Confidence and the Real Economy:
The Japanese Case
Atsuo Utaka
Discussion Paper 00-10

September 2000
この研究は「大学院経済学研究科・経済学部記念事業」
基金より援助を受けた、記して感謝する。
Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, Japan

Confidence and the Real Economy-- The Japanese Case--

Atsuo Utaka *Graduate School of Economics
Osaka University
1-7 Machikaneyama, Toyonaka, Osaka
560-0043, Japan
E-mail: autaka@compsrv.econ.osaka-u.ac.jp*

I would like to express my appreciation to Professor Masatsugu Tsuji for his kind support and pertinent
advice. The author is responsible for any errors which remain.

Abstract
This paper empirically analyzes whether consumer confidence has an effect on the real
economy in Japan. We use vector autoregressions including variables which represent
consumer confidence.

We show that in the cases of quarterly and monthly data,

consumer confidence has a significant effect on GNP fluctuations, whereas in the caseof semiannual data, it has no effect. In other words, consumer confidence has an effecton only very short-term economic fluctuations.

JEL Classification Number: E32
Keywords: Consumer confidence, Business fluctuations2

1. Introduction

In this paper, we empirically analyze whether consumer confidence has an effecton the Japanese economy.
It is often said that macroeconomic performance depends on confidence. 1

Thechange of confidence, however, may precede business fluctuations because economic
agents accurately forecast future economic conditions.

Matsusaka and Sbordone(1995) showed empirically the relation between consumer confidence and GNP in the
U.S. economy. To exclude the possibility that consumers simply forecast the future
growth rate, they use vector autoregressions including such control variables as theIndex of Leading Indicators, which are useful for predicting future economic
conditions. 2 In this method, the effect of pure confidence on GNP can be investigated.
They confirm that in the U.S. economy, consumer confidence itself has a significant
effect on GNP fluctuations.
By using quarterly data, Matsusaka and Sbordone investigate the relation between
consumer confidence and GNP.

In our paper, so as to investigate the more strict

relation between confidence and the real economy, we use not only quarterly data but

1

In standard neo-classical economics, under the ordinary assumptions on production function and utility
function, a unique equilibrium is determined. Therefore, confidence has no effect on the real economy.
But, for instance, in an economy where a sunspot equilibrium exists, the stochastic natural event which
has nothing to do with the economic fundamentals, can cause economic fluctuations. In this case,confidence can affect the real economy. Azariadis (1981) and Cass and Shell (1983) analyze theexistence of a sunspot equilibrium in the overlapping generations model with money. They show that inan ordinary exchange economy , a sunspot equilibrium occurs only if the degree of relative risk aversion is
sufficiently large. But, Fukuda (1998) and Utaka (2000), for instance, analyze the models in which asunspot equilibrium can occur under the assumption that the degree of relative risk aversion is small.
2
In addition to the Index of Leading Indicators, such indexes as government spending and “default risk”
are considered control variables in Matsusaka and Sbordone (1995). They show that in cases wheresuch indexes are added, the results are almost the same as those in cases where only the Index of Leading
Indicators is considered.

3also monthly and semiannual data, and analyze the effect of confidence on GNP
fluctuations in Japan.

2. Empirical Analysis

In this section, we empirically analyze the relation between consumer confidence
and business fluctuations in Japan. First, we investigate the quarterly case to confirm
whether the results of Matsusaka and Sbordone are applicable to the Japanese economy.
The Consumer Confidence Index (CCI), which has been formulated by the Economic
Planning Agency (EPA), is supposed to represent consumer confidence. 3
investigate whether CCI has an effect on GNP.

We

We utilize vector autoregressions

including GNP and CCI, and investigate whether CCI has a significant effect on GNP
fluctuations. We estimate the following equations: GNP t   a 11 ( L) a 12 ( L )  GNP t   u GNP t 

,

 =
 +
 CCI t   a 21 ( L ) a 22 ( L)  CCI t   u CCI t (1)where a( L) denotes polynominal in the lag operator L, and u is an error term.
The results are summarized in the following table.

3

CCI consists of such elements as the expectations of overall livelihood, income growth, and willingness
to buy durable goods.

4Table 1
Vector Autoregressions with GNP and CCI

GNP

CCI

Lag=2, Quarterly data
Dependent variables
GNP
CCI
F-test: p-value
0.526
0.000
Sum of coefficients-0.049182.2**
Standard error
0.205
38.58
F-test: p-value
Sum of coefficients
Standard error

0.001
0.16E-02**
0.53E-03

0.000
0.499**
0.099

This table indicates the p-value of F-test for the
hypothesis that the block of coefficients is jointly equal to zero.
In addition, the sum of the block of coefficients and its
standard error are presented. ** denotes significant rejectionof the hypothesis that the sum of the block of coefficients isequal to zero at 1 percent.

In this paper, the lag length is chosen by minimizing AIC (Akaike Information
Criterion).
1998.

The sample period covers the second quarter of 1983 to the third quarter of

The number of observations is 62.

differences.

CCI is expressed in levels.

GNP is expressed in terms of log
Each column is a regression and thedependent variable is shown at the top of each column. In this table, we first report thep-value of F-test for the hypothesis that the block of coefficients is jointly equal to zero.
In addition, we present the sum of the block of coefficients and its standard error. 4**

denotes significant rejection of the hypothesis that the sum of the block of coefficients isequal to zero at 1 percent.
4

All equations in our estimation include a constant term.

5

We are specifically interested in the effect of CCI on GNP. In the GNP equation,
the hypothesis that the block of CCI coefficients is jointly equal to zero is rejected at
almost the zero percent level. In other words, there exists Granger Causality fromconsumer confidence to GNP. Moreover, the sum of CCI coefficients is positive andthe hypothesis that it is equal to zero is rejected at the smaller than 1 percent level.
Table 1 also presents the results of the CCI equation. It is shown that GNP has asignificant effect on CCI.
Next, to investigate whether confidence itself has an effect on GNP, we use vector
autoregressions with GNP, CCI, and the control variable which is useful for forecasting
future economic conditions, as indicated by Matsusaka and Sbordone (1995). For thisvariable, we consider the Index of Business Conditions: Composite Index of Leading
Index (IBCL), which is also formulated by the EPA. 5
We estimate the following equations: GNP t   a 11 ( L) a 12 ( L ) a 13 ( L)   GNP t   u GNP t 



 
 
.
IBCL
=
a
L
a
L
a
L
IBCL
+
u
(
)
(
)
(
)
t


t

IBCL




21
22
23
t 

 

 


 CCI t   a 31 ( L) a 32 ( L) a 33 ( L )  CCI t   u CCI t (2)

The results of this estimation are denoted in the following table.

5

IBCL is calculated based on eleven statistical data such as money supply (M2+CD) and new orders formachinery, whose movements tend to precede those in business conditions.

6

Table 2
Vector Autoregressions with GNP, IBCL, and CCI

GNP

Lag=2, Quarterly data
Dependent variables
GNP
IBCL
CCI
F-test: p-value
0.182
0.416
0.001
Sum of coefficients-0.2310.508
160.8**
Standard error
0.207
0.381
41.95

IBCL

F-test: p-value
Sum of coefficients
Standard error

CCI

F-test: p-value
Sum of coefficients
Standard error

0.007
0.105*
0.045

0.000
0.716**
0.083

0.000
0.053
0.21E-02** -0.23E-02*
0.51E-03
0.94E-03

0.426
12.05
9.16
0.000
0.525**
0.104

This table indicates the p-value of F-test for the hypothesis that theblock of coefficients is jointly equal to zero. In addition, the sum of theblock of coefficients and its standard error are presented. * and ** denotesignificant rejection of the hypothesis that the sum of the block ofcoefficients is equal to zero at 5 percent and 1 percent, respectively.

This estimation also covers the second quarter of 1983 to the third quarter of 1998,
and the number of observations is 62. We express IBCL in terms of log differences.
* and ** denote significant rejection at 5 percent and 1 percent, respectively. From the
GNP equation, it is shown that even after adding IBCL to the system, there exists
Granger Causality from CCI to GNP. 6 The sum of CCI coefficients is positive and the
hypothesis that it is equal to zero is rejected at the smaller than 1 percent level. In

6

From the GNP equation, it is also shown that IBCL precedes GNP, namely, IBCL is useful for predicting
GNP movements.

7

addition, the CCI equation indicates that GNP has a significant effect on CCI. 7
Next, let us investigate the quantitative effect of consumer confidence on GNP byusing the forecast variance decomposition.

Table 3
Forecast Variance Decompositions of GNP and CCI

Quarter148

Dependent variable: GNP
GNP
IBCL
CCI
100.00
0.00
0.00
74.18
15.98
9.83
70.52
20.19
9.29

Quarter148

Dependent variable: CCI
GNP
IBCL
CCI
1.33
2.24
96.43
29.33
15.16
55.52
30.34
28.61
41.05

Quarter
1
4
8

IBCL
1.23
17.14
22.01

CCI
0.98
10.44
9.97

GNP
97.79
72.42
68.02

Quarter
1
4
8

IBCL
2.61
19.50
34.47

CCI
97.39
60.12
44.94

GNP
0.00
20.38
20.60

Quarter1
4
8GNP
100.00
74.18
70.52

CCI
0.00
13.56
13.13

IBCL
0.00
12.26
16.35

Quarter
1
4
8

GNP
1.33
29.33
30.34

CCI
98.67
60.20
46.09

IBCL
0.00
10.48
23.57

Quarter
1
4
8

CCI
1.33
14.72
14.50

GNP
98.67
73.02
69.15

IBCL
0.00
12.26
16.35

Quarter148CCI
100.00
66.57
51.92

GNP
0.00
22.95
24.51

IBCL
0.00
10.48
23.57

This table reports the percentage of forecast variance of dependent variables(especially GNP and CCI) which can be explained by GNP, IBCL, and CCI from one to7

As Matsusaka and Sbordone (1995), we also investigated the block exogeneity of GNP and IBCL withrespect to CCI by using χ 2 statistic. The hypothesis that GNP and IBCL are block exogenous isrejected at almost the zero percent level.

8eight quarters ahead. It is known that percentage contributions are affected by theordering of variables, so we investigate the variance decomposition in the four ways ofordering.
From GNP decompositions, it is shown that CCI explains about 10 percent to 15
percent of the forecast variance of GNP eight quarters ahead.

Matsusaka and

Sbordone (1995) show that in the U.S. economy, the variance of CCI explains 13
percent to 26 percent of forecast variance of GNP eight quarters ahead. Therefore, it isshown that in Japan, the quantitative effect of consumer confidence on GNP fluctuations
is smaller than that in the U.S. economy. CCI decompositions show that CCI explains
about 41 percent to 52 percent of its own innovation variance eight quarters ahead.
CCI can be said to be exogenous to this system.
Next, we investigate the more short-term effect of confidence on the real
economy by using monthly data. In this case, we substitute the Index of Industrial
Production (IIP) for GNP. 8

As the index which shows consumer confidence, weconstruct an original diffusion index (DI) from the survey on economic conditions
carried out by Jiji Press in the Public Opinion Poll Monthly compiled by the Prime
Minister's Office. 9 In this case, too, we use vector autoregressions not only with IIP
and DI, but also with IBCL. The results are presented in the following table.

8

IIP is a component of Coincident Index in the Index of Business Conditions.
The answer is chosen from among: (ⅰ) economic conditions have surely improved; (ⅱ) they haveslightly improved; (ⅲ) they are unchanged; (ⅳ) they have slightly worsened; and (ⅴ) they have surely
9worsened. We define DI as the difference between the percentage share of the respondents choosing(ⅰ) or (ⅱ) minus those choosing (ⅳ) or (ⅴ).

9Table 4
Vector Autoregressions with IIP and DI

IIP

DI

Lag=3, Monthly data
Dependent variables
IIP
DI
F-test: p-value
0.000
0.079
Sum of coefficients-0.434*
199.3*
Standard error
0.184
78.27
F-test: p-value
Sum of coefficients
Standard error

0.001
0.19E-03**
0.68E-04

0.000
0.905**
0.029

This table indicates the p-value of F-test for the hypothesis thatthe block of coefficients is jointly equal to zero. In addition, the sumof the block of coefficients and its standard error are presented. *and ** denote significant rejection of the hypothesis that the sum of
the block of coefficients is equal to zero at 5 percent and 1 percent,respectively.

Table 5Vector Autoregressions with IIP, IBCL, and DI

IIP

Lag=4, Monthly data
Dependent variables
IIP
IBCL
DI
F-test: p-value
0.000
0.226
0.863
Sum of coefficients-0.992**-0.097
119.0
Standard error
0.254
0.088
124.5

IBCL

F-test: p-value
Sum of coefficients
Standard error

0.000
0.889**
0.143

0.000
0.933**
0.050

0.337
123.8+
70.18

DI

F-test: p-value
Sum of coefficients
Standard error

0.000
0.31E-03**
0.70E-04

0.046-0.48E-04+
0.24E-04

0.000
0.914**
0.034

This table indicates the p-value of F-test for the hypothesis that the blockof coefficients is jointly equal to zero. In addition, the sum of the block ofcoefficients and its standard error are presented. +, *, and ** denotesignificant rejection of the hypothesis that the sum of the block of coefficients isequal to zero at 10 percent, 5 percent, and 1 percent, respectively.

10

IIP and IBCL are expressed in terms of log difference and DI in terms of levels. The
sample period is from April 1983 to September 1998, which is the same as that in thequarterly case. The number of observations is 186. +, *, and ** denote significant
rejection at 10 percent, 5 percent, and 1 percent, respectively.
In both models, it is shown that in the IIP equation, there exists Granger Causality
from DI to IIP. Moreover, the sum of DI coefficients is positive and has a significant
effect on GNP. It is shown that also in the monthly case, confidence itself has asignificant effect on the real economy.
Next, let us investigate the forecast variance decomposition. Table 6 reports thepercentage of IIP and DI forecast variance which can be explained by IIP, IBCL, and DI
from one to twenty-four months ahead (in other words, eight quarters ahead).

We

report the results of the variance decomposition in four ways of ordering.
From IIP decompositions, it is shown that DI explains about 4 percent to 5
percent of IIP variance twenty-four months ahead. In the monthly case, its quantitative
effect is considerably less than that in the quarterly case. DI decompositions show that
DI explains about 60 percent to 65 percent of its own innovation variance twenty fourmonths ahead, in other words, DI is to a large extent exogenous to this system.

11

Table 6
Forecast Variance Decompositions of IIP and DI

Month
1
6
12
18
24

Dependent variable: IIP
IIP
IBCL
DI
100.00
0.00
0.00
83.68
12.55
3.77
81.86
14.27
3.87
81.17
14.61
4.22
80.72
14.54
4.74

Month
1
6
12
18
24

Dependent variable: DI
IIP
IBCL
DI
0.29
0.27
99.43
0.94
8.78
90.28
0.70
22.17
77.13
1.06
30.05
68.90
1.45
31.26
67.29

Month
1
6
12
18
24

IBCL
1.37
17.57
19.27
19.60
19.51

DI
0.35
4.21
4.28
4.64
5.18

IIP
98.27
78.22
76.45
75.76
75.32

Month
1
6
12
18
24

IBCL
0.21
8.08
21.57
29.87
31.31

DI
99.79
91.33
77.98
69.64
68.06

IIP
0.00
0.59
0.45
0.49
0.63

Month
1
6
12
18
24

IIP
100.00
83.68
81.86
81.17
80.72

DI
0.00
3.58
3.69
4.01
4.53

IBCL
0.00
12.75
14.44
14.82
14.75

Month
1
6
12
18
24

IIP
0.29
0.94
0.70
1.06
1.45

DI
99.71
92.56
80.34
71.97
70.01

IBCL
0.00
6.50
18.96
26.98
28.54

Month
1
6
12
18
24

DI
0.29
3.90
3.98
4.31
4.85

IIP
99.71
83.36
81.57
80.87
80.40

IBCL
0.00
12.75
14.44
14.82
14.75

Month
1
6
12
18
24

DI
100.00
93.25
80.72
72.26
70.37

IIP
0.00
0.26
0.31
0.76
1.09

IBCL
0.00
6.50
18.96
26.98
28.54

Finally, we investigate the effect of consumer confidence on GNP by using
semiannual data. We construct the semiannual data by averaging quarterly data. The
results of the estimation are presented as follows.

12

Table 7
Vector Autoregressions with GNP and CCI

GNP
CCI

Lag=1, Semiannual data
Dependent variables
GNP
CCI
+
Coefficient
0.463
94.39+
Standard error
0.257
49.24
CoefficientStandard error

0.13E-02
0.11E-02

0.408+
0.212

This table indicates the coefficient and its standard error.
+ denotes significant rejection of the hypothesis that thecoefficient is equal to zero at 10 percent. In this estimation,
since the number of lag is one, the p-value of F-test is identicalto the p-value of the significance of the respective coefficients.

The sample period covers the latter half of 1983 to the first half of 1998. The numberof observations is 30. + denotes significant rejection at 10 percent. In this estimation,
since the number of lag is one, the p-value of F-test is identical to the p-value of thesignificance of the respective coefficients.
In the GNP equation, it is shown that the CCI coefficient is positive, but has no
significant effect on GNP. On the other hand, also in this case, GNP has an effect on
CCI.

13

3. Conclusion

We have empirically analyzed whether consumer confidence has an effect on
business fluctuations in Japan. It is shown that in the cases of quarterly and monthly
data, consumer confidence has a significant effect on economic fluctuations, whereas in
the case of semiannual data, it has no effect. Namely, confidence itself has an effect onvery short-term economic fluctuations, but in the long term, it does not affect the real
economy, as maintained by standard neo-classical economics.
The estimation process of our paper, however, cannot completely exclude thepossibility that there exists a certain variable that makes movements in confidence
precede those in the real economy, as previously is referred to by Matsusaka and
Sbordone (1995). Therefore, by grasping the purer confidence, we may be able toinvestigate the more precise effect of consumer confidence on the real economy.

14

References

Azariadis, C. (1981) Self-fulfilling Prophecies, Journal of Economic Theory 25, 380396.
Cass, D. and K. Shell (1983) Do Sunspots Matter? Journal of Political Economy 91,
193-227.
Fukuda, S. (1998) Sunspot Equilibria and Periodic Cycles under Idiosyncratic
Uncertainty, Journal of Economic Behavior and Organization 36, 127-138.
Matsusaka, J. and A. Sbordone (1995) Consumer Confidence and Economic Fluctuation,
Economic Inquiry 33, 296-318.
Jiji Press (1982-1998) The Survey on Economic Conditions in Gekkan Seron Chosa(Public Opinion Poll Monthly), Prime Minister's Office.
Utaka, A. (2000) Endogenous Price Fluctuations in an Economy with Public Goods,
Osaka Economic Papers 49, 321-331.

15