

20

Notes: Productivity does not respond to a noise shock.

important source of short- to medium-run volatility, explaining more than 60% ofconsumption volatility at a one-year horizon (light gray areas). On the contrary,both permanent (black areas) and transitory productivity (gray areas) shocks explain a much smaller fraction of consumption fluctuations, having almost no effecton quarterly volatility (permanent) and explaining less than 20% (transitory) at a
one-year horizon.
Figure 2: Variance decomposition: consumption (left) and productivity (right)
1

1

0.75

0.75

0.5

0.5

0.25

0.25

0

0
10

20

30

40

10

20

30

40

Notes: The black areas, the gray areas, and the light gray areas respectively represent a contribution of permanent
technology shocks, transitory technology shocks, and noise shocks to consumption fluctuations over different time
horizons.

16

3.3

Model-based consumer confidence for the U.S.

We now follow the procedure discussed in the last section and extract MB-CCI
by smooth-estimating structural shocks and state variables. The solid lines in
Figure 3 denote our model-based measures estimated for the sample period, andto compare our model-based consumer confidence to survey-based one, we also
plot the Index of Consumer Sentiment (the thin and dashed, black line). The
high-frequency measure (thin and full, red line) denotes our confidence measure as
defined in Definition 1, and the medium frequency measure (thick and full, blueline) is the one isolating medium-run dynamics using a band-pass filter at 32-200
frequencies.
Figure 3: Model-Based Consumer Confidence Index (solid), and the (survey-based)
Index of Consumer Sentiment (dashed)
120

0.04

100

0.02

80

0

60

-0.02

40

-0.04
1980

1985

1990

1995

2000

2005

2010

2015

Notes: Shaded areas indicate U.S. recessions. The red and thin solid line denotes the MB-CCI (blue and thicksolid for the medium frequency MB-CCI), whereas the black and thin dashed line denotes the ICS. The ICS
corresponds to the left y-axis and the MB-CCI to the right y-axis.

Our result shows that the correlation between the two indices is strictly positive(0.52) and statistically significant at the 1% level. For the medium frequency
confidence measure the correlation is estimated at 0.79. In addition, most of
U.S. recessions are characterized by preceding downward shifts and subsequent
recovery in consumer confidence in both measures. Our approach to extracting
consumer confidence does a good job of mimicking the dynamics of the survey-

17

based confidence index for the U.S. data.9
In order to justify our focus on what we have defined as MB-CCI, we also
consider all other estimated series of beliefs, shocks, and states in our model. We
compute their correlation with the survey-based consumer confidence measure.
Table 2 reports the results. While some have a positive, statistically significant
correlation with the survey-based confidence, namely beliefs about the long-run(corr = 0.37), permanent TFP (corr = 0.16), noisy signals (corr = 0.23), andnoise shocks (corr = 0.26), our confidence measure exhibits a higher correlation
than any other series of beliefs, shocks and states.
Table 2: Estimated unobserved shocks and states and the survey-based confidenceŝt − x̂t|atât+∞|tν̂tŝtx̂t∆ct|at
ẑt|t
x̂t|t
η̂t
ϵ̂t
x̂t−1|tx̂t|atẑtDescription
Our confidence measure (MB-CCI)
Our confidence measure (MB-CCI, medium frequency)
Beliefs about the long-run
Noise shocks
Noisy signals
Permanent component
Consumption change due to fundamentals
Beliefs about transitory component
Beliefs about permanent component
Transitory productivity shocks
Permanent productivity shocks
Beliefs about lagged permanent component
Beliefs about permanent component (with info. set Ωt )Transitory component

Correlation
0.52
0.79
0.37
0.26
0.23
0.16
0.13
0.08
0.05
0.05
0.03
0.03
0.02
-0.10

p-val
0.00010.00010.00010.00060.00220.0397
0.09200.29790.53870.4956
0.67220.71460.82870.1743

Notes: Correlation and p-val report the Pearson correlation coefficient and the associated p-value between thesurvey-based consumer confidence and the estimated variable of interest.

The finding that the MB-CCI is the measure that correlates the most with
the ICS is of independent interest for the news shocks literature. Ex-ante, it
is unclear why this measure ought to have the highest correlation, and not, for
9

The Index of Consumer Sentiment (ICS) has two separate components - Expected Index andCurrent Index. Expected Index is aggregated using the answers to the forward-looking questions,whereas Current Index to the questions regarding the current situation. Separately estimating
the correlation between these indices and our measure of consumer confidence, the correlation
between Expected Index and our measure is 0.51 and the one between Current Index and ourmeasure is 0.49.

18

example, beliefs about the long run (ât+∞|t ) or the noisy signal (ŝt ). This offersa theoretical window to interpret what lies behind fluctuations in the ICS: mostof the fluctuations represent fluctuations in beliefs that go beyond the beliefs thatare implied by the observation of fundamentals as productivity or income.
To conclude, our main empirical contribution is to establish that the MBCCI we have defined above closely mimics the ICS. We emphasize that this isachieved out-of-sample (the ICS is not used in the model estimation.) Therefore,
conceptually, this establishes a bridge between the survey measure, and the news
and noise model of consumer beliefs. Next, we will apply this insight to a crosscountry exploration.

19

4

Consumer confidence across Europe

In this section, we extract model-based consumer confidence for fourteen selected
European countries and make a comparison with European consumer confidence
indices. (Below we explain our focus on these countries.) Similar to the observation in the U.S., recent economic crises have been associated with deteriorating
consumer confidence in Europe as well.

4.1

Model estimation

We first present the estimation results for the model discussed in Section 2. The
sample is from 1995:II–2019:III. The model is estimated by maximum likelihood.
Our dataset includes series on labor productivity and per capita real consumption expenditure, and our sample includes fourteen European countries: the fivefounding member states of the EU - Belgium (BEL), France (FRA), (West) Germany (DEU), Italy (ITA), and the Netherlands (NLD) - along with nine othermember states who joined the EU on or before January 1995 - Austria (AUT),
Denmark (DNK), Finland (FIN), Greece (GRC), Ireland (IRL), Portugal (PRT),
Spain (ESP), Sweden (SWE), and the United Kingdom (GBR). We focus on thesefourteen countries in part due to data availability. Harmonized consumer surveys
are conducted by the Directorate General for Economic and Financial Affairs for
the European Union (EU) and the applicant countries.10 However, for some countries, the harmonized survey is only available from 2001 (Bulgaria, Cyprus, Latvia,
Lithuania, Poland, and Romania), 2002 (Luxembourg and Malta), 2005 (Croatia),
2007 (Turkey), 2012 (Montenegro and North Macedonia), 2013 (Serbia), and 2016
(Albania). Thus, we do not include these countries along with other countries who
became member states of the EU in the fourth wave of the enlargement in 2004.
To construct a series for labor productivity (real GDP divided by the labor
input), we use a quarterly Real GDP from the OECD contained in the measure
VORBASA and Total Employment from the Eurostat in the measure Total Employment - Domestic Concept. Both series are seasonally adjusted. Similarly, to
10

The full list includes Belgium, Bulgaria, the Czech Republic, Denmark, Germany, Estonia,
Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary,
Malta, Netherlands, Austria, Poland, Portugal, Romania, Slovenia, the Slovak Republic, Finland,
Sweden, the United Kingdom, Montenegro, North Macedonia, Albania, Serbia, and Turkey.

20

construct a series for per capita real consumption expenditure (real consumption
expenditure divided by the total population), we use a quarterly Private Final Consumption Expenditure from the OECD contained in the measure VORBASA andTotal Population from the Eurostat in the measure Total Population. Both series
are seasonally adjusted. For the survey-based measure, we use the Consumer Confidence Index (CCI) from the OECD. Since it is published in monthly frequency,
we change it to quarterly frequency by computing the quarterly arithmetic average
at every quarter.
The estimation results show that the persistence parameter ρ is estimated to behigh for all countries. Due to this high persistence, the standard deviation for permanent productivity shocks is very small. The estimates of the standard deviation
for noisy shocks are, in general, large, but vary considerably across countries.11

4.2

Model-based consumer confidence across Europe

We extract consumer confidence by estimating the series of structural shocks andstate variables using a Kalman smoother. We then use the same procedure described above and used for the U.S.
We first present a figure comparing the MB-CCI and the CCI for each country. Among the high- and medium-frequency measures of MB-CCI, the mediumfrequency measure allows for a clearer visual comparison to the CCI, and therefore
we present this one here in the body. (See Figure A6 in Appendix G for the otherone.) See Figure 4. It plots the MB-CCI using solid lines, and the CCI from
OECD using dashed lines.
As it is clear from the figure, most countries exhibit sizable fluctuations in
confidence according to both measures. Consumer confidence appears to be persistent. In several cases, there is a decline of confidence that is contemporaneous
to or lags the global financial crisis of 2008. We observe that there is an extended
period of lack of consumer confidence for many countries, which corresponds wellto the slow and anemic recovery from the Great Recession across Europe. As one
would expect, this decline in confidence is more protracted for periphery countries
as Portugal and Greece. Portugal and most countries exhibit high confidence inthe early part of the sample, which presumably is related to widespread optimism
11

Table A1 in Appendix E reports the estimation results.

21

Figure 4: Model-Based Consumer Confidence Index (at Medium Frequency), andthe OECD (Survey-Based) Consumer Confidence Index
AUT

BEL

-0.008
1995

2000

2005

2010

2015

97 -0.008
1995

103 0.002

2000

2005

DNK
103

2000

2005

2010

2015

2000

2005

2010

99 -0.008
1995

102

0.02

98

-0.02
1995

2015

2010

2015

2000

2005

2000

2005

2015

2005

2010

2015

96 -0.004
1995

106

96
2000

2010

2005

2010

2015

GRC
103

0.08

97

-0.08
1995

2015

104

95
2000

2005

2010

2015

NLD

97
2000

2005

2010

2015

-0.01
1995

103

98
2000

2005

2010

2015

SWE
103 0.015

2000

2010

103 0.015

PRT
0.015

-0.02
1995

2010

0.02

96 -0.025
1995

2005

FIN

ITA
106

2005

97
2000

104 0.006

IRL

2000

98.5-0.002
1995

GBR

0.015

-0.015
1995

2015

0.01

FRA
0.015

-0.015
1995

2010

104

ESP

0.006

-0.006
1995

DEU

103 0.009

0.006

2015

96 -0.015
1995

105

96
2000

2005

2010

2015

Notes: The solid lines denote the MB-CCI (medium frequency) isolated with the band-pass filter at 32-200
frequencies. The dashed lines denote the (quarterly) Consumer Confidence Index (CCI) available from the OECD.
Since it is published in monthly frequency, we change it to quarterly frequency by computing the quarterly
arithmetic average at every quarter. The MB-CCI is plotted against the right y-axis, and OECD consumer
confidence against to the left y-axis. For Sweden, the CCI is available only from 1995:IV (SWE).

22

regarding the European Economic and Monetary Union in the late 1990s.
Another interesting observation is that both measures seem to correlate strongly
for some countries (as Italy), but less so for other countries (as, for instance, Germany). In other to look more deeply at this aspect, we compute the correlation
between both measures and report it in Table 3. The table reports the correlation
between our confidence measure and the survey-based counterpart. We report thiscorrelation both for our high-frequency MB-CCI and for our medium-frequency
MB-CCI. As explained in Section 2.3, the high-frequency MB-CCI corresponds
simply to the measure obtained as in Definition 1. The medium-frequency MBCCI corresponds to the resulting series after using a band-pass filter at 32-200
frequencies.
We find that, for most countries, the correlations between the two indices
are strictly positive and statistically significant at the 1% level: for Spain, Italy,
Portugal, the UK, Netherlands, Ireland, France, and Greece, for example, thecorrelations are estimated to be around 0.4 or greater, showing a clear correlation
between the two indices. For some countries like Spain, Italy, Portugal, or the
U.K., the degree of correlation is remarkable: higher than 0.70 in the case of
the medium-frequency MB-CCI. At the same time, there are cases in which thecorrelations are quite small, as in the case of Austria, Belgium, or Sweden.
We draw two main conclusions from these results.
First, considering the high correlation of the MB-CCI with the CCI producedby the OECD based on a survey, the MB-CCI does seem like a valid approach
to measure consumer confidence. This is more clearly the case for countries thatexhibit a high correlation (top rows on Table 3), which is most European countries(these tend to be countries for which there appear to have been larger confidence
swings, as we will discuss below.) Moreover, given the solid theoretical basis of
the MB-CCI and how easily it can be obtained from national accounts data, it is,at the very least, a complementary measure to the CCI in the case of all the othercountries.
Second, there is a striking amount of heterogeneity in the correlation between
the MB-CCI and CCI. Indeed, this correlation behaves like in the U.S. for somecountries (high and statistically significant correlation, as in the case of Spain witha correlation of 0.63), and with the opposite pattern for other countries (as in thecase of Belgium, with a correlation of 0.01). This is a puzzling observation given
23

Table 3: Correlation between our model-based confidence measure and OECD
consumer confidence index (14 European countries)

High frequency
ESP
ITA
PRT
GBR
NLD
IRL
FRA
GRC
DNK
SWE
DEU
FIN
BEL
AUT

0.63
0.54
0.53
0.49
0.48
0.46
0.41
0.39
0.25
0.15
0.07
0.02
0.01
-0.05

Correlation
p-val Medium frequency

p-val

0.00010.00010.00010.00010.00010.00010.00010.00010.01190.13840.51590.81080.96800.64970.00010.00010.00010.00010.00010.00010.00010.00010.00010.70870.75220.00010.18930.4443

0.85
0.85
0.87
0.71
0.76
0.93
0.77
0.52
0.52
0.04
-0.03
0.39
-0.13
0.08

Notes: Correlation and p-val report the Pearson correlation coefficient and the associated p-value. The highfrequency measure denotes the smoothed-estimated confidence as in Definition 1, and the medium frequency
measure is the one isolating medium-run dynamics using a band-pass filter at 32-200 frequencies.

that the input used to obtain the MB-CCI is obtained from uniformly constructed
data, with an identical model and estimation procedure for all countries. Moreover,
the CCI survey is conducted by the same institution, and as far as we can tell fromstudying its description, it is based on a uniform set of questions and procedures.12
Hence, an obvious question is to what extent one can shed light on this finding.
We briefly look at this next.
12

The CCI survey is part of regular harmonized surveys conducted by the Directorate General for Economic and Financial Affairs for different sectors of the economies in the European Union (EU) and in the applicant countries. The methodology of the survey including national questionnaires, partner institutes, and guidelines are available from thefollowing link: https://ec.europa.eu/info/business-economy-euro/indicators-statistics/economicdatabases/business-and-consumer-surveys/methodology-business-and-consumer-surveys en.

24

4.3

What could explain the heterogeneity across countries?

As shown earlier, we observe a surprisingly high amount of heterogeneity in therelation of MB-CCI and the CCI of the OECD. What accounts for such observed
heterogeneity?
In our sample, the high correlation countries include the U.K., Netherlands,
France, and the PIIGS countries, i.e., Portugal, Ireland, Italy, and Spain. One
possibility from this observed pattern is the presence of large fluctuations, particularly during the 2008 global financial crisis and its aftermath in Europe. To
illustrate this point, consider Figure 5, plotting the MB-CCI and annualized quarterly consumption growth rates for two polar countries, Italy and Germany. Italy
was hard hit by the European debt crisis, and this generated a protracted consumption recession starting. Instead, consumption recovered quickly after 2008 in
the case of Germany. As a result, the swings in consumer confidence in Italy are
more dramatic. Accordingly, the correlation between the MB-CCI and the CCI ishigh for Italy (0.54), and low for Germany (0.07).
Figure 5: Model-based consumer confidence (dashed) and consumption growth
rates (solid): Italy (left) and Germany (right)
Italy

Germany

0.02

0.03

0.01

0.02

0

0.01

0.009

0.03

0.006

0.02

0.003

0.01
0

0
-0.01

0

-0.02

-0.03
1995

-0.01

-0.02
2000

2005

2010

2015

-0.003-0.01-0.006-0.02-0.009
1995

-0.03
2000

2005

2010

2015

Notes: The solid lines denote annualized quarterly consumption growth rates. The dashed lines denote the MBCCI (medium frequency) isolated with a band-pass filter at 32-200 frequencies. The consumption growth rates
correspond to the right y-axis and the MB-CCI to the left y-axis.

To make this point more precisely, we consider this heterogeneity from a statistical perspective. Let the MB-CCI, s1,t , be the sum of true unobservable confi-

25

dence, κt and a disturbance term e1,t :s1,t = κt + e1,twhere e1,t is an i.i.d. Gaussian disturbance. Similarly, let the CCI, s2,t , be the sumof true confidence and a different disturbance term e2,t :s2,t = κt + e2,twhere e2,t is an i.i.d. Gaussian disturbance and e1,t ⊥ et+j for all t and j.
The disturbance terms could be interpreted as measurement errors. For thesurvey measure of confidence, there is the problem of sampling the population.
Also, each participant answers the survey on a particular day while consumption
and productivity are averages over quarters. This adds measurement error to thesurvey. In contrast, the model-based measure of confidence is very simple andclearly imperfect. It is correlated with survey confidence, but we would not claim
it is true confidence measured without error.
As shown by this Equation (9)Corr =

var(κ)
1/2(var(κ) + var(e1 ))(var(κ) + var(e2 ))(9)the correlation between s1 and s2 is an increasing function of the variance of true
confidence κt . Thus, if the differences across countries are mostly var(κ), that is,the variance of true confidence, the correlation between MB-CCI and CCI should
be high.
Let us take the survey confidence measure itself to compute its volatility. Table
4 suggests that the correlation between the two measures of consumer confidence
is related to the volatility of the survey confidence measure: a high correlation
between two measures of consumer confidence is related to a larger volatility ofthe survey index.
Second, we look at the observed heterogeneity from a socio-economic perspective. Given the small sample size (n = 14), it is difficult to determine structural
factors delivering heterogeneity in terms of the correlation across countries. However, we can still see if the correlations reported on Table 3 are related to a range
26

Table 4: The volatility of confidence index and correlation between the two confidence measures
Correlation
Whole sample
High correlation
Low correlation

High frequency

Medium frequency

0.31
0.49
0.08

0.51
0.78
0.15

Volatility
1.213
1.363
1.012

Notes: Correlation denotes the average correlation coefficients between the model-based and survey-based consumer confidence in the sample, and Volatility denotes the average standard deviation of the survey-based consumer confidence in the respective sample. The first sub-sample (high correlation) contains those countries withthe correlation between the model-based and survey consumer confidence higher than 0.39 and includes Spain,
Italy, Portugal, the UK, Netherlands, Ireland, France, and Greece; the second sub-sample (low correlation) contains those with the correlation smaller than 0.25 in absolute terms and includes Austria, Belgium, Finland,
Germany, and Sweden.

of indicators of social, economic, and financial development. To this end, we show
Figure 6 the relationship between observed correlation heterogeneity with selectedeconomic, social, and institutional factors.13 Those that can potentially explain
observed heterogeneity include public spending on education, general government
spending, total health spending, and a median relative income of elderly people
among others whereas general government debt, fertility rates, the part-time employment rate, and the expenditure on pensions exhibit virtually no relationshipwith the observed confidence correlation. (Figures A7 and A8 in Appendix Greport results for another set of socio-economic variables.)The most plausible explanation for the observed heterogeneity, though, is thedegree to which the survey measure of consumption tracks actual consumption fluctuations. For those countries exhibiting a low correlation between our model-based
index and the survey-based confidence measure, we also observe a low correlation
between the survey-based one and observed consumption: Austria (0.05), Belgium(0.09), Germany (0.17), Finland (0.18), and Sweden (0.26). This finding is instark contrast to the countries where the fit between two confidence measures ishighly correlated (with the average correlation of 0.51 between the survey measureof confidence and actual consumption). It appears that when the survey measure does not replicate observed consumption dynamics, our model-based index
cannot match it well as our measure is obtained using observed consumption and13

Appendix B gives a detailed description of these factors.

27

1

r = 0.45

1.2

1

1

1
0.8
0.6
0

0.5

r = 0.04

2

Fertility rate

0.5

Exp. social protection

Elderly relative income

0

1.5

1
0

0.5

Health spending3

r = -0.52

50
40
30
0

35

0.5

30
25
20
0.5

20

0
0

0.5

10
8
6

1

0.5

1

r = 0.06

150

100

50
0

1

r = 0.0340

r = -0.49

0r = -0.38

0

12

1

Government debt

460

Pension spending

5

Government spendingr = -0.64

6Part-time employment

Education spending

Figure 6: Correlation of confidence indices and economic, social, and institutional
factors

20

0.5

1

r = -0.03

15
10
5
0

0.5

1

Notes: The panels show the relationship between various economic, social, institutional measures and the correlation of two confidence indices. r reports the Pearson correlation coefficient. Education spending denotes public
spending on education; Government spending denotes general government spending; Health spending denotes
total health spending; Elderly relative income denotes a median relative income of elderly people; Exp. social
protection denotes expenditure on social protection; Government debt denotes general government debt; Pension
spending denotes the expenditure on pensions. For a detailed description of the variables, see Appendix B.2.

productivity.

28

5Final remarks

We have shown how to extract consumer confidence using aggregate macroeconomic data based on a structural framework with imperfect information. We view
ours as a viable approach to study consumer confidence, which is based on a standard consumer theory and the state-of-the-art macroeconomic toolbox. Not only
do our efforts provide a theoretical interpretation to survey measures of confidence,but they also offer an internationally consistent measure of confidence groundedon the System of National Accounts.
We compare our measure of confidence with its survey-based counterpart bycalculating the correlation between the two measures of consumer confidence. We
have shown that the correlation between the two measures is remarkable for the
U.S. and a range of European countries.
Our methodological approach relies on a particular economic mechanism (thepermanent income model) and information structure (the combination of the permanenttransitory decomposition and the signal on the permanent component). They are
analytically convenient and, more importantly, reasonable for the application athand, as discussed in Section 2.1. Considering its simple nature, the model’s actual performance in generating filtered confidence and matching the survey-based
counterpart is striking and somewhat surprising. Nevertheless, an obvious next
step is to explore more complex models. We leave this to future work.

29

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31

A

Data appendix

A.1

The Index of Consumer Sentiment

The Index of Consumer Sentiment (ICS) is produced by the Survey ResearchCenter of the University of Michigan. The ICS is calculated by computing therelative scores for each of the five index questions on past and future financial,business, and macroeconomics conditions. Specifically, for each index question(Qi ), you subtract the percent giving unfavorable replies from the percent giving
favorable replies, then add 100 to compute the relative score Xi :ICS =X 1 + X2 + X 3 + X4 + X 5+ 2.0
base score

where X1 , ..., X5 denote the relative scores computed for each of the five index
questions, base score refers to the 1966 base period total of 6.7558, and 2.0 on thesecond term on the RHS is a constant to correct for sample design changes fromthe 1950s.
The five index questions are as follows:
Q1 : “We are interested in how people are getting along financially thesedays. Would you say that you (and your family living there) are better off
or worse off financially than you were a year ago?”
Q2 : “Now looking ahead–do you think that a year from now you (and yourfamily living there) will be better off financially, or worse off, or just about
the same as now?”
Q3 : “Now turning to business conditions in the country as a whole–do you
think that during the next twelve months we’ll have good times financially,or bad times, or what?”
Q4 : “Looking ahead, which would you say is more likely–that in the country
as a whole we’ll have continuous good times during the next five years or so,or that we will have periods of widespread unemployment or depression, or
what?”

32

Q5 : “About the big things people buy for their homes–such as furniture,a refrigerator, stove, television, and things like that. Generally speaking,do you think now is a good or bad time for people to buy major household
items?”The Index is available at http://www.sca.isr.umich.edu/tables.html.

A.2

Consumer Confidence Index

The consumer confidence indicator is calculated by computing the simple arithmetic average of the seasonally adjusted balances of answers to questions on thefinancial situation of households, the general economic situation, unemployment
expectations, and savings over the next 12 months.
The questions relevant for computing the consumer confidence indicator arechosen from the full set of questions in the individual survey and are given as
follows:
Q2 : “How do you expect the financial position of your household to changeover the next 12 months? ”
Q4 : “How do you expect the general economic situation in this country todevelop over the next 12 months?”
Q7 : “How do you expect the number of people unemployed in this country
to change over the next 12 months?”
Q11 : “Over the next 12 months, how likely is it that you save any money?”For each questions, there are six possible answers, i.e., strongly positive, positive to neutral, negative, and strongly negative, as well as “don’t know.”More details are available from the European Commission Directorate-General
For Economic and Financial Affairs (European Commission).

33

[FOR ONLINE PUBLICATION]

Supplementary Material for “Confidence,
Fundamentals, and Consumption”
Jean-Paul L’Huillier, Robert Waldmann, Donghoon Yoo
November 2021

B

Economic and social factors

This section provides a detailed description of the variables used in Section 4.3 andAppendix G. The nine financial development indices are from the IMF Financial
Development Index Database which is maintained by the IMF Strategy, Policy,
and Review Department.14 Other variables are from the OECD database.

B.1

IMF Financial Development index

The Financial Development index is constructed using a three-step approach reducing multidimensional data into one summary index: (i) normalization of relevant
variables; (ii) aggregation of normalized variables into the sub-indices representing
a particular functional dimension; and (iii) aggregation of the sub-indices into thefinal index.15
Specifically, Financial Development index (FD) is the highest level aggregate index representing a relative ranking of countries on the depth, access, andefficiency of their financial institutions and financial markets. It is an aggregate of
the Financial Institutions index and the Financial Markets index.
The Financial Institutions index (FI) is an aggregate of the Financial
Institutions Depth index (FID), the Financial Institutions Access index(FIA), and the Financial Institutions Efficiency index (FIE). FID compiles
data on bank credit to the private sector in percent of GDP, pension fund assets
14

See Sahay et al. (2015) for details.
IMF Financial Development Index Database contains information on its methodology anddataset (https://data.imf.org/?sk=F8032E80-B36C-43B1-AC26-493C5B1CD33B).
15

34

to GDP, mutual fund assets to GDP, and insurance premiums, life and non-life
to GDP; FIA compiles data on bank branches per 100,000 adults and ATMs per
100,000 adults; FIE compiles data on banking sector net interest margin, lendingdeposits spread, non-interest income to total income, overhead costs to total assets,
return on assets, and return on equity.
The Financial Markets index (FM) is an aggregate of The Financial
Markets Depth index (FMD), The Financial Markets Access index (FMA),
and The Financial Markets Efficiency index (FME). FMD compiles data on
stock market capitalization to GDP, stocks traded to GDP, international debt securities of government to GDP, and total debt securities of financial and non-financial
corporations to GDP; FMA compiles data on the percent of market capitalization
outside of 10 largest companies and the total number of issuers of debt (domestic
and external, nonfinancial and financial corporations) per 100,000 adults; FME
compiles data on the stock market turnover ratio (stocks traded to capitalization).
For a detailed description, see Svirydzenka (2016).

B.2

OECD data

The following variables are taken from the OECD database: variables on education (public spending on education), on society (expenditure on social protection, median relative income of elderly people, pension spending, the aggregate
replacement ratio, total fertility rate), on health (health spending), on government(general government debt, general government deficit, general government financial wealth, general government spending), on jobs (adequacy of minimum income
benefits, employment rates, hours worked, part-time employment, the gender wagegap), and on the economy (household saving, household spending).
Public spending on education includes direct expenditure on educational
institutions as well as educational-related public subsidies given to households andadministered by educational institutions. This indicator is shown as a percentage
of GDP, divided by primary, primary to post-secondary non-tertiary, and tertiarylevels. Public spending includes expenditure on schools, universities, and otherpublic and private institutions delivering or supporting educational services.
Expenditure on social protection contains social benefits, which consist oftransfers, in cash or in-kind, to households and individuals to relieve them of the35

burden of a defined set of risks or needs; administration costs, which represent thecosts charged to the scheme for its management and administration; other expenditure, which consists of miscellaneous expenditure by social protection schemes(payment of property income and other). Median relative income of elderly
people is defined as the ratio between the median equivalized disposable incomeof persons aged 65 or over and the median equivalized disposable income of persons
aged between 0 and 64. Pension spending is defined as all cash expenditures(including lump-sum payments) on old-age and survivors pensions. Old-age cash
benefits provide an income for persons retired from the labor market or guarantee
incomes when a person has reached a ‘standard’ pensionable age or fulfilled thenecessary contributory requirements. This indicator is measured in percentage ofGDP. The aggregate replacement ratio is the gross median individual pension
income of the population aged 65-74 relative to gross median individual earnings
from work of the population aged 50-59, excluding other social benefits. Total
fertility rate in a specific year is defined as the total number of children thatwould be born to each woman if she were to live to the end of her child-bearing
years and give birth to children in alignment with the prevailing age-specific fertility rates. It is calculated by totaling the age-specific fertility rates as defined over
five-year intervals. This indicator is measured in children per woman.
Health spending measures the final consumption of health care goods andservices (i.e. current health expenditure) including personal health care (curative
care, rehabilitative care, long-term care, ancillary services, and medical goods)
and collective services (prevention and public health services as well as healthadministration), but excluding spending on investments. It is measured as a shareof GDP, as a share of total health spending, and in USD per capita (using economywide PPPs).
General government debt measures the gross debt of the general government as a percentage of GDP. Debt is calculated as the sum of the following liability
categories (as applicable): currency and deposits; debt securities, loans; insurance,
pensions, and standardized guarantee schemes, and other accounts payable. General government spending indicates the size of government across countries.
This indicator is measured in terms of thousand USD per capita and as a percentage of GDP. General government deficit is defined as the balance of income
and expenditure of government, including capital income and capital expenditures.
36This indicator is measured as a percentage of GDP. General government financial wealth is the total value of its financial assets minus the total value of its
outstanding liabilities. The general government sector consists of central, state,
and local governments as well as social security funds. This indicator is measured
as a percentage of gross domestic product. For these variables, all OECD countries
compile their data according to the 2008 System of National Accounts (SNA).
Part-time employment is defined as people in employment (whether employees or self-employed) who usually work less than 30 hours per week in theirmain job. Employed people are those aged 15 and over who report that they haveworked in gainful employment for at least one hour in the previous week or who
had a job but were absent from work during the reference week while having aformal job attachment. This indicator shows the proportion of persons employed
part-time among all employed persons. The gender wage gap is defined as
the difference between median earnings of men and women relative to the medianearnings of men. Data refer to full-time employees on the one hand and to selfemployed on the other. (Average annual) hours worked is defined as the total
number of hours actually worked per year divided by the average number of people
in employment per year. Actual hours worked include regular work hours of fulltime, part-time, and part-year workers, paid and unpaid overtime, hours worked
in additional jobs, and exclude time not worked because of public holidays, annual
paid leave, own illness, injury and temporary disability, maternity leave, parental
leave, schooling or training, slack work for technical or economic reasons, strikeor labor dispute, bad weather, compensation leave, and other reasons. The data
cover employees and self-employed workers. This indicator is measured in terms ofhours per worker per year. Adequacy of minimum income benefits measures
the income of jobless families relying on guaranteed minimum income benefits as apercentage of the median disposable income in the country. Housing supplements
are included subject to relevant eligibility conditions. Employment rates arecalculated as the ratio of the employed to the working-age population. Employedpeople are those aged 15 or over who report that they have worked in gainful employment for at least one hour in the previous week or who had a job but wereabsent from work during the reference week. The working-age population refers
to people aged 15 to 64. This indicator is seasonally adjusted and it is measured
in terms of thousand persons aged 15 and over; in numbers of employed persons
37

aged 15 to 64 as a percentage of working-age population.
(Net) household saving is defined as household net disposable income plus
the adjustment for the change in pension entitlements less household final consumption expenditure (households also include non-profit institutions serving households). The adjustment item concerns (mandatory) saving of households, by building up funds in employment-related pension schemes. The net household savingrate represents the total amount of net saving as a percentage of net household
disposable income. It thus shows how much households are saving out of current
income and also how much income they have added to their net wealth. Household spending is the amount of final consumption expenditure made by resident
households to meet their everyday needs, such as food, clothing, housing (rent),
energy, transport, durable goods (notably cars), health costs, leisure, and miscellaneous services. Household spending including government transfers (referred toas “actual individual consumption” in national accounts) is equal to households’
consumption expenditure plus those expenditures of general government and nonprofit institutions serving households (NPISHs) that directly benefit households,
such as health care and education. Household spending including governmenttransfers is measured as a percentage of GDP. For these variables, all OECD countries compile their data according to the 2008 System of National Accounts (SNA2008).

38

C

Consumption and confidence in the model

In this section, we look at how consumption and MB-CCI comove (in the model).
Figure A1 shows the dynamics of consumption and confidence following alternative
structural shocks affecting the economy: Specifically, we consider the impulse responses of consumption and confidence following one standard deviation negative
shock to permanent productivity, transitory productivity, and the signal. In our
description, we focus mainly on the sign of the responses.
With a permanent decrease in productivity, consumption slowly decreases to its
new long-run level while confidence does not get affected by much. A (negative)
transitory shock generates an initial decrease in consumption, but consumption
returns to its original level. On the contrary, confidence initially moves in the
opposite direction of consumption and returns to its original level in the long run.
Following a negative noise shock, consumption behaves qualitatively similar to the
response to a negative transitory shock. However, the response of confidence ismuch greater on impact as it moves in the same direction with consumption. Afterthe first period, the behavior is qualitatively similar to the ones with a transitory
shock. Quantitatively, we can see that the dynamics of confidence are mostly
driven by noise shocks.
Figure A1: Impulse responses: confidence and consumption
Transitory

Permanent
0.006

Noise
0.015

0.002

confidence
consumption

0-0.006

0

0

0

5

10

15

20

-0.002

0

5

10

15

20

-0.015

0

5

10

15

20

Notes: We use parameter values estimated in Table 1 to deliver impulse responses following one standard deviation
negative shocks.

39

D

Solution

D.1

Solving the model

Consider the dynamic system:
xt = Axt−1 + Bvt
st = Cxt + Dvt′′′

and xt = (xt , xt−1 , zt ) , vt = (ϵt , ηt , νt ) , st = (at , st ) ,




"
#
"
#
1 + ρ −ρ 0
1 0 0
1
0
1
0
0
0




A= 1
, D=
0 0 , B = 0 0 0 , C =
1 0 0
0 0 1
0
0 ρ
0 1 0
Conditional on observing current productivity at , consumers’ beliefs xt|at are
given byxt|at = Axt−1|t−1 + H(at − at|t−1 )= [I − HC1 ]Axt−1|t−1 + Hat(10)

where H is the Kalman gain for observing productivity,
at = C1 xt + D1 vthihi
and C1 = 1 0 1 , D1 = 0 0 0 .
Then, observing a noisy signal st consumers’ beliefs xt|t are given by
xt|t = xt|at + G(st − st|at )= [I − GC2 ]xt|at + Gst
where G is the gain of observing new information st ,s t = C 2 xt + D 2 vt

40

(11)

h
i
h
i
and C2 = 1 0 0 , D2 = 0 0 1 .
Substituting xt|at from Equation (10) into Equation (11), we consumers’ expectations xt|t are given by
xt|t = [I − GC2 ][I − HC1 ]Axt−1|t−1 + [I − GC2 ]Hat + Gst
Once consumers’ expectations are formed, consumption can be solved:
ct = Et [at+∞ ] = Et [xt+∞ + zt+∞ ]1
xt|t − ρxt−1|t
=
1−ρ

D.2

Estimating the model

While the econometrician does not observe noisy signals, her information set includes productivity signals, assumed to be publicly available, and consumption
observations. Thus, she extracts consumers’ beliefs using all available information
with the following Kalman filter:





x
xt−1|t−1
xt|at
t−1
h
i






xt|at = xt−1|at  = A xt−2|t−1  + H 1 + ρ −ρ −ρ xt−2  + Hϵt + Hηt (12)zt−1
zt−1|t−1
zt|atConditional on xt|at , xt|t is given by


 



xt|t
xt|at
h
i xt−1 xt−1|t  = xt−1|at  + G 1 + ρ −ρ 0 xt−2  + Gϵt + Gηt + Gνt
zt|t
zt|at
zt−1
We let xE
t to represent the state vector of the econometrician where′xE
t = (xt , xt−1 , zt , xt|t , xt−1|t , zt|t )

41

(13)

then, xEt followsExE
t = Qxt−1 + R(ϵt , ηt , νt )

′

(14)

The matrices Q and R, which depend on the underlying parameters of themodel, are given respectively by
"

A 0
Q=
Q A

#

" #
B
R=
R
where Q, R, and A are given by"

1 + ρ −ρ ρ
Q=B
1 + ρ −ρ 0

#

"

#
"
#
"
#
1+ρ 0 01+ρ 0 01+ρ 0 0
R=B
+B
+B
1+ρ 0 01+ρ 0 01+ρ 0 0hihi
A = I − HC1 I − GC2 A
The observation equation is given by(at , ct ) = T XE
t
where

"

1 0 1
0
0
0
T =
0 0 0 1/ (1 − ρ) ρ/ (1 − ρ) 0

(15)

#

We then can build the state space representation of the model using (12), (13),(14) and (15) and structurally estimate it.

42

E

Estimation results for the European countries
Table A1: Parameter estimates (14 European countries), 1995:II-2019:III

AUT
BEL
DEU
DNK
ESP
FIN
FRA
GBR
GRC
IRL
ITA
NLD
PRT
SWE

Persistence (ρ)

Std. Perm. (σϵ )Std. Tran. (ση )Std. Noise (σν )0.9825 (0.0082)
0.9511 (0.0137)
0.9067 (0.0338)
0.9312 (0.0283)
0.9942 (0.0021)
0.9185 (0.0455)
0.9630 (0.0095)
0.9748 (0.0069)
0.9713 (0.0074)
0.9868 (0.0082)
0.9658 (0.0086)
0.9693 (0.0085)
0.9720 (0.0118)
0.9388 (0.0169)≤0.0001
0.00020.00070.0007≤ 0.0001
0.00090.00020.00010.00040.00030.00020.00020.00030.00050.00520.00440.00690.00990.00680.01110.00410.00580.01420.02300.00610.00800.00910.00760.0119 (0.0061)
0.0112 (0.0046)
0.0041 (0.0026)
0.0084 (0.0048)
0.0039 (0.0015)
0.0144 (0.0087)
0.0155 (0.0050)
0.0153 (0.0052)
0.0716 (0.0213)
0.0283 (0.0204)
0.0190 (0.0083)
0.0124 (0.0053)
0.0130 (0.0089)
0.0233 (0.0072)

Notes: The parameter σu defined by the standard deviation of ∆at . Given the random walk Assumption (2) for
at , σϵ and ση are determined by ρ and σu . As they are indirectly recovered, no standard errors are given.

Figures A2 and A3 report impulse responses of productivity and consumption
following three exogenous shocks for the fifteen countries in the sample. We use
the estimated parameters in Table A1. Due to a high productivity persistence,
productivity in general gradually builds up (in the case of permanent tech shock)and slowly declines after an initial increase (in the case of transitory tech shock).
A noise shock does not affect productivity.
Figure A3 shows that consumption slowly increases following a permanent techshock. This is because the large volatilities in transitory productivity and noise
shocks prohibit agents from immediately recognizing the permanent productivity
change. Thus, they adjust consumption slowly. Similarly, it takes time for consumers to recognize a temporal change in productivity or a noisy disturbance andreduce consumption after an initial impulse following a transitory tech. shock ora noise shock. How fast the adjustment takes place and how large the magnitudeof adjustments depends on the estimated volatilities of the shocks.
43

Figure A2: Impulse responses: productivity
AUT

DEU

BEL

0.008

0.01

0.008

0

0
0

10

20

30

40

0
0

10

DNK

20

30

40

20

30

40

10

20

30

40

10

20

30

40

10

20

30

40

20

0

10

ITA

30

40

40

20

30

40

30

40

NLD
0.008

0
10

30

GRC

0.008

0

20

0
0

IRL
0.025

0

10

0.02

0
0

0

GBR
0.008

0

40

0
0

FRA
0.005

30

FIN

0

0

20

0.015

0.01

10

10

ESP

0.015

0

0

0
0

10

PRT

20

30

40

0

10

20

SWE

0.01

0.01
Perm.
Tran.

0

0
0

10

20

30

40

0

10

20

30

40

Notes: Plots correspond to the impulse responses of productivity following technology shocks of one standard
deviation. The solid lines correspond to the impulse responses of permanent productivity shocks; the dashed lines
to those of transitory productivity shocks. Productivity does not respond to a noise shock.

Figure A4 reports the implications of the estimated parameters in Table A1 forthe variance decomposition of consumption, summarizing the contribution of thethree shocks to the forecast error variance. We observe that across countries noise
shocks are a very important source of short to medium run volatilities, explaining
44

Figure A3: Impulse responses: consumption
BEL

AUT

DEU

0.02

0.01

0.01

0

0

0

0

10

20

30

40

0

10

20

30

40

20

30

40

10

FRA

0

0
0

10

20

30

40

20

40

0

10

30

30

40

30

40

30

40

0
0

10

20

30

40

0

10

0.008

0

0
0

10

PRT

20

20
NLD

0.01

40

20
GRC

ITA

0
10

30

0.025

IRL
0.025

0

20
GBR

0.01

40

0
0

0.01

30

0.015

0

0

20
FIN

0.08

10

10

ESP

DNK
0.015

0

0

30

40

0

10

20

SWE

0.01

0.01

Perm.
Tran.
0

Noise

0
0

10

20

30

40

0

10

20

30

40

Notes: Plots correspond to the impulse responses of consumption to three shocks of one standard deviation. The
solid lines correspond to the impulse responses of permanent productivity shocks; the dashed lines to those oftransitory productivity shocks; the dotted lines to those of noise shocks.

more than 60 to more than 90% of consumption volatility at a one-year horizon. On
the contrary, both permanent and transitory productivity shocks explain a muchsmaller fraction of consumption fluctuations, having almost no effect on quarterly
volatility (permanent) and explaining less than 20% (transitory) for most countries
45

Figure A4: Variance Decomposition, 1995:II-2016:III
AUT

BEL

DEU

1

1

1

0.5

0.5

0.5

0

0
10

20

30

40

0
10

DNK

20

30

40

10

ESP
1

1

0.5

0.5

0.5

0
10

20

30

FRA

20

30

40

10

GBR
1

1

0.5

0.5

0.5

0
10

20

30

40

IRL

20

30

40

10

ITA
1

0.5

0.5

0.5

0
20

30

40

NLD

20

30

40

10

PRT
1

0.5

0.5

0.5

0
20

30

40

30

40

20

30

40

30

40

SWE

1

10

20

0
10

1

0

40

LUX

1

10

30

0
10

1

0

20
GRC

1

0

40

0
10

40

30

FIN

1

0

20

0
10

20

30

40

10

20

Notes: The black areas, the gray areas, and the light gray areas respectively represent a contribution of permanent
technology shocks, transitory technology shocks, and noise shocks to consumption fluctuations over different time
horizons.

at a one-year horizon. At the same time, we observe heterogeneity across countries.
For example, noise shocks are still an important source of consumption fluctuations
even at a ten-year horizon for countries such as Greece, Ireland, Netherlands,
46

Portugal, Spain, and the UK.

47

F

The alternative productivity process specification and estimated consumer confidence

We relax the parameter restrictions from Equations (3) and (4) and present theestimation results for the model in Section 2. We jointly estimate a set of parameters (ρx , ρz , σϵ , ϵη , σν ). Table A2 reports the estimation results. Figure A5
depicts estimated consumer confidence. As shown in the figure, these parametricconditions are not restrictive in the sense that our estimated consumer confidences
are very similar irrespective of whether we impose such restrictions or not.
Table A2: Parameter estimates, US 1976:II–2019:III
Parameter

ρx
ρz
σϵ
ση
σν

Description
Persistence permanent productivity
Persistence transitory productivity
Std dev. permanent shock
Std dev. transitory shock
Std dev. noise shock

Value
0.96120.96110.00020.00580.0122s.e.
0.00210.00270.00000.00030.0037

Notes: The parameter σu defined by the standard deviation of ∆at . Given the random walk Assumption (2) for
at , σϵ and ση are determined by ρ and σu . As they are indirectly recovered, no standard errors are given.

Figure A5: Estimated model-based consumer confidence index: 1976:II–2019:III
0.04

corr=0.99
0.02

0.00

-0.02

-0.04
1980

1985

1990

1995

2000

2005

2010

2015

Notes: Shaded areas indicate U.S. recessions. The dashed line denotes the MB-CCI estimated with parametersin Table 1 whereas the solid line denotes the MB-CCI estimated with parameters in Table A2. corr denotes thecorrelation coefficient between them.

48

G

Extra figures

Figure A6 plots our consumer confidence estimated for the sample period (solidlines) along with Consumer Confidence Index from OEDC (dashed lines). We
also show the relationship between observed heterogeneity across countries and
IMF financial development index (Figure A7) and various economic, social, andinstitutional factors (Figure A8).

49

Figure A6: Consumer confidence and OECD Confidence Index
AUT

BEL

0.04

-0.04
1995

2000

2005

2010

103

0.04

97

-0.02
1995

2015

2000

2005

2015

2000

2005

2010

2015

99 -0.015
1995

2000

2005

2005

2010

97
2000

2010

104

0.06

102

0.06

98

-0.06
1995

2015

2000

2005

IRL

2010

96

-0.08
1995

2015

2010

2015

96
2000

2005

2010

2015

GRC
103

0.25

97

-0.25
1995

2015

104

95
2000

ITA
106

0.06

2005

106

GBR

FRA

2000

98.5 -0.02
1995

104

FIN

103 0.025

0.06

-0.08
1995

2010

0.02

ESP

DNK
0.03

-0.04
1995

DEU
103

2005

2010

2015

NLD

0.06

103

0.04

103

-0.06
1995

96
2000

2005

2010

2015

-0.06
1995

97
2000

2005

2000

2005

2010

2015

-0.04
1995

98
2000

2005

2010

2015

SWE

PRT
0.04

-0.04
1995

2010

2015

103

0.08

96

-0.06
1995

105

96
2000

2005

2010

2015

Notes: The dashed lines denote the (quarterly) Consumer Confidence Index (CCI) available from the OECD. Since
it is published in monthly frequency, we change it to quarterly frequency by computing the quarterly arithmetic
average at every quarter. The solid lines denote the MB-CCI. OECD consumer confidence corresponds to theright y-axis and the MB-CCI to the left y-axis. For Sweden, the CCI is available only from 1995:IV (SWE).

50

Figure A7: Correlation of confidence indices and IMF financial development indicesr = 0.38

r = 0.20

0.8

FD

FM

0.8

0.8
0.6

0.6

0.6

0.4

0.4
1

0.4
0

r = 0.11

0.5

0

0.5

1

0

r = 0.09

1

FMD

1

0.5

0.5

1

0
0

r = 0.38

0.5

0

0.5

1

0.8

0.5

0.5

1

0.5

1

r = -0.20

0.7

0.6

0
0

0

r = 0.12

1

FID

1

1

0.5

0
0

0.5

r = 0.15

1

FIE

FMA

0.5

FME

0

FIA

r = 0.42

1

FI

1

0

0.5

1

0

0.5

1

Notes: The panels show the relationship between IMF financial development indices and the correlation of two
confidence indices. r reports the Pearson correlation coefficient. FD denotes the Financial Development index;
FM denotes the Financial Markets index; FI denotes the Financial Institutions index; FMA denotes the Financial
Markets Access index; FMD denotes the Financial Markets Depth index; FME denotes the Financial Markets
Efficiency index; FIA denotes the Financial Institutions Access index; FID denotes the Financial Institutions
Depth index; FIE denotes the Financial Institutions Efficiency index. For detailed description of the variables,
see Appendix B.1.

51

Figure A8: Correlation of confidence indices and economics, social, and institutional factors

-10

0

-20
0.5

1

r = -0.09

0.6
0.5
0.4
0

0.5

0

1

Gender wage gap

Household saving

0.5

r = -0.29

0Replacement ratio

1600
1400

0

0.7

Employment rate

-5

1800

1

25

0.5

r = -0.16

20
15
10
0

30

0.5

1

r = -0.08

20
10
0
0

0.5

80

1

r = -0.40

60

40

1

0

Govt financial wealth

Hours worked0

20

r = 0.43

2000

Min. income benefitsr = -0.44

Household spending

Govt deficit

5

0.5

1

r = -0.16

100
0
-100
-200

0

100

0.5

1

r = -0.07

50

0
0

0.5

1

Notes: The panels show the relationship between economics, social, and institutional factors and the correlationof two confidence indices. r reports the Pearson correlation coefficient. Govt deficit denotes general government
deficit; Hours worked denotes average annual hours worked; Govt financial wealth denotes general government
financial wealth; Replacement ratio denotes the aggregate replacement ratio; Min. income benefits denote theadequacy of minimum income benefits. For detailed description of the variables, see see Appendix B.2.

52