Liu, Hai Yue; Chen, Xiao Lan
A icle
The impo ed p ice, in la ion and exchange a e pass-
h ough in China
Cogen Economics & Finance
P o ided in Coope a ion wi h:
Taylo & F ancis G oup
Sugges ed Ci a ion: Liu, Hai Yue; Chen, Xiao Lan (2017) : The impo ed p ice, in la ion and exchange
a e pass- h ough in China, Cogen Economics & Finance, ISSN 2332-2039, Taylo & F ancis,
Abingdon, Vol. 5, Iss. 1, pp. 1-13,
h ps://doi.o g/10.1080/23322039.2017.1279814
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/194641
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Liu & Chen, Cogen Economics & Finance (2017),
5: 1279814
h p://dx.doi.o g/10.1080/23322039.2017.1279814
FINANCIAL ECONOMICS | RESEARCH ARTICLE
The impo ed p ice, in la ion and exchange a e
pass- h ough in China
Hai Yue Liu
1
and Xiao Lan Chen
2
*
Abs ac :This pape conside s he e ec o exchange a e (ER) le el on China’s do-
mes ic p ices du ing he pe iod o 2003–2012. We examine China’s consume p ice
index (CPI), impo p ice index (IPI) and p oduce p ice index (PPI) by using ime
se ies ec o e o co ec ion analysis. The main inding o he pape is ha ER pass-
h ough has had a limi ed bu g owing e ec on domes ic p ices and will con inue
o do so. ER egime change ha was announced by Chinese go e nmen in 2005 led
o an inc eased sensi i i y o ER pass- h ough o domes ic p ices.
Subjec s: Economic Theo y & Philosophy; Mone a y Economics; In e na ional Finance
Keywo ds: CPI; PPI; IPI; exchange a e
JEL classi ica ion: F31; F41; E31
1. In oduc ion
As China has been u he emb acing globaliza ion, exchange a e becomes an impo an economic
index o e lec he s a us o he mac o economy. I is a key ac o linking in e na ional ade and
capi al low. O e he pas wen y yea s, China has been mo ing om a managed exchange a e
owa d a ully loa ing exchange a e. Since 2005, China has e o med he exchange a e egime o
a managed loa ing sys em e e ing o a baske o cu encies. A p esen , China is u ging u he
e o m o imp o e exchange a e lexibili y as pa o plans o libe a e China’s capi al accoun . In he
lead up o es ablishing a ully loa ing exchange a e, he luc ua ion o he RMB will ha e mo e im-
pac s on China’s economy, such as on he p ice le els in China.
*Co esponding au ho : Xiao Lan Chen,
School o Economics, Sichuan Uni e si y,
Chengdu 610064, China
E-mail: [email p o ec ed]
Re iewing edi o :
Ca oline Ellio , Hudde s ield Uni e si y,
UK
Addi ional in o ma ion is a ailable a
he end o he a icle
ABOUT THE AUTHORS
Hai Yue Liu is a associa e p o esso in he Business
School o Sichuan Uni e si y, China. He esea ch
in e es s a e in e na ional economics, money and
banking, C oss-Bo de Me ge and Acquisi ion, e c.
Xiao Lan Chen is a lec u e in he School o
Economics o Sichuan Uni e si y, China. She
specialized in econome ics. He esea ch in e es s
a e en i onmen al economics and in e na ional
economics.
PUBLIC INTEREST STATEMENT
How does exchange a e pass- h ough a ec
domes ic p ice le el? I has long been a opic
o in e es in academia. In China, Schola s uses
mul iple heo ies and empi ical me hods only o
p o e ha he impe ec ions o exchange a e
pass- h ough channels. While in ou esea ch, we
ind ha a shock on nominal e ec i e exchange
a e (NEER) has nega i e e ec on CPI which
depic s ha RMB app ecia ion will cause CPI
dec ease. Exchange a e pass- h ough also p o ed
o ha e limi ed bu g owing impac on domes ic
in la ion since he o eign exchange a e egime
e o m in 2005. In ligh o hese indings, China’s
in la ion can’ only be explained as “domes ic
economic imbalance”. Mo e lexible exchange a e
egime b ings s onge ansmission e ec om
exchange a e and impo ed p ices o domes ic
in la ion in China.
Recei ed: 23 Oc obe 2016
Accep ed: 03 Janua y 2017
Published: 02 Feb ua y 2017
Page 1 o 13
© 2017 The Au ho (s). This open access a icle is dis ibu ed unde a C ea i e Commons A ibu ion
(CC-BY) 4.0 license.
Page 2 o 13
Liu & Chen, Cogen Economics & Finance (2017),
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h p://dx.doi.o g/10.1080/23322039.2017.1279814
The luc ua ion o exchange a e will cause changes on he impo ed p ice and hen pass on o he
p oduce p ice index (PPI) and consume p ice index (CPI) o a coun y. T adi ional heo y s a es ha
he app ecia ion o a coun y’s cu ency will elie e he p essu e on he in la ion in ha coun y.
Since 2003, he RMB has app ecia ed o e 24% and his app ecia ion didn’ help b ing down he p ice
le el while China was acing he high p essu e o in la ion(Yue, Qiang, & Kai, 2015). The eal e ec i e
exchange a e (REER) inc eased 17% om 2003 o 2008, while CPI inc eased o e 8% du ing ha
same ime. I aised an in e es ing poin ega ding how e ec i e he exchange a e pass- h ough is
o he impo ed p ice and p ice le el in China. This pape ies o s udy he e ec s o he exchange
a e luc ua ion on he p ice le els in China. Using mon hly da a om 2003 o 2012, he sensi i i y o
he impo ed p ice and consume p ice in China esponding o he exchange a e luc ua ion was
examined h ough he ec o e o co ec ion (VEC) model.
The es o he pape is o ganized as ollows: Sec ion 2 e iews he ele an heo e ical and empi i-
cal li e a u e. Sec ion 3 desc ibes he heo e ical backg ound. Sec ion 4 se s up he empi ical model
and explains he esul s. Sec ion 5 concludes he pape .
2. Li e a u e e iew
Al hough exchange a e pass- h ough has long been o in e es , he ocus o his in e es has e ol ed
conside ably o e ime. Beginning in he la e 1980s, a e a long deba e o e he law o one p ice and
con e gence ac oss coun ies, exchange a e pass- h ough s udies emphasized indus ial o ganiza-
ion heo ies. The ole o segmen a ion and p ice disc imina ion ac oss geog aphically dis inc p od-
uc ma ke s was also hea ily s udied. Mo e ecen ly, pass- h ough issues ha e played a cen al ole
in hea ed deba es o e app op ia e mone a y policies and an op imal exchange a e egime in gen-
e al equilib ium models. These deba es ha e b oad implica ions o he conduc o mone a y policy,
o mac oeconomic s abili y, o in e na ional ansmission o shocks, and o e o s o con ain la ge
imbalances in ade and in e na ional capi al lows.
2.1. Explana ions om heo ies o indus ial o ganiza ion
Subs an ial wo k, bo h heo e ical and empi ical, has d awn on models o indus ial o ganiza ion o
explain he links be ween exchange a es and p ices in e ms o ma ke concen a ion, impo pen-
e a ion, he subs i u abili y o o eign and domes ic p oduc s, and he na u e o oligopolis ic com-
pe i ion. Dohne (1984) de eloped a dynamic model o p icing by o wa d-looking compe i i e
p o i -maximizing expo e s in which consume s adjus slowly o p ice changes. The le el o pass-
h ough is de e mined by he speed o consume adjus men and expec a ions o he du a ion o
exchange- a e changes. Mann (1986) and K einin, Ma in, and Sheehey (1987) all p esen some e i-
dence sugges ing ha indus y cha ac e is ics in luence he ex en o which exchange- a e mo e-
men s a e passed on o he dolla p ice o o eign goods. K ugman (1986) poin ed ou ha he eason
o incomple e exchange a e ansmission o he impo ed p ice is he p icing- o-ma ke (PTM) be-
ha io o i ms. Feinbe g (1986, 1989) ound ha ma ke concen a ion and impo pene a ion, di -
e ing by indus y, in luenced he pass- h ough o changes in exchange a es o p ices o Wes
Ge man domes ic goods om 1977 o 1983. He also explained he pass- h ough in o domes ic p ices
o he Uni ed S a es o e he 1974–1987. F oo and Klempe e (1989) p o ide bo h heo y and e i-
dence sugges ing ha expec a ions o exchange a es play a dominan ole in p ice de e mina ion.
Do nbusch (1987) de eloped se e al s a ic models o explain pass- h ough wi h e e ence o he
na u e o compe i ion be ween i ms and he ela i e numbe o home and o eign i ms. The esul s
showed ha incomple e pass- h ough can occu e en in he case o pe ec subs i u es. Baldwin
(1988) p esen ed a model, wi h sunk ma ke -en y cos s, in which empo a y exchange- a e luc ua-
ions al e domes ic ma ke s uc u e su icien ly o in luence he pass- h ough om eal exchange
a es o eal impo p ices. Fishe (1989) laid ou a pa ial equilib ium model in which domes ic and
o eign i ms se p ices based on expec ed exchange a es; he showed ha exchange- a e changes
could imply changes in impo p ices, bu he deg ee o pass- h ough depended on domes ic and
o eign ma ke s uc u es. As su eyed by Goldbe g and Kne e (1997), mos o he a ailable e i-
dence is om e y na owly de ined expo indus ies, wi h an emphasis o en placed on he p icing-
o-ma ke beha io o expo e s. Ma s on (1990), Kne e (1992, 1993) and Goldbe g and Kne e
Page 3 o 13
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h p://dx.doi.o g/10.1080/23322039.2017.1279814
(1997) used expo p ices o expo uni alues om speci ic coun ies o mul iple des ina ions wi h
he in en o iden i ying p ice disc imina ion o p icing- o-ma ke ac i i y. Whe eas impo p ices a e
by de ini ion jus he local-cu ency alue o ano he p oduce ’s expo p ices, he impo p ice se ies
agg ega e ac oss p oduce s om all sou ce coun ies and ac oss a b oade a ay o p ices.
2.2. Explana ions om mone a y heo ies
Mos no ably, heo e ical wo ks a gue ha ola ili y in mone a y agg ega es and exchange a es o
coun ies should in luence he choice o in oice cu encies in ade (De e eux & Engel, 2001). In
equilib ium, coun ies wi h low ela i e exchange a e a iabili y o s able mone a y policies would
ha e hei cu encies chosen o ansac ion in oicing. The low-exchange- a e- a iabili y coun ies
would also be hose wi h lowe exchange a e pass- h ough. Taylo (2000) also has no ed he po en-
ial complemen a i y be ween mone a y s abili y and mone a y e ec i eness as a policy ins u-
men . The idea is ha i pass- h ough a es a e endogenous o a coun y’s ela i e mone a y s abili y,
pe iods o mo e s able in la ion and mone a y pe o mance will also be pe iods when mone a y
policy may be mo e e ec i e as a s abiliza ion ins umen . Fo he pu pose o he ele an mac oe-
conomic deba e, impo p ice se ies wi h agg ega ion a e he app op ia e uni s o analysis. Taylo
(2000) and Gold ajn and We lang (2000), among o he s, ha e a gued ha pass- h ough a es may
ha e been declining o e ime.
2.3. Some e idence ound in Chinese li e a u e
Recen empi ical esea ch by some Chinese schola s has d awn con o e sial conclusions on he
exchange pass- h ough o China’s in la ion. Jixiang, Yancheng, Liping, and Yuan eng (2011) ound
ha RMB app ecia ion would inc ease he domes ic p ice le el h ough a G ange causali y es by
using mon hly da a o 2005–2010. Zhu and Liu (2012) s udied he exchange a e pass- h ough and
i s ela ionship wi h agg ega e domes ic demand and wo ld commodi y p ice om he yea 1980 o
2010. The esul shows a posi i e ela ionship wi h RMB app ecia ion and domes ic in la ion by using
an ARDL model. They d ew a conclusion ha China’s domes ic in la ion is a “domes ic issue” a he
han an “impo ed” one. Ha (2008)a gued ha RMB app ecia ion would es ain he domes ic in la-
ion. Shi, Fu, and Xu (2008) ound he nega i e ela ionship be ween RMB app ecia ion and in la ion
by using qua e ly da a om 1994 o 2007. They also ound ha he exchange a e pass- h ough is
impe ec in China. Jiang and Kim (2013) u he p o ed hei esul s. Bei and Zhu (2008) esea ch
showed ha he e is no di ec ela ionship be ween exchange a e and domes ic p ice. This conclu-
sion is simila o ha o Li and He (2007).
This pape looks a China’ ER pass- h ough o domes ic p ice le els by ocusing on he exchange
a e egime e o m. Al hough many exis ed li e a u e concluded ha China does no ha e a s ong
exchange a e pass- h ough channel. We a gue ha a sho - e m pass- h ough channel om ex-
change a e o IPI and PPI and hen o CPI is p o ed o be e ec i e and smoo h. Fu he he ex-
change a e e o m in 2005 s eng hened he sensibili y o domes ic p ice le els esponding o
exchange a e luc ua ions.
3. Exchange a e pass- h ough and domes ic p ice le el
Acco ding o S ensson (2000)’s in la ion a ge ing model, exchange a e can a ec domes ic p ice
le el h ough di ec and indi ec channels hence ha ing u he impac on mone a y policy. Fi s ly,
exchange a e ola ili y a ec s he impo ed p ice le el which is a pa o he CPI calcula ion baske .
Secondly, exchange a e ola ili y a ec s he ela i e p ice le el o he impo ing and expo ing
coun ies, which in u n changes he demand le el o he impo ed and expo ed p oduc s. Thi dly,
aking o eign exchange a e as an asse , he luc ua ion o he exchange a e expec a ions a ec s
he domes ic p ice le el. Finally, he mac oeconomic condi ions o o eign coun ies would also a -
ec he domes ic p ice le el. Buil on S ensson’s amewo k, his pape se up a VAR model o he
dynamic exchange a e pass- h ough o domes ic p ice le el aking Chinese economic eali y in o
conside a ion.
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The ansmission s a ed om he o eign supply shocks which we use IM (China’s o al impo ) as
a p oxy a iable.
TIM
ep esen s he o eign supply. We use IM as a p oxy a iable. M is he b oad money. We ake M2
as a p oxy a iable.
NEER
is he nominal e ec i e exchange a e.
ES
,
EM
,
ED
and
EE
ep esen he
impac om shocks o he han o he supply le el, he money supply, domes ic demand o he ex-
change a e.
We assume ha he IPI, PPI and CPI ha e posi i e ansmission ela ionships wi h each o he .
Exp essed as lows:
PIPI
,
PPPI
and
PCPI
ep esen he impo ed p ice index, p oduce ’s p ice index and consume ’s p ice
index, espec i ely. These h ee a iables e lec he p ice le el o a coun y.
EIPI
,
EPPI
and
ECPI
ep e-
sen he impac om shocks o IPI, PPI, and CPI, espec ully. We ake hese se en impac s as a i-
ables o he VAR model o e alua e hei e ec on in la ion.
4. Empi ical analysis
Basically, he analysis s a s om he educed o m o an un es ic ed VAR model wi h k lags ha
can be w i en down as:
whe e D is a
(n×1)
ec o o all de e minis ic a iables such as in e cep s, ends, dummies e c.,
A1,
⋯
,Ak
a e
(n×n)
ma ix o coe icien s, Z = (Z1 , Z2 , ⋯, Zn )’ is a ec o o
n
a iables, u is a
(n×1)
column ec o o inno a ions, ha is, se ially unco ela ed dis u bances ha ha e ze o mean and a
a iance-co a iance ma ix a iance-co a iance ma ix
E�
u1,u�
�
=
∑u
, i.e.
u
∼N
�
0,
∑�
.
The es ima ion o
a,A1,
⋯
,Ak
, a e ob ained by applying o dina y leas squa es (OLS) o each pa
o Equa ion (8) sepa a ely, and he es ima e o
∑u
is gi en by he sample co a iance ma ix o he
OLS esiduals.
I co-in eg a ion be ween he a iables exis s, he VAR model can be ew i en in a VEC ( ec o
e o co ec ion) model as:
whe e
Δ
is a di e ence ope a o , is a
(n×n)
ma ix o long- un mul iplie s and
[
Γi
]k−1
i−1
is a
(n×n)
ma ix o coe icien s ha con ain he sho - un esponses among a iables.
(1)
TIM
=E
+1(
T
IM
)
+E
S
(2)
M
=E
+1(
M
)
+𝛼
1
E
S
+E
M
(3)
𝛾
GDP
=E +1
(
𝛾GDP
)
+𝛽1ES
+𝛽2EM
+E
M
(4)
NEER
=E +1
(
𝛾GDP
)
+𝛾1ES
+𝛾2EM
+𝛾3ED
+E
E
(5)
PIPI
=E
−1(
P
IPI
−1)
+𝛿
1
E
S
+𝛿
2
E
M
+𝛿
3
E
D
+𝛿
4
E
E
+E
IPI
(6)
PPPI
=E
−1(
P
PPI
−1)
+𝜃
1
E
S
+𝜃
2
E
M
+𝜃
3
E
D
+𝜃
4
E
E
+𝜃
5
E
IPI
+E
PPI
(7)
P
CPI
=E −1
(
PCPI
−1
)
+𝜂1ES
+𝜂2EM
+𝜂3ED
+𝜂4EE
+𝜂5EIPI
+𝜂6EPPI
+E
CPI
(8)
Z
=a0D +
k
∑
i=1
Ai+Z −1+u
(9)
Δ
Z =a0D +
k
∑
i=1
ΓiΔZ −i+∏Z −k+u

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In his s udy,
Z
=
(
LCPI,LIPI,LPPI,LM2, LIM,LNEER,LGDP
)�
, a
(7×1)
ec o o
I(1)
a iables
a e conside ed as endogenous.
The da a employed o he s udy a e mon hly se ies o CPI, IPI, PPI, NEER, M2, GDP, and IM, ep e-
sen ing in la ion, impo p ice index, p oduce ’s p ice index, nominal e ec i e exchange a e, money
supply, domes ic demand and o eign supply, espec i ely.
The ime span chosen o analysis is om 2003 o 2012. Th ee majo in la ions happened du ing
his pe iod. The da a sou ces o a iables a e lis ed in Table 1. Unless o he wise s a ed, all a iables
ha e been adjus ed seasonally using X12 me hod and adop ed he na u al loga i hm in o de o
elimina e he in luence o seasonal in e up ions and he e oscedas ici y (Findley, Monsell, Bell, O o,
& Chen, 1998). Table 2 p esen s summa y s a is ics on he key a iables used in his analysis.
The es ima ion p ocess is o ganized as ollows: i s ly, i p esen s he uni oo es esul o he
s abili y o all a iables. Secondly, i shows he co-in eg a ion ela ionship examined by he Johansen
co-in eg a ion es . A las , a VEC model will be se up o analyze he impulse esponse and a iance
decomposi ion.
4.1. Uni oo es
To de e mine he app op ia e speci ica ion o he VAR es ima e, he a iables mus be es ed o he
s a iona y p ocess. The p esence o non-s a iona y beha io in he au o eg essi e ep esen a ion o
he a iable, i.e. he o de o in eg a ion o each a iable is de e mined using he Augmen ed
Dickey-Fulle (ADF) uni oo es . The esul s o he ADF es s o six key a iables e eal ha GDP,
IPI, IM and PPI a e s a iona y a le el (I(1)p ocess), while CPI, M2 and NEER a e i s di e ence s a-
iona y (I(2) p ocess) a 10% c i ical alue (Table 3).
Table 2. Summa y s a is ics o key a iables
Va iables Obse a ion Mean S d. de . Minimum Maximum
LCPI 120 4.6347 0.0227 4.5870 4.6886
LIPI 120 4.6642 0.0955 4.3770 4.8097
LPPI 120 4.6346 0.0407 4.5196 4.7014
LNEER 120 4.5521 0.0708 4.4308 4.6818
LM2 120 12.961 0.4929 12.155 13.789
LGDP 120 2.6473 0.2928 1.6864 3.1442
LIM 120 6.6628 0.4805 5.4714 7.4242
Table 1. Va iable desc ip ion and da a sou ce
Va iables Desc ip ion Rep esen a ion Sou ce
CPI Consume p ice index
(2005 = 100)
Domes ic in la ion Na ional Bu eau o S a is ics
IPI Impo p ice index (2005 = 100) P ice le els o impo ed
p oduc s
Na ional Bu eau o S a is ics
PPI P oduce p ice index
(2005 = 100)
P oduce ’s manu ac u ing cos Na ional Bu eau o S a is ics
NEER Nominal e ec i e exchange
a e (2005 = 100)
Exchange a e le el IMF In e na ional Finance
S a is ics
M2 B oad money (in million US
dolla s)
Domes ic money supply Na ional Bu eau o S a is ics
GDP G oss domes ic p oduc (using
indus ial alue-added as p oxy
a iable)
Agg ega e domes ic demand Na ional Bu eau o S a is ics
IM China’s o al impo om he
wo ld (in million US dolla s)
Fo eign supply Na ional Bu eau o S a is ics
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4.2. Co-in eg a ion es and VEC model se up
Conside ing his s udy is o analyze he pass- h ough o he a iables ela ed o exchange a e ola-
ili y o he domes ic p ice le el, all hese mac o-economic a iables migh sha e he same long- un
end and a ec each o he in a ce ain way, o while hey exhibi sho un luc ua ions a ound a
long un equilib ium. The ADF es indica es ha all he ime se ies a e no s a iona y a he same
le el. A Johansen co-in eg a ion es was employed in o de o p o e he e is co-in eg a ion ela ion-
ship be ween hese a iables. Table 4 shows ha he e a e a leas wo co-in eg a ing equa ions a
he 5% le el o bo h ace es and maximum eigen alue es . The e o e, CPI, GDP, IPI, PPI, NEER,
M2, and IM we e included o es ablish a VEC model. The esul shows ha all he in e se oo s a e
less han one, i.e. all he model oo s a e loca ed in a uni ci cle (see Figu e 1), and he op imal lag
pe iod is 2 acco ding o AIC esul (Table 5). The model is s able so ha we can p oceed o impulse
esponse and a iance decomposi ion analysis.
4.3. The dynamic beha io o he VEC model
The dynamic beha io o he VEC model is analyzed using he impulse esponse unc ion and a i-
ance decomposi ion.
4.3.1. Impulse esponse unc ions
The impulse esponse unc ion (IRF) aces he e ec o a one-s anda d-de ia ion shock in a a iable
on cu en and u u e alues o o he s in he sys em. I indica es he size and cha ac e is ics o he
shock’s e ec s. I is well known ha he esul s o he IRF and a iance decomposi ion based on
Cholesky’s decomposi ion a e sensi i e o he o de o he a iables and he lag leng h. A VEC model
Table 4. Johansen co-in eg a ion es esul
No es: T ace es indica es 2 co-in eg a ing equa ion(s) a he 0.05 le el. Maximum eigen alue es indica es 2 co-in eg a ing equa ion(s) a he 0.05 le el.
*Rejec ion o he hypo hesis a he 0.05 le el.
Un es ic ed co-in eg a ion ank es ( ace) Un es ic ed co-in eg a ion ank es (maximum eigen alue)
Hypo hesized
No. o CE(s)
Eigen alue T ace
s a is ic
0.05 C i ical
alue
P ob. Hypo hesized
No. o CE(s)
Eigen alue T ace
s a is ic
0.05 C i ical
alue
P ob.
None* 0.4239 166.6119 125.6154 0.0000 None* 0.4239 62.3116 46.2314 0.0005
A mos 1* 0.3087 104.3002 95.7537 0.0113 A mos 1* 0.3086 41.7067 40.0776 0.0325
Table 3. ADF uni oo es esul s
No e: Lag leng h selec ion is au oma ically based on Schwa z in o C i e ion (SIC), maxlag=13.
*Rejec ion o he null hypo hesis (a a iable has a uni oo ) a 1% signi icance le el.
**Rejec ion o he null hypo hesis (a a iable has a uni oo ) a 5% signi icance le el.
Va iables A le el 1s di e ence
- alue (cons an
and end)
p- alue (cons an
and end)
- alue (cons an
and end)
p- alue (cons an
and end)
LCPI −2.3969 0.3793
LGDP −3.6875** 0.027
LIPI −4.2898* 0.0046
LPPI −2.5369** 0.0101
LM2 −2.5369 0.3101
LNEER −3.1075 0.1094
LIM −5.6911* 0.0000
LCPI −12.3134* 0.0000
LM2 −5.5843* 0.0000
LNEER −7.4046* 0.0000
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can be used o es he G ange causali y among a iables o he model, i.e. o asce ain he di ec-
ion o causali y. G ange ’s de ini ion o causali y is a widely used concep o causali y (G ange ,
1969). In o de o analyze he causal ela ionships be ween CPI and he o he a iables, a G ange
causali y es wi hin a VEC amewo k is used. The es es ima es he χ2 alue o he coe icien on
he lagged endogenous a iables. The null hypo hesis is ha he lagged explana o y a iables o he
model and also hei join signi icance do no G ange -cause he dependen a iable. Table 6 epo s
p- alues o F-s a is ics a lag h ee o he VEC G ange causali y es esul s.
The esul s p esen ed in Table 6 indica e ha among six a iables, excep dLM2 o he s do “cause”
CPI indi idually and he six a iables in luence CPI join ly. Combined wi h he G ange Causali y Tes
esul s and he economic eali y, we can decide he Cholesky o de as ollows: GDP, M2, NEER, IM, PPI,
IPI, CPI.
Figu e 2 shows a shock on NEER has nega i e e ec on CPI. The impac om exchange a e o he
CPI added up g adually and eached he highes poin o 0.36% a ound he se en h mon h. A shock
on PPI has posi i e e ec on CPI. The e ec is quick ( eaches he highes poin in he i s mon hs a
abou 0.36%) and hen aded ou o 0.1% a e 12 mon hs. A shock om impo ed p ice also has
posi i e e ec on CPI. I eaches he highes poin a e 3 mon h o 0.18% and slowly ades ou . The
esul s show a sho - e m pass- h ough channel om exchange a e o he CPI h ough impo ed
p ice and PPI. This is he i s empi ical e idence ha ou hypo hesis con o ms o eal-wo ld ends.
Figu e 1. In e se oo s o AR
cha ac e is ic polynomial.
Table 5. VAR lag o de selec ion c i e ia
No es: FPE: Final p edic ion e o ; AIC: Akaike in o ma ion c i e ion; SC: Schwa z in o ma ion c i e ion; HQ: Hannan-
Quinn in o ma ion c i e ion.
*Lag o de selec ed by he c i e ion: LR: sequen ial modi ied LR es s a is ic (each es a 5% le el).
Lag LogL LR FPE AIC SC HQ
0 1,006.884 NA 4.15E-17 −17.855 −17.685 −17.786
1 1,870.569 1,603.985 2.00E-23 −32.403 −31.044* −31.852
2 1,962.539 159.305 9.37E-24* −33.170* −30.622 −32.136*
3 1,996.708 54.914 1.25E-23 −32.905 −29.168 −31.389
4 2,044.992 71.565* 1.33E-23 −32.893 −27.965 −30.894
5 2,081.281 49.250 1.81E-23 −32.666 −26.549 −30.184
6 2,124.255 52.949 2.29E-23 −32.558 −25.252 −29.594
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Figu e 3 shows ha a shock on NEER has nega i e e ec on CPI, IPI, and PPI. The impo ed p ice
index has been mos a ec ed by he changes in he exchange a e compa ed wi h he o he wo
p ice indices. The e ec inc eases g adually and eaches he highes poin a e 7 mon hs (2.2%). I
indica es he pass- h ough e ec om exchange a e o impo ed p ice le els is ela i ely smoo h.
The NEER has he smalles impac on he CPI (0.27% a he highes ). I explains he exchange a e
pass- h ough o domes ic in la ion is impe ec and lagged-behind.
4.3.2. Va iance decomposi ion
Va iance decomposi ion measu es he pe cen age o he o ecas a iance in in la ion. This a iance
can be a ibu ed o shocks o changes o each explana o y a iable o e a se ies o ime ho izons.
In addi ion, i shows how his p opo ion changes o e ime. The a iance decomposi ion in CPI o
he VEC model by using he Cholesky decomposi ion me hod is epo ed in Table 6 ( he Cholesky
o de has been discussed ea lie ).
The a iance decomposi ion esul s suppo he empi ical indings om he impulse esponse
unc ions (Table 7). CPI i sel caused 60% o i s a iabili y. Then ou side supply shocks (IM) accoun s
o almos 16% o he CPI’s a iance a e en lags, which is he highes among all hese explainable
a iables. Exchange a e (NEER) claims 9.6% o he CPI’s a iance a he end o 10 mon hs which is
suppo ed by he IRF es as well. PPI has a signi ican con ibu ion o CPI’s a iance in he beginning
(15.6%) bu he pe cen age dec eases g adually. IPI makes up 2% o CPI’s a iance in he i s mon h
and keeps a s eady pe cen age o 1.75% a e 10 lags. M2 has immedia e in luence in he second
Figu e 2. Response o LCPI o
Cholesky one S.D inno a ions.
Table 6. VEC g ange causali y/block exogenei y wald es esul s-p obabili ies
*Rejec ion o he null hypo hesis (a a iable has a uni oo ) a 1% signi icance le el.
**Rejec ion o he null hypo hesis (a a iable has a uni oo ) a 5% signi icance le el.
Dependen
a iables
Excluded a iables (d =3)
dLCPI dLNEER dLIPI dLPPI dLM2dLGDP dLIM Block
exogenei y
(d =10)
dLCPI 0.01* 0.01* 0.01* 0.34 0.00* 0.07* 0.00*
dLNEER 0.48 0.63 0.01* 0.75 0.65 0.26 0.21
dLIPI 0.86 0.44 0.00* 0.64 0.84 0.35 0.00*
dLPPI 0.03** 0.00** 0.14 0.77 0.05** 0.48 0.00
dLM2 0.02** 0.36 0.26 0.20 0.62 0.40 0.18
dLGDP 0.60 0.55 0.63 0.92 0.18 0.56 0.29
dLIM 0.25 0.77 0.13 0.23 0.00* 0.86 0.00*