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3-Vu TuanKhai

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Examining the Possibility of Introducing a Common
Currency for ASEAN
─An empirical analysis of symmetry of shocks
Vu Tuan Khai
Abstract
This paper examines the possibility of introducing a common currency for nine ASEAN countries by analyzing
symmetry of shocks between these countries using the structural VAR method developed by Blanchard and Quah (1989),
and data on CPI and GDP. Two new contributions of the paper are the inclusion of four ASEAN countries (Vietnam,
Laos, Cambodia and Myanmar) in the analysis, and the emphasis on the consistency in the signs of responses of GDP
and CPI across the countries to structural shocks. The results imply that ASEAN as a whole does not form an optimum
currency area, but a group of Indonesia, Malaysia, Singapore, Thailand and the Philippines with high correlations
between structural shocks and fast speed of adjustment to shocks, is suitable for a common currency. The results also
show that the signs of responses to the same shock are not necessarily consistent across the countries, implying a
problem on the validity of the Blanchard-Quah VAR method in analyzing the symmetry of shocks.
Keyword: Structural VAR, Long run restriction, Symmetry of Shocks, Common Currency, ASEAN.
JEL Classification: E32, F33, F41.
1.Introduction
Recently, economic integration is proceeding at a fast pace in the Association of Southeast Asian Nations (ASEAN)
countries1). Regional cooperation has become very active and is strongly recognized by the governments in ASEAN to
further accelerate economic integration. At the latest annual summit in Vientiane, November 2004, leaders of ASEAN
countries reaffirmed ASEANʼs commitment to speed up the integration towards the ASEAN Economic Community
that they agreed to establish by 20202). On the other hand, after the Asian crisis, the vulnerability of the dollar-peg
system became clear and a more appropriate exchange rate regime is becoming a matter under consideration. With the
successful launch of the euro, introducing a common currency is emerging as an interesting option for ASEAN.
One criterion to judge if countries are suitable to form a common currency area (CCA) is provided by the concept
of symmetry of shocks3) which focuses on the cost aspect of forming a CCA. The argument is that the more symmetric
are shocks across a group of countries, the less costs they will have to pay when forming a CCA. The reasoning is as
follows. When countries form a CCA, they abandon their monetary policy autonomy, in other words they can no longer
adopt different monetary policies. If shocks occurring in these countries are symmetric, they can implement a common
monetary policy, for example changing money supply or interest rates. In contrast, if shocks are asymmetric a common
monetary policy cannot be adopted, and there is a need to take other policies in an asymmetric way among them4). In
this case the adjustment costs are much higher5).
横浜国際社会科学研究
28 (28)
第 13 巻第 1・2 号(2008 年 8 月)
Among the existing studies about the symmetry of shocks, there are some papers applying the structural
VAR (SVAR) methodology developed by Blanchard and Quah (1989) to calculate structural shocks. Bayoumi and
Eichengreen (1993) used two-variable SVAR and annual data to study shocks of ten European countries and compared
with regions in the US. Bayoumi and Eichengreen (1994) extended the study above for many parts in the world,
including five ASEAN countries6). Using three-variable SVAR, Zhang et al. (2004) studied the feasibility of a monetary
union for a group of ten East Asian countries which also contains ASEAN-5 countries. In these papers, however, the
study about other ASEAN countries such as Vietnam, Laos, Cambodia and Myanmar (the VLCM group) is still left
untouched even though their existence is non-negligible in ASEAN7).
Table A1 in the Appendix shows some recent socio-economic indicators of ASEAN including the four VLCM
countries. We can see that, while these four countries have a share less than 10% of the total ASEAN in GDP, they
account for about one third of ASEAN population, which implies a sizable market. All of them have a ratio of trade
to GDP greater than 50%, which is larger than that of the US and Japan, and on average close to that of Korea. This
demonstrates their high degrees of openness. In addition, their growth rates have been quite high for the last five years
with an average of 7.1% (3% higher than the ASEAN-5 group). All of these facts suggest the potential of the four
countries, and now as they have started taking part in the dynamism of economic growth and integration in the region,
it can be expected that they will play a more significant role in ASEAN in a near future. Hence, it can be argued that
studying these countries has an important meaning.
The purpose of this paper, thus, is to incorporate these countries in an analysis to examine whether ASEAN as a
whole is suitable to introduce a common currency based on the criterion of symmetry of shocks. The paper uses the
SVAR methodology developed by Blanchard and Quah (1989), and data on CPI and GDP. With this method, demand
and supply shocks are identified by imposing the so-called long-run restriction which means that in the long run a
demand shock has no effect on real GDP. Once structural shocks are identified, correlations of these shocks between
countries are calculated and used as a proxy for shock symmetry.
This paper also attempts to make another contribution by pointing out the importance of the consistency in the
signs of responses of economic variables to shocks between the countries, which has received little attention in the
literature so far. The argument is that the correlations can be used as a proxy for symmetry of shocks only when the
signs of responses are consistent between the countries. We analyze this by using the impulse response functions in the
VAR model.
The remainder of the paper is organized as follows. Section 2 explains the model, Sections 3 and 4 describe the
data and estimation, respectively. Section 5 discusses the estimation results. The last section concludes.
2.The model
2.1
Structural model
Assume that the inflation rate and growth rate in each country are subject to two types of structural shocks as
follows
xt
A0 A1H t A2H t 1 ...
A0 ( A1 L0 A2 L1 ...)H t
A0 A( L)H t
(
A0 ( A1 L0 A2 L1 ...)H t A0 A( L)H t
(1),
t denotes
t), xt ' log cpit (1),
' log gdpt c , and
x A A H A H ... where
A0 ( A
L0 A L1time
...)H(year
A0 tor
A(quarter
L)H t
(1),
xxt t AA00AA1H1Ht tAA22HHt t 11......t AA000((AA111LL0t0AA222LL1t11...)
...)HHt t A
A00A1A((LL)H)Ht t 2 (1),
(1), t
0
1
A1H(year
... A0 t),
( A1 L
...)H t ' log
A0 gdp
A( Lc)H, t and(1),
xt A'2 L
logcpi
H t H dt H st isc the structural shock vector consisting of demand c shock ( H dt ) and suppl
0time
t A2Ht t or
1 quarter
t
t
H
H st
where t denotes time (year t or quarter t), xt c c ' log cpit ' log gdpc ct c , and H t
t tdenotes
denotes
time
(year
quarter
tt),
), xxt t ''log
logcpi
cpit t ''log
loggdp
gdpt t ,,,and
and HHt t HHdtdt HHstst is the structuraldt shock
where
where
where
time(year
(yeart tor
orquarter
quartert),
and
vector
t denotestime
tor
c
c
0 H shock
1H
ltime
shock
vector
of
demand
shock
(
H
)
and
supply
(
).
(year
t or consisting
quarter
t
),
x
'
log
cpi
'
log
gdp
,
and
H
x
A
A
H
A
H
...
A
(
A
L
A
L
...)
H
A
A
(
L
)
H
(1),
ofofconstant
Ai are
2×2( Hcoefficient
matrices. A( L
consistingt oft demand
supply
vector
constant
terms,
and
are
2⊗2
coefficient
t2 t 1 ( dt ) and
t0
t shock
dt ( sttst).A0 0 isisaavector
0
1 t isshock
1
2
t
the
structural
shock
vector
consisting of demand
shock
( H terms,
) and and
supply
shock
st ).
isisthe
thestructural
structuralshock
shockvector
vectorconsisting
consistingofofdemand
demandshock
shock((HHdtdt))and
andsupply
shock
shock(dt
(HHstst).).
matrices.
cansupply
be written
in matrix
form as follows
A(L) is a matrix of polynomials in the lag operator, which
lag operator
which can be written in matrix form as follows
vector
consisting
shock
( Hmatrices.
( H st 'of
).log
c andpolynomials,
f shock
constant
terms,
and
Ai of
aredemand
2×2 coefficient
is
acpi
matrix
dt ) and supply
t denotes
time
(year
t or
xAt ( L) 'shock
log
H t H dt matrices.
H st c
where
t and
t ,2×2
A0 is
a quarter
vector oft),constant
terms,
Aigdp
are
coefficient
A( L) is a matrix
AA00 isisaavector
vectorofofconstant
constant
terms,
terms,and
andAAi i are
are2×2
2×2coefficient
coefficientmatrices.
matrices. AA((LL)) isisaamatrix
matrix
f
§ f a (i ) Li 1
a (i ) Li 1 ·
olynomials,
which
can
be
written
in
matrix
form
as
follows
11
¨ asi follows
¸
1
i 1 12
constant terms,
Ai are 2×2
coefficient
matrices.
L) is awhich
matrix
is theand
structural
shock
vector
consisting
ofA(demand
shockcan
( H dtbe
) and
supply
shock
(form
H st ).
of
lag operator
polynomials,
written
in matrix
(2).
A
(
L
)
¨ f
ofoflag
lagoperator
operatorpolynomials,
polynomials,which
whichcan
canbe
bewritten
writtenin
inmatrix
matrixform
formas
asfollows
follows
f
i 1
i 1 ¸
f
¨
¸
a
(
i
)
L
a
(
i
)
L
i 1 ·
§ f a (i ) Li 1
21
22
a
(
i
)
L
f
f
i
1
i
1
©
¹
11 be written
12 matrix
olynomials, which
form
¨ i 1can
i 1in
f as follows
ff §
a (i ) Li 1
a (i ) Li 1 ·
§¸§ fand
(2).
A( L) A
of constant
terms,
coefficient
¸
aa11A
(i(i)i )Lare
Li i 11 2×2
aa¨12(i()i )LiLi 1i 11·11·matrices. iA1( L12) is a matrix
¨ 0 isf a vector
f
i 1
i 1¨¸¨
(2).
A
(
L
¸
¸
i i 11 11
i )i 11 12
f
f
¨
¸
¨ f
¸
a22AA(i(L)LL
f
(2).
§© i 1aa21((ii))LLi 1
1
i(2).
i 1structural
i 1a12
¸21¸ (i )assume
(i ) L)i)1 ·¨¹¨ ff
We
that
the
shocks H dt and H st have means equal
ff ¨
¸
a
L
a
(
i
)
L
11
i
i
1
1
i
i
1
1
22
¨ lag
¸¨¨
i 1operator polynomials,
i 1
¸
¸
of
which
can
be
written
in
matrix
form
as
follows
i
1
i
1
a
a
(
i
(
)
i
)
L
L
a
a
(
i
(
)
i
)
L
L
©
¹
A( L) ¨
i 11 2121
i i 11 2222
©© i (2).
¹¹
f
f
i 1
i 1 ¸
¨
¸
a21 (i ) L H anda22H(i ) Lhave
variances
equal to unity. In addition, they are mutually independent,
f zero iand
t the structural
to
1
i 1
© i 1shocks
§¹ fmeans
·
dt
st
(i ) Liequal
a12 (i ) L 1shocks
We assumea11
that
the structural
H and H have means equal to zero and
A0 A1H t A2H t 1 ...
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
' log cpit ' log gdpt c , and H t
H dt H st c
where t denotes time (year t or quarter t), xt
' log cpit ' log gdpt c , and H t
H dt H st c
where t denotes time (year t or quarter t), xt
is the structural shock vector consisting of demand shock ( H dt ) and supply shock ( H st ).
is the structural shock vector consisting of demand shock ( H dt ) and supply shock ( H st ).
A0 is a vector of constant terms, and Ai are 2×2 coefficient matrices. A( L) is a matrix
A0 is a vector
of constant
terms, and
Ai are 2×2
coefficient
matrices. ATuan
( L) is
a matrix
(29)
29
Examining
the Possibility
of Introducing
a Common
Currency
for ASEAN(Vu
Khai)
of lag operator polynomials, which can be written in matrix form as follows
of lag operator polynomials, which can be written in matrix form as follows
f
§ f a (i ) Li 1
a (i ) Li 1 ·
0
1
i 1
¨
i 1 f11
i 1 f12
§
xt A0 A1H t A2H t 1 ... A0 ( A1 L A2 L ...)H t AA0 (L)A( L)H t
(1),
a11 (i ) L
a (i ) Li¸1 · (2).
¨ ¨ i1
f i 1 12 i 1 ¸ ¸
(2).
(2).
A( L) ¨ ¨ f a (i ) Li 1
a
(i ) L ¸
© ¨ i 1 f 21a (i ) Li 1 i 1 f 22a (i ) Li¹1 ¸¸
0
21
22
c
c
A2H t 1 time
... A0(year
( A1 L
A2 L1 ...)tH),t xA0 'Alog
( L)Hcpi
(1),
i
1
i
1
©
¹
enotes
t or quarter
'
log
gdp
,
and
H
H
H
t t
t
t
t
dt
st
¦
¦
¦
¦
¦
¦
¦
¦
We assume that the structural shocks H dt and H st have means equal to zero and
c
We
assume
that
H st and
have means equal to zero and
uctural
shock tvector
consisting
of
demand
(t H dt structural
)Hand
andH stsupply
shock Hequal
(dtH st and
). to zero
ar
t or quarter
),Wextassume
' logthat
cpit the
' log
gdpt c , shock
and Hthe
structural
shocks
haveshocks
means
variances equal to unity. In addition, they
dt
variances equal to unity. In addition, they are mutually independent, and serially
are mutually independent,
and
serially
uncorrelated
at
all
leads
and
lags.
With
these
assumptionsand
we can
write
variances equal to unity. In addition, they are mutually independent,
serially
uncorrelated
atsupply
all leads
and(lags.
assumptions we can write
ector
demand
( H2×2
shock
H st ).A(With
ector consisting
of constantofterms,
andshock
Ai are
coefficient
matrices.
L) isthese
a matrix
dt ) and
uncorrelated at all leads and lags. With these assumptions we can write
§ § 0· §1 0··
rator
polynomials,
which
can
be
written
in matrix
as follows
H t 㨪 ¨¨ ¨ § §¸0; ¨· § 1 ¸0¸¸ · · (3).
(3).
t terms, and Ai are 2×2 coefficient
matrices.
A( L) form
is a matrix
Ht 㨪
(3).
© © ¨¨0¨¹ ©¸0; ¨ 1 ¹ ¹ ¸ ¸¸
f
0¹ ©0 1 ¹¹
i 1 ·
©
§ f a (i ) Li 1
©
a (i ) L
ls, which can be written
¨ i 1in11matrix formi 1as12follows¸
(2).
A( L) ¨
1
f
f
x
Bi 11xt¸¸1 B2 xt 2 ... B p xt p et B0* > I B( L) L @ et B0* C ( L)et
(4).
t aB0(i) L
¨ f a21 (i )iLi11·
i 1
§ f a (i ) L2.2
1
22
*
*
Estimated
model
i
1
i
1
©
¹
a12 (i ) L
11
x
B
B
x
B
x
...
B
x
e
B
I
B
(
L
)
L
e
B
C
(
L
)
e
(4).
1
>
@
*
*
¨ i1
i 1
t
0
1 t 1
2 t 2
p t p
1 t
xt ¸B0 tB
xt ...
B t > I B0B(*L) L
)0et* C(4).
(2).
L) ¨
B
1 0BB
2 xtxt 21*...
p xt1 xtis
B2 xBt p2bivariate
eBt p xVAR
[email protected] 1eBt ( L)[email protected]*eCt ( LB
( L)et
(4).
¸Bmodel
0 as >follows
t 0p * e
t
f
f estimated
The
areduced-form
model
i 1
are
xit1Here
B21xtB
B vectors
*B
( L) L @ elements,
et B01 C (BLi)*0et( i 1,...,
(4). p ) are 2×2
¨
¸ 0 BB
0t 1and
0 > Iconstant
1x
20 ...
p xt p et of Btwo
a
(
i
)
L
a
(
i
)
L
21
22
B
B
x
B
x
B * > I B( L) L
me ©thati 1the structural
means
equal
to
zero
and
i 1 shocks ¹H dt xandB Hst B have
1 > I *B ( L ) L @ ext
1 ... B p* xt p et
*
x
B
x
...
B
x
e
B
B
C
(
L
)
e
(4).
0
1
t
1
2
t
*
*
0
t e B(4).
0 ...
1
1 xare
2 t 2vectorsBpof
tIB
Bt B
p ()L)are
xt B0 BHere
xt 1 tB20xt and
ptwo
Bt (1Lt )constant
[email protected] B...0elements,
BCp x( L
I tB
L @2 e2×2
B0 C ( L)et 0(4).
> are
i B(0 i 1,...,
0 0 >
t
t
p* t p etofxt B
1x
t )pe
t 1,...,
and BB0*2and
are B0vectors
two
constant
elements,
B
(
i
p
)
2×2
Here B10Here
i
are vectors of two constant
elements,
Bi ( i 1,..., p ) are 2×2
0 * matrices,
0
c is* the vector
egdp
In
coefficient
and
and
B0mutually
ofet ...two
constant
(1ieerror
1,...,
2×2addition,
Here Bthey
suctural
equal to
unity.H In and
addition,
are
serially
1(ecpi and
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are
BB1 x**t vectors
>independent,
BHere
( LB)iL @of
B0*pterms.
)CBare
( L* )eare
(4).
(4).
t
1to
t (2Land
p xtB
p* t ( L )Be0 > I(4).
xshocks
B0 B1dtxt 1 B2 xHt st 2 have
...0 *Bmeans
xBxt t p IBzero
2 xBvectors
) L @ eBof
* eC
Bt0Bi and
vectors
and
B
are
two
constant
elements,
( terms.
i 1,...,
are
2×2 of two constant eleme
Here
0
0
t
t
t
p
t
0 p
c
0
0
econstant
is the
vector
of
error
In) addition,
matrices,
and
Bcoefficient
andcoefficient
B0 matrices,
are vectors
of
two
and
B
vectors
B
(
i
of
1,...,
two
p
)
constant
are
2×2
elements,
Bi ( i 1,..., p ) are 2×2
Herelags.
Here
t B0(ecpi
,tc eelements,
gdp
,t ) are
ated at all leads and
these
assumptions
we
can
write
0 With
i
0
et and
(ecpi ,of
egdp
the)in
vector
ofvector
error ofterms.
In
addition,
andmatrices
B( L) , C ( L) matrices,
are
the
lag
operator,
which
can
be
written
as
c
t et polynomials
t ) ,t isegdp
(e,cpi
is
the
error
terms.
In
addition,
coefficient
,t
c is the
*e
y. In addition, they *arecoefficient
mutually
independent,
(serially
ecpi
egdp ,t )of
vector
of
error terms.
matrices,
and
t are
,t polynomials
and
B0and
two
constant
elements,
Bi matrices,
(In
i addition,
1,...,
) are
Here
* Bfollows
, of
C·(BL
are
matrices
ofevectors
the
operator,
which
can
beInpwritten
as
0) matrices,
§)Bvectors
·matrices
0
e 2×2
(ecpi ,t egdp ,t )c is the vector
coefficient
and
c 1,...,
§,00
·(are
§()L1)vectors
B0B0 and
are
two
constant
elements,
BB
(( ,it lag
p lag
are
2×2
Here B0 and
Here
of
two
constant
elements,
))lag
are
2⊗2
coefficient
matrices,
and
(
e
)in
is
the
vector
of error
terms.
addition,
coefficient
and
ie
B
(
L
C
L
are
of
polynomials
in
the
which
can
written
as
i
t
cpi
,
t
gdp
c
c be
L) ,can
C
the
operator,
canvector
be written
as terms. In addition,
and lags. With these
assumptions
H t 㨪 ¨¨matrices,
;B¨ (we
(3).
eare
(ematrices
egdp ,tof
) polynomials
is the
vectorinand
ofoperator,
error
et (terms.
ecpi
egdp
In
)addition,
is the
of t error
coefficient
and
matrices,
¸¸( L)write
¨
¸
¸
t
cpi ,t coefficient
,t
,twhich
follows
B( L©)of
,
C
(
L
)
are
matrices
of
polynomials
in
the
lag
operator,
which
can
be
written
as
0
0
1
is the vector
error
terms.
In
addition,
and
are
matrices
of
polynomials
in
the
lag
operator,
which
can be
B(L)
© ¹ ©
¹¹
p
follows
1
ithe
i)1, C
§eC(L)p eb (in
·which
B
() L
( L) are
matrices
of polynomials
in the lag opera
follows
B
(
L
)
,
C
(
L
)
are
matrices
of
polynomials
lag
operator,
can
be
written
as
c
i
)
L
b
(
i
L
e
(
)
is
the
vector
of
error
terms.
In
addition,
coefficient
matrices,
and
11
12can be¸ written
§ § 0matrices,
c
t (the
cpi
,t i 1operator,
gdp
,t
· §B1( L)0, ·C·(follows
L) are
matrices
of
polynomials
B
(
L
)
in
,
C
L
)
lag
are
matrices
of
which
polynomials
in
the
lag
as
operator,
which
can
be written as
e
(
e
e
)
is
the
vector
of
error
terms.
In
addition,
coefficient
and
¨
1
i
p
p
t
cpi ,t
gdp ,t
as
follows
1
1
i
i
§
·
,
B
(
L
)
H t 㨪 ¨¨ ¨ ¸written
;¨
(3).
¸
¸¸
p
b (i) Lp ii11
) L ii11 ¸
follows
¨
pb12 (ifollows
§
i i p11 ·
follows
b11¨ §(¨i) Lii p1p1 11
(i) Llag
© 0 ¹ follows
©0 1 ¹¹
B( L) , C ( L) are
matrices
the
b21((can
boperator,
L ¸ ·¸ , which can be written as
ii))iin
LL
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(8).
or
a
(
i
)
0
(8).
i 1 A( L ) ¨
0
*
¸
¸
0
*
21
21
©
¹
i 1
From (4),From
(5), and
have
0(6)*we
0 *¹
(4), (6)
(5),we
and
have i ©1§ * ¹* ·
©
¹
©
f
* · (6) we fhave
§ * and
From (4), (5),
a (i ) 0
(8).
¨
¸ or
A( LFrom
) ¨ (4),¸(5),
or and (6)a21we
(i ) have
0A( L) (8).
i 1 21
From (4), (5), and (6) we have
*¹
i 1
© 0 and
* ¹ have
From (4), (5), and (6)
From (4), (5),
(6) we have
© 0 we
4
4
From (4), (5), and (6) we haveFrom (4), (5), and (6) we have
¦
¦
¦
¦¦
¦
¦
¦
¦
¦
¦
¦
¦
¦ ¦¦
¦¦
¦ ¦
¦ ¦
¦
¦¦
¦¦
¦
¦
¦
¦
¦ ¦
¦
¦¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦ ¦¦
¦ ¦¦ ¦
¦
¦¦
¦
¦¦ ¦
¦
¦
¦ ¦¦¦
¦¦ ¦
¦¦ ¦¦ ¦¦
¦
¦
¦ ¦ ¦
¦¦
¦ ¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
¦
while D contains four unknown elements, we need to impose one more condition to
identify D . The one we impose here is a long-run restriction, which says that in the
long run real GDP is not affected by the demand shock ( H dt ). This means that the
30
cumulative effect of a demand shock on GDP level at infinity is zero. This cumulative
effect is the
sum of the effects of the
demand
shock
on the年
first-difference
of GDP (i.e.
横浜国際社会科学研究
第 13
巻第 1・2
号(2008
8 月)
growth rate) in each period. Thus, from the structural model, it must be the case that
(30)
§ * *·
A( L) ¨
¸ or
© 0 *¹
¦
f
a (i )
i 1 21
0
(8).
(8).
From (4), (5), and (6) we have
From (4), (5), and (6) we have
B0* C ( L)( DH t )
xt
Note that (9)xis
t
B0* C ( Lform
)( DH tof
)
another
B0* D4H t (C1 L1 C2 L2 ...) DH t
(9).
1
2
B0*structural
DH t (C1 L
C2 L(1).
...)
DH t
(9).
the
model
Compare
(1)
Note that (9) is another form of the structural model (1). Compare (1) and (9) to have
(9).
and (9) to have
Note that (9) is another form of the structural
a12 (1)(1).
§ a11 (1) model
· Compare (1) and (9) to have
2
xt B0* C ( L)( DDH t ) A1B0*¨ a D(1)
H t a(C1(1)
L1 ¸ C2 L(10).
...) DH t
(9).
§ a11©(1)21 a12 (1)22· ¹
(10).
D A1 ¨
¸
Note
that
another
of the ©in
structural
model
(1).
Comparecan
(1) and
(9) to have
(1) the
(1)
a21(5),
a22long
D (9)
in is
(10)
and Cform
( L) given
restriction
be written
as
With
¹ run
(10).
f
a11ª(1)long
a f (1) · restriction
º a (1) can
written as
With D in (10) and ªC ( L) bgiven
(i ) Li ºina (5),
(1)§ the
1 12run
b (i) Li(10).
0 be(8’).
¬« i 1 21 D ¼»A111 ¨ a ¬«(1) a i 1(1)22¸
¼» 21
21
22
©
¹
f
f
With D in (10) and C(L) given in (5),ª the long
be written
as(1) 0
b (irun
) Li º restriction
a (1) ª1 can
) Li º aable
(8’).
22 (iare
Having four equations
(8’),i 1 bwe
« i 1 21 from
» 21 to identify the four unknown
¼» 11(7)inand
¬« the
(5),
long run¼ restriction can be written as
With D in (10) ¬and C ( L) given
elements of D and thus recover structural shocks.
Having four equationsf from (7) and (8’), wef are able to identify the four unknown
ª
b21 (i ) Li º astructural
(1) ª1 shocks.
b (i) Li º a21 (1) 0
(8’).
(8ʼ).
elements of D and«¬ thus
i 1 recover
i 1 22
»¼ 11
«¬
»¼
2.3 The AD-AS model
Having four equations from (7) and (8’), we are able to identify the four unknown
Having four equations
from
(7) and (8ʼ), we are able to identify the four unknown elements of D and thus recover
2.3
The
AD-AS
elements
of D model
and thus recover structural shocks.
[Figure 1 about here]
¦
¦
¦
¦
¦
¦
structural shocks.
[Figure 1 about here]
The AD-AS model
The explanation of the AD-AS (Aggregate Demand-Aggregate Supply) model is given
2.3 The AD-AS model
in detail in Dornbusch et al. (2001)8. Here we use it to see the responses of the price
The explanation of the AD-AS (Aggregate
Supply) model is given
[Figure 1Demand-Aggregate
about here]
level (P) and real GDP (Y) to (positive) demand and supply shocks in the short run and
in detail in Dornbusch et al. (2001)8. Here we use it to see the responses of the price
long
run.
The explanation
of
the
AD-AS
(Aggregate
Demand-Aggregate
Supply) model is in
given
in detail
inand
Dornbusch et
levelThe
(P) and
real GDP
(positive)
demandDemand-Aggregate
and supply shocks Supply)
the short
run
explanation
of(Y)
theto
AD-AS
(Aggregate
model
is given
8)
Figure
1
shows
the
AD-AS
model.
The
AD
line
is
downward
sloping,
the
short
run
AS supply
al. (2001) . Here long
we use
it to see the responses of the price
level (P) and real GDP (Y) to (positive) demand and
run.
in detail
in Dornbusch et al. (2001)8. Here we use it to see the responses of the price
(SRAS) is upward sloping, and the long run AS (LRAS) is vertical. Suppose that the
shocks in the short level
run
and
run.
Figure
1 shows
AD-AS
Thedemand
AD line and
is downward
sloping,
(P)long
and
real the
GDP
(Y) tomodel.
(positive)
supply shocks
in the
the short
short run
runAS
and
economy is in equilibrium at the intersection of AD, SRAS and LRAS (point E ).
is
upward
sloping,
and
the
long
run
AS
(LRAS)
is
vertical.
Suppose
that
the sloping,
Figure 1 shows(SRAS)
the
AD-AS
model.
The
AD
line
is
downward
sloping,
the
short
run
AS
(SRAS)
is
upward
long run.
When a demand shock occurs, the AD line shifts upward. From the graph on the left
is in
equilibrium
at themodel.
intersection
AD,
SRAS
and LRAS
(point
E ). run
Figure
1isshows
theSuppose
AD-AS
ADofline
downward
sloping,
the short
and the long run economy
AS (LRAS)
vertical.
that
theThe
economy
is isin
equilibrium
at the
intersection
ofAS
AD, SRAS
When
asee
demand
occurs,
line
shifts
upward.
the graph
on the
we canis
that inshock
the short
runthe
theAD
price
level
up and
real
GDP
increases
( E left
ńthe
A1 ),
(SRAS)
upward
sloping,
and
the
long
run
ASgoes
(LRAS)
isFrom
vertical.
Suppose
that
2.3
and LRAS (point E).
economy is in equilibrium at the intersection of AD, SRAS and LRAS (point E ).
When a demand shock occurs, the AD line shifts upward. From the graph on the left
run the price level
goes
upthe
andlong
GDP
(E→A
the run
longlevel
run real
gets back
the long run
while
run,
GDP gets
back
to theinlong
and GDP
the price
level to
goes
1), while
up in
further
(real
A1 ń
A2 real
). increases
can
seeup
that
in the
run the price level goes up and real GDP increases ( E ń A1 ),
level and the price we
level
goes
further
(Ashort
).
→A
1
2
up further
( Aa
A2 ). shock
1ń
When
supply
occurs, the AS line shifts downward. From the graph on the
When a supply
shock
ASreal
lineGDP
shiftsgets
downward.
From
therun
graph
onand
the the
rightprice
we observe
that in the
while
in occurs,
the longthe
run,
back to the
long
level
level goes
right we observe that, in the short run the price level goes down and real GDP increases
When a supply shock occurs, the AS line shifts downward. From the graph on the
short run the price level
goes down and real GDP increases (E→B1), and in the long run these effects proceed further
up further
( A1 ńthat,
A2 ). in the short run the price level goes down and real GDP increases
right
observe
( Ewe
ńB
1 ), and in the long run these effects proceed further ( B1 ń B2 ).
we while
can
see
that
the
short
run
the price
level
goes
up
andrun
real
GDP
increases
(see
E level
ń
A1 ),
inoccurs,
the in
long
real
GDP
gets
back
to
the
long
and
thecan
price
goes
When a demand
shock
therun,
AD
line
shifts
upward.
From
the
graph
onlevel
the left
we
that
in the short
(B1→B2).
When a supply shock occurs, the AS line shifts downward. From the graph on the
right we observe that, in the short run the price level goes down and real GDP increases 9)
to
note here.
First,
as awhose
definition,
a supply
(demand)
shock is supply
a shock
whose first
definition, a supply (demand)
shock
shock
firstconsider
impact
isstructural
a shift
in the
aggregate
(demand)
Now at the
endisofa this
section we
shocks.
There are some
pointscurve . We
9. We have seen above how AD
impact
is
a
shift
in
the
aggregate
supply
(demand)
curve
ń Band
), and
in
theaslong
run
these
effects
proceed
further
( B1 ńisBa
1here.
2 ).shock whose first
to( Enote
a definition,
a supply
(demand)
shock
have seen above how
AD
ASFirst,
curves
shift
when
shocks
occur.
and AS curves shift when shocks occur.
impact
is a
in the
aggregate
supply
(demand)
curve9. in
We
haveThere
seen
above
howpoints
AD
Second, we keep
in mind
the behavior
of the
central
in each
country
response
to shocks
when
decomposing
Now
atshift
the
end
of this
section
webank
consider
structural
shocks.
are some
Second, we keep in mind the behavior of the central bank in each country in
and
AS
curves
shift
when
shocks
occur.
noteand
here.
First,
as a into
definition,
aand
supply
(demand)
shock
is athat
shock
whose
first
the movements in to
output
the
price
level
supply
demand
parts.
We
assume
the
central
response to shocks when decomposing the movements in output and the price levelbank
in toderives
Second,
we
keep
in aggregate
mind the supply
behavior
of the curve
central
bank
inseen
eachabove
country
in
9. We
impact
is
a
shift
in
the
(demand)
have
how
AD
its optimal monetarysupply
policy and
to minimize
quadratic
function
inflation
gapbank
and output
terms of their
demanda parts.
We loss
assume
thatof the
central
derivesgap
itsinoptimal
response
to shocks
when
decomposing
the movements in output and the price level in to
and AS curves
shift
when
shocks occur.
monetary
policy
to minimize
a quadratic
loss
function of
inflation
gap and
output
gap
equilibrium levels.
The
central
bank
will
act
to
smooth
out
the
movements
of
both
inflation
and
output.
Theinoptimal
supply
and demand
We the
assume
that ofthe
optimal
Second,
we keep parts.
in mind
behavior
thecentral
centralbank
bankderives
in eachitscountry
in
of
their equilibrium
levels.
TheIncentral
bank
will actshock,
to smooth
out the
monetary policy monetary
willterms
differpolicy
depending
on
the
type
of
shocks.
the
case
of
a
demand
for
example
to minimize
a quadraticthe
lossmovements
function ofin
inflation
output
gapinintoa sudden
response to shocks
when decomposing
output gap
and and
the price
level
terms
of and
theirdemand
equilibrium
levels.
The central
bank
will
actandtoderives
smoothits
the and the
decrease in private
consumption,
the
AD parts.
line shifts
causing
both
output
inflation
toout
decrease,
supply
We downward
assume
that
the
central
bank
optimal
E ńof
BNow
), and
in
long
run these
effects
proceed
further
( Bshocks.
ń B2 ).points
1 this
1 some
at
thethe
end
ofconsider
this
section
we consider
There to
arenote
some
points
Now at the (end
section
we
structural
shocks.structural
There are
here.
First as a
monetary policy to minimize a quadratic loss 5function of inflation gap and output gap in
terms of their equilibrium levels. The 5 central bank will act to smooth out the
5
Examining the Possibility of Introducing a Common Currency for ASEAN(Vu Tuan Khai) (31)
LRAS
P
P
ADʼ
A2
AD
AD
LRAS
31
LRASʼ
E
SRAS
B2
A1
B1
E
SRAS
SRASʼ
Y
Y
Response to a demand shock
Figure 1
Response to a supply shock
The AD-AS model and the responses of the price level and GDP to shocks
central bank will react by a monetary loosening. In the case of a supply shock, for example sudden rise in oil price, the
AS line shift upward, output decreases while inflation goes up, the central bank faces a trade-off and will respond with a
tightened monetary policy to cool down inflation at the expense of output. In the literature, some authors regard only the
correlations of supply shocks are important because they consider most of the demand shocks are monetary ones, and
that once the monetary union is formed these shocks will be automatically synchronized. To a certain extent I agree with
this view, but I think that demand shocks other than monetary ones such as shocks to private consumption or investment
are also important, and they may not necessarily be synchronized even when the monetary union is formed. Hence,
correlations of both demand and supply shocks are important when considering forming a CCA10).
The third point is that, to identify the structural shocks we assumed that there are two kinds of shocks based on
their effects on output, i.e. the one that lasts long (permanent) and the other that is short-lived (temporary). They are
named supply shock and demand shock, respectively, because it is widely assumed that supply shocks are permanent
while demand shocks are temporary. In reality, however, as well-recognized in Bayoumi and Eichengreen (1994), there
might be long-lasting demand shocks as well as short-lived supply shocks. In such cases the method used here cannot
correctly identify demand and supply shocks. Admittedly, this is the limitation of the method. The impulse response
functions will help us to see this.
3.Data
Real GDP and CPI data of ASEAN countries were used for the analysis. Due to the constraint on data for ASEAN
横浜国際社会科学研究
32 (32)
第 13 巻第 1・2 号(2008 年 8 月)
countries, two data sets were collected and used. Quarterly data were available for only six ASEAN countries which
are referred to as ASEAN-6 in this paper. The sample periods are as follows: 1993Q1─2004Q2 for Indonesia (INA),
1980Q1─2004Q2 for Malaysia (MAL), 1984Q1─2004Q2 for Singapore (SIN), 1993Q1─2004Q2 for Thailand (THA),
1981Q1─2004Q2 for the Philippines (PHI) and 1989Q1─2004Q2 for Vietnam (VIE).
data sources are the websites of the Asia Regional Information Center (ARIC), ASEAN,
The other
set contains
annualfordata
nine ASEAN
countries.
Here this group
is denoted
ASEAN-911). The
the data
International
Centre
theofStudy
of East Asian
Development
(ICSEAD),
central
re
the
websites
of
the
Asia
Regional
Information
Center
(ARIC),
ASEAN,
ation Center (ARIC),
12. Philippines, Malaysia, and Myanmar (MYA),
sampleASEAN,
periods
areand
1976─2003
fordepartments
Indonesia, Singapore,
banks
statistical
of ASEANThailand,
countriesthe
nal
Centre for
the Study
of East Asian Development (ICSEAD), central
velopment
(ICSEAD),
central
Before
being
theand
quarterly
data
were seasonally adjusted using
1981─2003
for Vietnam,
1980─2003
forestimation
Laos (LAO)
Cambodia
(CAM).
12. used for
istical departments
of ASEAN
countries
data sources are the websites of the Asia Regional Infor
Asia Regional InformationCensus
Center (ARIC),procedure.
ASEAN, Together with annual data, their
were2004, ADB Key
These
data wereX-12
collected
from
variousadjusted
sources,using
mainly IMF International log-differences
Financial Statistics
gere
used
for
estimation
the
quarterly
data
were
seasonally
the International Centre for the Study of East Asian D
seasonally
adjusted
using
udy of East Asian Development (ICSEAD), central
Together
with
annual
data,
log-differences
were'other
Indicators
2004,
and UN
Statistical
Yearbook
The
data
sources
are and
the websites
ofdepartments
the Asia Regional
calculated.
The
twotheir
variables
' log2004.
cpit and
log gdp
be approximately
considered
t can
banks
statistical
of ASEAN countries1
,fprocedure.
their log-differences
were
ASEAN
countries12.
Information
Center
(ARIC),
ASEAN,
the
International
Centre
for
the
Study
of
East
Asian
Development
(ICSEAD),
Before
being
used
for
estimation
the quarterly data
two
variables
'
log
cpi
and
'
log
gdp
can
be
approximately
considered
the
quarterly
data
were
seasonally
adjusted
using
t
as inflation t rate and growth rate, respectively.
n be approximately considered
12)
Census X-12 procedure. Together with annual da
statistical departments
r with annualcentral
data,banks
theirandlog-differences
were of ASEAN countries .
e and growth rate, respectively.
Before
being used forconsidered
estimation the quarterly data were seasonallycalculated.
adjusted using
Census
X-12 procedure.
The two
variables
' log cpit and ' log gdpt c
cpit and ' log gdpt can
be approximately
spectively.
on
Together with annual data, their log-differences were calculated. The two variables ∆ log cpit and ∆ log gdpt can be
4. Estimation
approximately considered as inflation rate and growth rate, respectively.
as inflation rate and growth rate, respectively.
Estimation was done using Eviews
4.1. The lag length for the reduced-form VAR
4.Estimation
model
is
chosen
as
follows.
At
first,
SBC
Criterion)
and AIC (Akaike
4. Estimation
wasfordone
Eviews VAR
4.1. The lag length for the reduced-form(Schwarz-Bayesian
VAR
th
the using
reduced-form
Estimation
was doneCriterion)
using Eviews
4.1.
The
lag length
for the
reduced-form
VAR
model
chosen as follows.
Information
were
used
to
select
the
most
appropriate
VAR
model,
but is
these
n
follows. At
first,
(Schwarz-Bayesian Criterion) and AIC (Akaike
anasCriterion)
and
AICSBC
(Akaike
two
criteria
often
suggested
different
lag
lengths.
Thus,
the
author
decided
to
set
the
At
first,
SBC
(Schwarz-Bayesian
Criterion)
and
AIC
(Akaike
Information
Criterion)
were
used
to select the most
riterion)
weremodel,
used to
select
the most appropriate VAR model, but these
Estimation was done using Eviews 4.1. The lag le
opriate
but
these
ews 4.1.VAR
The lag length
for
the lag
reduced-form
VAR
same
length
for
all
countries
in
each
data
set.
Specifically,
the
lag
length
was
one
appropriate
VAR
model,
but these
two criteria
often
Thus, the authorfor
decided to set the
en suggested
different
lagthe
lengths.
Thus,
the author
decided
to suggested
set the different lag lengths.
model is chosen as follows. At first, SBC (Schwarz-Baye
the
author
decided
to set
SBC
(Schwarz-Bayesian
Criterion)
anddata
AICset
(Akaike
the
annual
(ASEAN-9),
and
four
for
the
quarterly
data
set
(ASEAN-6).
h
for
all
countries
in
each
data
set.
Specifically,
the
lag
length
was
one
for
same
lag
length
for
all
countries
in
each
data
set.
Specifically,
the
lag
length
was
one
for
the
annual
data
set (ASEAN-9),
Information Criterion) were
used to select the most ap
ally,
the the
lag length
was one forVAR model, but these
o select
most appropriate
After
having estimated
the structural shocks we calculate the correlation coefficients
adata
set (ASEAN-9),
and
four
for
the
quarterly
data
set (ASEAN-6).
and
four
for
the
quarterly
data
set
(ASEAN-6).
two
criteria
often
suggested
different lag lengths. Thu
set
(ASEAN-6).
nt lag lengths. Thus, the author
decided
to
set
the
of structural
shocksthe
forcorrelation
each pair coefficients
of countries. In addition, we test if the correlation
g
estimated
the structural
shocks
we calculate
same
lag
length
for
all
countries
ate
the
correlation
coefficients
After having
thesignificantly
structural
shocks
we calculate
the correlation
coefficients
of structural shocks in
foreach
eachdata set. Specif
each data set. Specifically,
the
lagestimated
lengthare
was
one
for
coefficients
positive
by using
the Fisher’s
variance-stabilizing
hocks
for
each
paircorrelation
of countries.
In addition,
we test if the
correlation
the
annual
data
set
(ASEAN-9),
and
four for the quarte
on,
we
test
if
the
d four for the quarterly
data set In
(ASEAN-6).
13. the
pair of countries.
addition, we
if the correlation
coefficients
significantly
by using
Fisherʼs varianceThetest
statistical
background
of theare
test,
in short,positive
is as follows
It is
eFisher’s
significantly
positive transformation.
by using the Fisher’s
variance-stabilizing
After
having
estimated
the
structural
shocks we calc
variance-stabilizing
13)
tural shocks westabilizing
calculate the
correlation
proved
that coefficients
the statistical
statistic background
z = (1/2)log[(1+
U ) /(1-U )] which is called the Fisher’s
transformation.
The
13 of the test, in short, is as follows . It is proved that the statistic
.n The
statistical
background
13. It is of the test, in short, is as follows . It is
of
structural
shocks
for
each
pair
of
countries.
In add
short,
is asInfollows
of
countries.
addition,
we
test
if
the
correlation
variance-stabilizing
transformation
of the estimated correlation coefficient U ,
the
statistic zthe
= (1/2)log[(1+
U ) /(1-U )] which
which is is
called
the Fisher’s
called
the Fisherʼs
variance-stabilizing transformation
of the
estimated correlation
ich
coefficients are
significantly
positive by using the
tive isby called
using the Fisher’s
Fisher’s variance-stabilizing
izing
transformation
of U
the
estimatedfollows
correlation
coefficient U , with mean zero, and variance T-3 1/ 2
asymptotically
a anormal
correlation
coefficient
13
of the test
coefficient
, ,asymptotically
follows
normaldistribution
distribution with mean zero,transformation.
and variance The statistical
where background
T is the
kground
of the
test, in short,
is as follows
. It is
1/ 2
that
statistic
U ) /(1-U )] w
size.is
By
the
significance
level
at 5%, and
running
a one-tailed proved
test (so that
the the
critical
level of z =
is (1/2)log[(1+
1.645)
/2)log[(1+
U )normal
/(1-Usample
)] distribution
which
called
the
Fisher’s
1/
2 setting
follows
a
with
mean
zero,
and
variance
T-3
zero, and variance T-3 where T is the sample size. By setting the significance level at 5%, and running a
variance-stabilizing
transformation
of the estimate
n of the estimated
correlation
coefficient
we can obtain
the critical
of Uthe
, ,given
the sample
T.1.645)
It is worth
noting
here
that
because
the
one-tailed
testlevel
(solevel
that
critical
of size
z ais
we can
obtain
the
critical
level
ofsample size T is
e level
sample
By setting
the
significance
at 5%,
andlevel
running
at size.
5%, and
running
a
1/their
2 T. critical
U ,country
given the
sampleso
size
It is worth
here that because
theare
sample
size T ais normal distribution with mea
different
from
to country,
levelsnoting
of the correlation
coefficients
also follows
different.
asymptotically
stribution
mean
zero,
variance
T-3obtain the critical level of
(so
thewith
critical
level
of of
zand
is 1.645)
we can
canthat
obtain
the
critical
level
different
from
country
to
country,
so
their
critical
levels
of
the
correlation
coefficients
ample
size T.sample
It is worth T
noting
here that because the sample size T is
because
isat 5%,
where T is the sample size. By setting the significan
tting
thethe
significancesize
level
and running a
5.Results
arecritical
also different.
country
to
country,
so
their
levels of the correlation coefficients
of the
one-tailed test (so that the critical level of z is 1.645) w
evel
of correlation
z is 1.645) coefficients
we can obtain the critical level of
nt.
U , countries.
given the sample size T. It is worth noting here th
Tables
2 andthe
3 report
thesize
correlations
of structural shocks between ASEAN
orth noting here that
because
sample
T is
different from country to country, so their critical leve
are also different.
o their critical levels of the correlation coefficients
5. demand
Results
Correlations of
shocks
In Table 2, while the results using quarterly data do not show high correlations of demand shocks among the
Tablesusing
2 and
3 report
the that
correlations
structural
shocks the
between
ASEAN
countries, the results
annual
data imply
Malaysia,ofSingapore,
Thailand,
Philippines
and Indonesia (the
countries.
Results
nd 3
reportbetween
the
correlations
of structural
shocks
between
ASEAN
ASEAN-5)
are good
candidates for
a CCA.
The results
also display minus or5.
near-zero
correlations for the other four
ral
shocks
ASEAN
countries except the case of Vietnam and the Philippines.
rrelations of structural shocks between ASEAN
[Table 2 about here]
[Table 2 about here]
Correlations of supply shocks
Table 2 about here]
Tables 2 and 3 report the correlations of struc
countries.
Similar to demand shocks, the results using quarterly data in Table 3a do not show high correlations among the
7
[Table 2 about here]
7
7
7
Examining the Possibility of Introducing a Common Currency for ASEAN(Vu Tuan Khai) (33)
Table 2
33
Correlation coefficients of demand shocks of ASEAN
2a.ASEAN-6 (quarterly data 1980Q2 2004Q2)
INA
MAL
SIN
THA
PHI
VIE
INA
1.00
MAL
0.14
1.00
SIN
0.11
0.08
1.00
THA
-0.01
0.20
0.32*
1.00
0.08
0.23
1.00
0.13
0.21
0.37*
1.00
THA
PHI
VIE
*
PHI
0.19
0.19
VIE
0.32*
0.04
2b.ASEAN-9 (annual data 1966 2004)
INA
MAL
SIN
INA
1.00
MAL
0.66*
1.00
SIN
0.17
0.74*
1.00
THA
0.25
0.75*
0.82*
PHI
0.02
0.49
*
*
0.54
0.42*
1.00
VIE
-0.27
-0.24
-0.07
-0.01
0.45*
1.00
LAO
CAM
MYA
1.00
LAO
0.19
-0.02
-0.40
0.05
-0.13
-0.16
CAM
0.10
-0.20
0.09
-0.23
-0.28
-0.29
1.00
0.00
1.00
MYA
0.27
-0.07
-0.42
-0.25
-0.25
0.05
-0.09
0.06
1.00
Notes: *means significantly positive at the 5% level.
countries except the correlations between Indonesia, Thailand and the Philippines. However, if we turn to Table 3b we
can see that the ASEAN-5 is suitable to form a CCA. This is consistent with the results in Table 2.
The reasons for the ASEAN-5 countries to have high positive correlations in both demand and supply shocks might
be that, in comparison with other four, their economic development stages are closer to one another, and their economies
share many common features such as adopting export-oriented policies, receiving much FDI from Japan etc. In addition,
there are close linkages between these economies, for example the close linkage in the electronic industry.
There is an argument that we need to see also the correlation between one countryʼs supply shocks and another
countryʼs demand shocks, because a supply shock occurs in one country (e.g. an improvement of technology in the
electronic industry in Indonesia) may lead to a demand shock in another country (e.g. Malaysia, an electronic-partsexporting country, as output rises in Indonesiaʼs electronic industry causing import of electronic parts from Malaysia
to increase). Doing that, however, does not make much sense because, as we saw above when explaining the AD-AS
model and will discuss further right below, the responses (of CPI and GDP) to a demand shock are different from those
to a supply shock in the short run and long run. For instance, in the short run CPI increases when a (positive) demand
shock occurs while it decreases if that is a (positive) supply shock. Therefore, a high correlation between one countryʼs
demand shocks and another countryʼs supply shocks does not mean low costs when the two countries form a CCA.
横浜国際社会科学研究
34 (34)
Table 3
第 13 巻第 1・2 号(2008 年 8 月)
Correlation coefficients of supply shocks of ASEAN
3a.ASEAN-6 (quarterly data 1980Q2 2004Q2)
INA
MAL
SIN
THA
PHI
VIE
INA
1.00
MAL
0.46*
1.00
SIN
0.10
0.34*
1.00
THA
0.31*
0.13
-0.03
1.00
PHI
0.32*
0.03
0.00
0.17
1.00
VIE
0.24
0.00
-0.22
0.02
0.08
1.00
THA
PHI
VIE
3b.ASEAN-9 (annual data 1966 2004)
INA
MAL
INA
1.00
MAL
0.75*
1.00
SIN
0.53*
0.60*
*
*
THA
0.67
*
0.61
SIN
LAO
CAM
MYA
1.00
0.49*
1.00
PHI
0.29
0.23
0.31*
0.32*
1.00
VIE
0.22
0.46*
0.25
0.13
-0.17
1.00
LAO
0.25
0.09
-0.21
-0.03
-0.07
0.22
1.00
CAM
0.14
0.27
0.37*
0.30
0.14
0.05
-0.50
1.00
MYA
-0.24
-0.28
-0.26
-0.41
-0.09
0.29
0.51*
-0.49
1.00
Notes: *means significantly positive at the 5% level.
Responses of CPI and GDP to structural shocks
We emphasize here one thing that has received little attention in the literature so far. That is, the signs of responses
of an economic variable across countries to a shock need to be consistent. The sign is positive if the economic variable
(GDP or CPI) increases, and negative if the economic variable decreases in response to a shock. This is a crucial
point because, as we will see below, the responses of CPI and GDP to demand and supply shocks may not necessarily
follow the implications of the AD-AS model, and more importantly their signs might be very different from country to
country. To make this point clearer, consider a case where a (positive) supply shock causes CPI to fall in one country,
and to rise in another. In this case not symmetric supply shocks, but asymmetric ones are desirable. A more complicated
case is the one in which, in response to a (positive) demand shock, GDP increases in one country while decreases and
then increases in another. In this case it is difficult to draw a relation between the symmetry of shocks and the costs of
forming a monetary union.
A merit of the VAR model is that it allows us to check the responses of economic variables to structural shocks.
Figures A2a and A2b in the Appendix show the graphs of the impulse response functions. One country has four graphs,
which display the cumulative responses of CPI and GDP to one standard deviation (s.d.) demand shock and one s.d.
supply shock over time. For each country, the upper two graphs show the cumulative impacts of shocks on GDP, while
the lower two show the cumulative impacts of shocks on CPI over time. The response of GDP to a demand shock
Examining the Possibility of Introducing a Common Currency for ASEAN(Vu Tuan Khai) (35)
Table 4
35
Number of cases consistent with the AD-AS model (for the estimated lines)
consistent
in part consistent
inconsistent
Response of GDP to a supply shock
15
0
0
Response of GDP to a demand shock
5
3
7
Response of CPI to a supply shock
7
2
6
Response of CPI to a demand shock
15
0
0
Table 5
Number of cases consistent with the AD-AS model (for the bands)
consistent
in part consistent
inconsistent
Response of GDP to a supply shock
14
1
0
Response of GDP to a demand shock
0
12
3
Response of CPI to a supply shock
5
10
0
Response of CPI to a demand shock
15
0
0
is displayed on the upper right-hand graph in which the line approaches the horizontal axis following the long run
restriction imposed in the model.
In each graph, the solid line is the point-estimates one. The other two broken lines, respectively called the upper
band and lower band of the impulse response function, are generated by bootstrapping14) with a 95% confidence interval
and one-thousand replications15). Tables 4 and 5 summarize the results in Figures 2a and 2b, showing the number of
cases in which responses of CPI and GDP are consistent, in part consistent, and inconsistent with the implications of the
AD-AS model discussed in section 2. These cases are judged by the sign of the impulse response function. Here, a case
that is “in part consistent” is defined as the one which is consistent in the short run but inconsistent in the long run or
vice versa. With two data sets for ASEAN-6 and ASEAN-9 we have a total of 15 cases for each response.
It is clear from Table 4 that the responses of CPI to a demand shock, and the response of GDP to a supply shock
follow the AD-AS model very well. However, the results for the responses of CPI to a supply shock, and of GDP to
a demand shock are mixed. These facts are also observed in Table 5, with only one difference that for the response of
CPI to a supply shock and the response of GDP to a demand shock, the number of “consistent” cases decreases while
the number of “in part consistent” cases increases sharply. More importantly, we observe that the signs of responses of a
variable to the same shock are not consistent between the countries. For instance, in response to a demand shock, GDP
increases in the case of the Philippines, while decreases in the case of Malaysia (quarterly data). In response to a supply
shock, CPI increases in the case of Singapore, but decreases and then increases in the case of Thailand. As a possibility,
the mixed result of the response of CPI to a supply shock can be attributed to the aforementioned problem in correctly
identifying structural shocks of the method. That is, there might have been demand shocks that had permanent effects on
real GDP, and thus they were treated as supply shocks. As a result, these shocks, in contrast to supply shocks, raised the
price level, making the behavior of the price level inconsistent with what is implied by the AD-AS model16). Anyhow,
the results imply a problem on the validity of the Blanchard-Quah SVAR method in calculating the symmetry of shocks.
The size of shocks, and the speed of adjustment to shocks
As argued by Bayoumi and Eichengreen (1994), not only the correlation between the structural shocks, but also
their size and speed of adjustment to shocks are important because even if shocks are asymmetric across the countries,
横浜国際社会科学研究
36 (36)
Table 6
第 13 巻第 1・2 号(2008 年 8 月)
The size of shocks and the speed of adjustment to shocks of ASEAN-9
Demand shocks
Supply shocks
Size
Speed
Size
Speed
INA
0.069
0.975
0.062
0.988
MAL
0.015
0.825
0.063
0.930
SIN
0.013
0.892
0.060
0.967
THA
0.026
0.815
0.088
0.938
PHI
0.034
0.981
0.061
0.992
VIE
0.182
0.605
0.045
0.615
LAO
0.251
0.973
0.032
0.997
CAM
0.088
0.953
0.077
0.984
MYA
0.095
0.967
0.093
0.931
ASEAN-9 average
0.086
0.891
0.064
0.929
EU average
0.010
0.533
0.027
0.898
but the size of shocks is small and they converge quickly to the equilibrium level, then the costs to adjust them are
relatively minor.
Another merit of the VAR model is that it provides us a way to calculate the size of underlying shocks and the
speed of adjustment to these shocks. Here the size of shocks and the speed of adjustment are defined as follows17):
the size of demand shocks = the first-year impact of one s.d. demand shock on CPI,
the size of supply shocks = the impact in the long run of one s.d. supply shock on GDP,
the speed of adjustment to demand shocks
= the fourth-year impact / the impact in the long run of one s.d. demand shock on CPI,
the speed of adjustment to supply shocks
= the fourth-year impact / the impact in the long run of one s.d. supply shock on GDP18).
The long run here is chosen to be 30 years. The smaller is the size of shocks and the faster (i.e. closer to unity) the speed
of adjustment to shocks, the more suitable are the countries to form a CCA. The reason for choosing the responses of
CPI to a demand shock, and of GDP to a supply shock in order to measure the size and speed of adjustment is that as we
have seen above, these responses are consistent with the AD-AS model, and thus consistent across all ASEAN countries.
In addition, for the purpose of comparison, estimation for EU was done using 13 EU countriesʼ annual data from IMF
International Financial Statistics CD-ROM. The sample period of data on EU in this table was set to be 1980─2003, which
is the same as that on ASEAN.
Table 6 reports these results for ASEAN-9 using annual data. We can see that in comparison with the EU area,
the sizes of shocks, especially the sizes of demand shocks in ASEAN are large. Two countries that have largest sizes
of demand shocks are Vietnam and Laos, while their sizes of supply shocks are quite small. This result likely reflects
the transition process to market economy in the late 1980s in these countries in which their government used to issue
much money in order to finance budget deficits. As argued by Zhang et al. (2004) the 1997─1998 Asian financial crisis
played a role to expand structural shocks. By drawing graphs of the structural shocks calculated, it is found that rather
than demand shocks, supply shocks were negative and large for all ASEAN-5 countries and Vietnam in the period 1997
─1998. This might be consistent with the argument that the Asian crisis was a supply shock. When we confine to the
Examining the Possibility of Introducing a Common Currency for ASEAN(Vu Tuan Khai) (37)
37
group of ASEAN-5, the average size of demand shocks is 0.031, relatively closer to that of the EU.
When we turn to the results of speed of adjustment, it is clear that structural shocks, especially demand shocks
converge to the equilibrium faster in ASEAN than in the EU. Thus it can be argued that ASEAN has a more favorable
environment to adopt a common currency than the EU in terms of speed of adjustment to shocks.
6. Concluding remarks
In summary, we find that correlations of both demand and supply shocks are high between a group of Indonesia,
Malaysia, Singapore, Thailand and the Philippines (the ASEAN-5), but not all ASEAN countries. In comparison with
the EU area, while the sizes of shocks are larger, the speed of adjustment to shocks is faster. The results suggest that
ASEAN as a whole does not form an optimum currency area, but the ASEAN-5 countries are good candidates to
introduce a common currency. Regarding the signs of responses of the variables to shocks, we find that the responses
of CPI and GDP to structural shocks are consistent with the AD-AS model and consistent among all ASEAN countries.
However, the responses of CPI to a supply shock, and of GDP to a demand shock are mixed, and quite different between
the countries. This implies an important problem about the validity of the Blanchard-Quah VAR method.
For the other four ASEAN countries, although the results do not support their introduction of a common currency
with the ASEAN-5 countries, given the fact of active economic integration and the strong will of governments in the
region to cooperate, it can be argued that the results do not necessarily reject the possibility for them for the following
reasons. First, the data used in this analysis for these four countries are those of the 1980s and 1990s, the period
when countries like Vietnam and Laos were in a transition process, and Cambodia was suffering from the aftermath
of its civil war with much instability. These countries may need time for economic development before considering
the introduction of a common currency. A period to prepare, i.e. a period for their economies to regain stability, and
moreover, for economic variables among countries in the region to converge to some degree as had occurred in the EU,
is likely needed. Second, we still need to analyze the beneficial aspect of forming a CCA, especially under the present
dynamism in the region where trade and investments are very active. Furthermore, as argued by Frankel and Rose (1998),
shocks are likely to become symmetric as trade increases. I leave these issues for future work.
Acknowledgements
This is a revised version of my master thesis submitted to the International Graduate School of Social Sciences,
Yokohama National University in March, 2005. I am deeply indebted to Etsuro Shioji, my Academic Advisor, for his
valuable advice and unstinting guidance. I am grateful to Kiyotaka Sato for his kind encouragement and comments.
I am benefited from comments of Keiichi Koda, Craig Parsons, and Tsunao Okumura. I would also like to thank an
anonymous referee for very helpful and detailed comments and suggestions. The remaining errors are my own.
References
ASEAN Secretariat, Media Release─Vientiane, 29th November 2004. Available from http://www.aseansec.org/home.htm.
Bayoumi, T. and Eichengreen, B. (1993), “Shocking Aspects of European Monetary Integration". In Adjustment and Growth in
the European Monetary Union (eds) Torres, F. and Giavazzi, F., Cambridge University Press, Cambridge, pp. 193─229.
Bayoumi, T. and Eichengreen, B. (1994), “One Money or Many? Analyzing the Prospects for Monetary Unification in Various
Parts of the World", Princeton Studies in International Finance, 16, International Section, Princeton University.
Blanchard, O.J. and Quah, D. (1989), “The Dynamic Effects of Aggregate Demand and Supply Disturbances", American
38 (38)
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第 13 巻第 1・2 号(2008 年 8 月)
Economic Review, 79 (September), 665─673.
De Grauwe, P. (2000), The Economics of Monetary Integration (4th ed.), Oxford University Press, Oxford.
Dornbush, R., Fischer, S. and Startz, R. (2001), Macroeconomics, International Edition (8th ed.), the McGraw-Hill Companies,
Inc., Ch. 2, 5, 6.
Frankel, J. A. and Rose, A. (1998), “The Endogeneity of the Optimum Currency Area Criterion", Economic Journal, 108 (July),
1009─1025.
Mundell, R. (1961), “A Theory of Optimum Currency Areas", American Economic Review, 51 (September), 657─665.
Zhang, Z., Sato, K., and McAleer, M. (2003), “Is a Monetary Union Feasible for East Asia?", Applied Economics, 36, 1031─
1043.
References on data
Asian Development Bank, Key Indicators 2004.
Asia Regional Information Center (ARIC) homepage. Available from http://aric.adb.org/.
International Centre for the Study of East Asian Development (ICSEAD) homepage. Available from http://www.icsead.or.jp/.
International Monetary Fund, International Financial Statistics 2004.
United Nations, Statistical Yearbook 2004.
Appendix
Table A1
Average
growth rate
(1999─2003)
Recent socio-economic indicators of ASEAN
Trade in
percent of
GDP (97─01)
Real GDP
in billions
USD (2003)
Population
(2003)
Brunei
2.9 %
101.4%
4.7
na
Cambodia
6.8 %
78.6%
4.2
13.3
Indonesia
3.4%
59.8%
208.5
215.0
Laos
6.1%
53.6%
2.0
5.7
Malaysia
4.9%
172.9%
103.2
25.0
Myanmar
9.0%
50.5%
9.6
53.2
Philippines
4.3%
79.9%
80.4
81.1
Singapore
3.6%
274.6%
91.4
4.2
Thailand
4.7%
89.6%
143.3
64.0
Vietnam
6.5%
82.4%
39.0
80.9
ASEAN
5.2%
118.8%
686.3
542.4
VLCM1
7.1%
66.3%3)
8.0%*
28.2%*
ASEAN-52
4.2%
135.4%
91.3%*
71.8%*
Note: *as percent of total ASEAN
1 VLCM: Vietnam, Laos, Cambodia, Myanmar.
2 ASEAN-5: Indonesia, Malaysia, Singapore, Thailand and the Philippines.
3 For comparison, the same data for the US, Japan and Korea are 24.2%, 19.0% and 70.9%, respectively.
Sources: IFS 2004 and ADB Key Indicators 2004.
Examining the Possibility of Introducing a Common Currency for ASEAN(Vu Tuan Khai) (39)
Figure A2
39
Accumulated responses of CPI and GDP to structural shocks and bootstrap 95% confidence bands
A2a.ASEAN-6 (quarterly data 1980Q2 2004Q2)
MALAYSIA
INDONESIA
GDP to supply shock
.06
.05
GDP to demand shock
.010
GDP to supply shock
.000
.04
.05
.005
-.004
.03
.04
-.008
.000
.02
.03
-.012
-.005
.02
.01
-.010
.01
.00
5
10
15
CPI to supply shock
.00
.00
-.015
20
5
10
15
-.016
2
4
6
20
8
10
12
14
16
18
20 22 24
CPI to supply shock
.012
CPI to demand shock
.05
6
8
10
12
14
16
18
20
22 24
20
22 24
20
22 24
20
22 24
20
22 24
20
22 24
CPI to demand shock
.008
.004
.03
.006
.004
.02
-.08
.000
.002
.01
-.10
-.12
5
10
15
-.004
.00
20
5
10
15
2
4
6
8
10
12
14
16
18
GDP to supply shock
.024
.016
.012
.008
.004
2
4
6
8
10
12
14
16
18
20
22
24
CPI to supply shock
.000
.07
.005
.06
.004
.05
.003
.04
.002
.03
.001
.02
.000
.01
-.001
.00
4
6
8
10
12
14
16
18
20
22
24
CPI to demand shock
.020
.012
-.016
.008
.004
2
4
6
8
10
12
14
16
18
20
22
24
2
4
.05
6
8
10
12
14
16
18
20
22
24
2
4
6
8
10
12
14
16
18
20 22 24
CPI to supply shock
.005
.004
8
10
12
14
16
18
20
22
24
CPI to supply shock
.015
-.015
10
12
14
16
18
.001
2
4
6
8
10
12
14
16
18
20 22 24
GDP to supply shock
.000
2
4
6
8
10
12
14
16
18
.010
.005
.000
-.005
-.010
-.015
.000
4
6
8
10
12
14
16
18
20
22
24
CPI to demand shock
.016
-.005
GDP to demand shock
.015
.010
2
8
.002
.005
6
6
CPI to demand shock
.003
.015
-.010
4
4
.006
.004
-.002
2
.007
.020
-.005
2
-.04
.006
.025
.000
.01
18
GDP to demand shock
.009
.030
.005
.02
16
.008
.035
.010
.03
14
VIETNAM
GDP to demand shock
.015
.04
12
-.03
THAILAND
GDP to supply shock
10
-.02
.000
-.028
.000
8
.00
.002
-.024
6
-.01
.008
-.020
4
.02
.010
.016
-.012
2
.01
.012
-.004
-.008
GDP to supply shock
.08
.006
2
.000
SINGAPORE
GDP to demand shock
.007
.020
20 22 24
20
PHILIPPINES
2
4
6
8
10
12
14
16
18
20 22 24
CPI to supply shock
.08
-.020
2
4
6
8
10
12
14
16
18
CPI to demand shock
.20
.014
.010
.012
.005
.010
.000
.04
.16
.00
.12
-.04
.08
.008
.006
-.005
.004
-.010
-.015
4
.010
-.06
.00
2
.012
.04
-.04
-.032
-.020
.008
-.02
.000
GDP to demand shock
.004
-.08
.04
.002
2
4
6
8
10
12
14
16
18
20
22
24
.000
2
4
6
8
10
12
14
16
18
20
22
A2b. ASEAN-9 (annual data 1966-2004)
24
-.12
2
4
6
8
10
12
14
16
18
20 22 24
.00
2
4
6
8
10
12
14
16
18
横浜国際社会科学研究
40 (40)
第 13 巻第 1・2 号(2008 年 8 月)
A2b.ASEAN-9 (annual data 1966 2004)
CAMBODIA
GDP to supply shock
.16
INDONESIA
GDP to demand shock
.005
.14
.09
.08
.06
.05
-.010
-.015
.04
-.015
.02
-.025
.00
-.030
5
10
15
20
CPI to supply shock
.6
.03
5
10
15
20
CPI to demand shock
.2
.1
5
10
15
20
.0
15
20
10
15
20
.05
.05
.04
.03
.02
.01
5
10
15
20
.00
10
15
20
CPI to supply shock
.2
.4
.0
.3
-.1
.2
-.2
.1
-.3
.0
20
-.008
.03
-.012
.02
-.016
.01
5
10
15
20
CPI to demand shock
.00
GDP to demand shock
-.004
.04
.5
.1
15
.000
.05
.000
5
10
.004
.06
-.004
-.008
5
.008
.07
.004
.01
.00
.012
.08
.008
.02
GDP to supply shock
.09
.012
.03
20
MALAYSIA
GDP to demand shock
.016
.04
15
.06
-.06
LAOS
GDP to supply shock
10
CPI to demand shock
.07
-.04
-.12
5
.08
-.10
5
-.030
.09
-.08
.0
-.2
10
CPI to supply shock
.00
.3
-.1
5
-.02
.4
.1
.00
.02
.5
.2
-.025
.01
.4
.3
-.020
.02
.6
.5
-.005
.06
-.010
-.020
.04
.000
.07
-.005
.10
GDP to demand shock
.005
.08
.000
.12
GDP to supply shock
5
10
15
20
CPI to supply shock
.05
-.020
5
.05
.04
10
15
20
CPI to demand shock
.04
.03
.03
.02
5
10
15
20
.02
.01
.01
.00
5
10
15
20
-.01
5
10
15
20
MYANMAR
GDP to supply shock
.12
.08
.00
.004
.002
.02
.000
.01
10
15
20
CPI to supply shock
.20
-.04
5
10
15
20
CPI to demand shock
.30
.00
.25
-.02
.10
.20
-.04
.05
.15
-.06
.00
.10
-.08
-.05
.05
-.10
5
10
15
20
.00
5
10
15
5
20
-.12
10
15
20
CPI to supply shock
.00
.15
-.10
.006
.03
-.03
5
20
.008
.05
-.02
.02
15
.010
.04
.04
10
GDP to demand shock
.012
.06
-.01
.06
.00
GDP to supply shock
.07
.08
5
PHILIPPINES
GDP to demand shock
.01
.10
.00
-.002
5
10
15
20
CPI to demand shock
.07
.06
.05
.04
.03
.02
.01
5
10
15
20
.00
5
10
15
20
Examining the Possibility of Introducing a Common Currency for ASEAN(Vu Tuan Khai) (41)
SINGAPORE
GDP to supply shock
.08
THAILAND
GDP to demand shock
.012
.07
GDP to supply shock
.12
.008
.10
.004
.04
.08
.000
.06
.03
-.004
.04
-.008
.02
.020
.015
.010
.005
.000
.02
.01
.00
5
10
15
20
CPI to supply shock
.06
-.012
5
10
15
20
CPI to demand shock
.035
.005
-.02
.00
.000
-.04
10
15
20
20
-.010
5
10
15
20
CPI to demand shock
.10
.08
.06
.04
.00
.010
.01
5
15
.02
.015
.02
10
CPI to supply shock
.04
.020
.03
5
.06
.025
.04
-.005
.08
.030
.05
.00
GDP to demand shock
.025
.06
.05
41
5
10
15
20
.02
5
10
15
20
.00
5
10
15
20
VIETNAM
GDP to supply shock
.06
GDP to demand shock
.012
.010
.05
.008
.04
.006
.03
.004
.002
.02
.000
.01
.00
-.002
5
10
15
20
CPI to supply shock
0.4
5
10
15
20
CPI to demand shock
.9
.8
0.0
.7
-0.4
.6
.5
-0.8
.4
-1.2
.3
.2
-1.6
-2.0
-.004
.1
5
10
15
20
.0
5
10
15
20
Notes
1) ASEAN now consists of 10 members: Indonesia, Malaysia, Singapore, Thailand, Philippines, Brunei, Vietnam, Laos,
Cambodia, and Myanmar.
2) See the Media Release─Vientiane, 29 November 2004.
3) First discussed in Mundell (1961).
4) Even in this case the costs could be small if there exists some kind of adjustment mechanisms in the market, for example the
adjustment through the labor market provided that wages are flexible or labor is mobile across countries. These conditions,
however,
are often hardly coefficients
satisfied in reality.of demand shocks of ASEAN
Table
2: Correlation
5) A more comprehensive explanation about the theory of optimum currency area is given De Grauwe (2000). Bayoumi and
2a. Eichengreen
ASEAN-6
(quarterly data 1980Q2-2004Q2)
(1994) discussed in detail the concept of symmetry of shocks as a criterion for introducing a CCA.
INASingapore,
MAL
THA(often PHI
VIE
6) Indonesia, Malaysia,
ThailandSIN
and Philippines
known as ASEAN-5).
7) The reasons might
INA
1.00be that these countries joined ASEAN late, and the data were not available.
8) See also Bayoumi and Eichengreen (1993).
MAL
0.14
1.00
9) See Dornbusch et al. (2001) pp. 118─122.
SIN
0.11
0.08
1.00 me to think about this important point.
10) I thank an anonymous referee for suggesting
THA
0.20
1.00
11) Brunei is not-0.01
included in the
analysis0.32*
due to the lack
of data.
Tables
PHI
VIE
0.19
0.32*
0.19*
0.04
0.08
0.13
0.23
0.21
1.00
0.37*
1.00
42 (42)
横浜国際社会科学研究
第 13 巻第 1・2 号(2008 年 8 月)
12) For the quarterly data of some countries (for instance, those of Malaysia, Vietnam, and recent data of ASEAN-6), the author
contacted with the organizations and departments in charge, and received the data by e-mail.
13) See Zhang et al. (2004).
14) See Enders (2004) pp. 234─238 for more details of this method.
15) For several cases bootstrapping with ten-thousand replications was tried and the results were almost the same as onethousand-time repetition.
16) The explanation for the mixed result of response of GDP to a demand shock is still unknown here.
17) Drawn upon Zhang et al. (2004).
18) For some cases where the four-year impact went beyond the long run level, the speed of adjustmentʼs definition is corrected
as follows: 2-(the fourth-year impact / the impact in the long run).
[ブー
トウン
カイ
横浜国立大学大学院国際社会科学研究科博士課程修了]
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