CESSA WP 2015-09

Regional or Global Shock?
A Global Vector Autoregressive Analysis of
Asian Monetary Integration

Sheue Li Ong
Kiyotaka Sato
Yokohama National University
September 2015

Center for Economic and Social Studies in Asia (CESSA) Working Paper
Downloadable from:http://www.econ.ynu.ac.jp/cessa/publication/workingpaper.html

Center for Economic and Social Studies in Asia, Department of Economics
Yokohama National University

Regional or Global Shock?
A Global Vector Autoregressive Analysis of Asian Monetary Integration

Sheue Li Ong
Graduate School of International Social Sciences
Yokohama National University79-3 Tokiwadai, Hodogaya-ku, Yokohama, 240-8501, Japan
Email: ongsheueli@hotmail.com

Kiyotaka Sato
(Corresponding Author)
Department of Economics
Yokohama National University79-3 Tokiwadai, Hodogaya-ku, Yokohama, 240-8501, Japan
Email: sato@ynu.ac.jp

September 2015

Acknowledgement
This study is financially supported by Yokohama National University and the JSPS (Japan
Society for the Promotion of Science) Grant-in-Aid for Scientific Research (A) 24243041 and(B) 24330101.

1Regional or Global Shock?
A Global Vector Autoregressive Analysis of Asian Monetary Integration

Abstract
This study employs the global vector autoregressive (GVAR) model to empiricallyinvestigate the viability of regional monetary arrangements in Asia. Although numerous
studies have been conducted on the feasibility of Asian monetary integration/union,
whether regional co-movements of real outputs and other macroeconomic variables are
driven by external shocks or self-sustaining development in Asia has not yet been
rigorously demonstrated. The novelty of this study is to employ the GVAR model,
which allows global inter-linkages between domestic and foreign variables, toinvestigate the generalized impulse responses of Asian economies’ real outputs andother macroeconomic variables to global and regional shocks. In marked contrast to
previous studies, we found that a regional shock, i.e., China’s output and inflation
shock, exerted more influence on Asian economies than a global (U.S.) shock. Anotherregional shock, i.e., a Japanese shock, had a far smaller influence on Asian economies.
The relative importance of regional shocks originating from China needs to beconsidered when establishing a regional monetary arrangement in Asia.
Keywords: Global vector autoregressive (GVAR) model; global shock; regional shock;monetary integration; optimum currency area; Asia
JEL Classification: C32; C53; F15; F33

2

1. Introduction
The feasibility of forming a regional economic and monetary union in Asia hasgained considerable attention over the last several decades against a backdrop of
growing intra-regional trade and investment.1 The 1997–1998 Asian financial crisisheightened calls to establish regional monetary and financial cooperation among
regional economies. After the 2008 collapse of Lehman Brothers, Asian currencies
substantially and asymmetrically fluctuated. While the Japanese yen appreciatedsharply against almost all currencies, the Korean won began to drastically depreciate.
Such large and asymmetric exchange rate responses changed export pricecompetitiveness between Asian economies, which may inhibit region-wide steadyeconomic growth.2 Thus, regional exchange rate stability has been an important policy
agenda for the further growth and development of regional economies.
A large number of studies have also analyzed regional exchange rate stability
and possible monetary cooperation and arrangements in Asia. These studies typically
rely on the theory of optimum currency area (OCA) to investigate whether it makes
economic sense for Asian economies to adopt a regional monetary arrangement. The
OCA theory suggests several preconditions to forming a currency area, and existingstudies have mostly investigated business cycle synchronization and symmetry infundamental shocks as one of major OCA preconditions. 3 Most studies, such as
Bayoumi et al. (2000), Zhang et al. (2004), Bacha (2008), Allegret et al. (2012), andLee and Koh (2012), employ a vector autoregressive (VAR) model to analyze thedegree of symmetry in shocks among Asian economies. However, this approach cannot
explain whether growing similarity in real output fluctuations are driven by external
shocks or self-sustaining development in Asia.
Chow and Kim (2003) examined the relative importance of global, regional, andcountry-specific shocks for Asian economies using variance decomposition tests basedon a structural VAR analysis. If business cycle co-movements are mainly affected by
1

Ferrarini (2013) analyzes recent trade network development in Asia. Kwon and Ryou (2015)
investigate value-added trade and vertical specialization focusing on Asia.
2
See, for instance, Sato et al. (2013) and Ito and Shimizu (2015) for an analysis of export pricecompetitiveness of Asian economies.
3
The OCA theory typically suggests the following preconditions: economic openness and tradeintegration; business cycle synchronization and symmetry of fundamental economic shocks;
financial integration; and factor market integration including free labor mobility.
3

regional shocks, a common monetary policy can be an effective tool for regional
economies. In contrast, if country-specific shocks are prevalent in the region, regional
economies need to adopt independent monetary policies. If regional output
co-movements are driven largely by global shocks, global arrangements need to beconsidered when establishing regional monetary coordination.
Hsu (2010) extended the Chow and Kim (2003) approach by constructing
weighted average macroeconomic variables as a proxy for regional variables. Sato et al.
(2011) and Dungey and Vehbi (2015) employed the structural VAR method to compare
the degree of regional influence between global and regional shocks according to an
impulse response function analysis. Assuming Chinese and Japanese shocks as regional
shocks, these studies found that a global (U.S.) shock has a greater regional influence
on Asian economies than does regional shocks. However, these standard VAR models
can only deal with a relatively small number of variables and interactions between alimited number of variables. To capture the complicated international linkages between
variables, the model needs to include either higher-order time lags or a large number ofdomestic variables, but it then cannot avoid a serious dimensionality problem.
Furthermore, it is important to note that the above VAR models maintain aclosed-economy assumption, which fails to capture international linkages ofendogenous variables across countries.
Recent business cycle studies, such as Lee and Azali (2012) and Hirata et al.
(2013), employ the dynamic factor model and have found that regional factors play amore important role in Asia than global factors in explaining fluctuations inmacroeconomic variables. While the estimated unobserved factors are assumed tosummarize the empirical content of a large number of macroeconomic variables, Deeset al. (DdPS, 2007) noted that the dynamic factor model’s results are subject to theidentification problem of unobserved factors, especially when making economic
interpretations.4 To assess the source of macroeconomic fluctuations more rigorously,
it is necessary to rely on a far more detailed global model and framework.
To overcome the methodological limitation of the standard VAR and dynamic
factor models, Pesaran et al. (2004), thereafter modified by DdPS (2007), and Dees et
4

DdPS (2007) emphasized that “even when all such ‘common’ factors are taken into account, there will
be important residual interdependencies due to policy and trade spillover effects that remain to be
explained” (page 3).

4al. (DPSS, 2014) developed a global VAR (GVAR) model. The associated GVAR
model is literally a global model that allows global inter-linkages between domestic andforeign variables. The GVAR modeling approach has a number of attractive features:
1. This approach allows interdependence at various levels, including national
and international levels, because the lags of all variables enter individual
equations and the reduced-form errors can be cross-sectional dependent.
2. It allows for both long-run and short-run relationships consistent with thetheory and data.
3. It solves the dimensionality problem in which both the cross-section
dimension N and time-series dimension T can be relatively large as a resultof estimating the country-specific error-correction models (ECMs)
separately.
These features are important because they provide a global modelingframework for quantitatively analyzing the relative importance of different shocks andtransmission channels. Thus, using the GVAR model offers a strong advantage inexamining the feasibility of forming a regional monetary arrangement in Asia.
Various studies have applied the GVAR model to the question of forming amonetary union. DdPS (2007) applied the GVAR model to the analysis of international
linkages in the Euro area. Pesaran et al. (2007) empirically investigated theconsequences of a scenario in which the U.K. adopted the Euro in 1990. Fielding et al.
(2012) identified the channels through which macroeconomic innovations in one
country affect other countries in the West African Economic and Monetary Union(UEMOA).
The objective of this study is to investigate the feasibility of a regionalmonetary arrangement in Asia by using a GVAR model to evaluate whether a global orregional shock exerts greater influence on Asian economies. We compare the impulse
responses of macroeconomic variables, such as real output, inflation rates, and interest
rates in Asia, to both global and regional shocks. Recently, Feldkircher and Korhonen(2014) employed the GVAR model to assess the degree of China’s economic influenceon various regions and countries and surprisingly found little Chinese influence on
Japan and the entire Asian region. In marked contrast, we demonstrate that fluctuations
in macroeconomic variables are more affected by a regional (Chinese) shock than by aglobal (U.S.) shock.
5Specifically, by applying the GVAR model rigorously to the OCA question toallow global inter-linkages between domestic and foreign variables, we revealed that
Asian economies tend to show significantly positive real output responses to Chinese
output shock, while responses to a Japanese output shock are far less statisticallysignificant. Such asymmetric responses are likely due to Japan’s unilateral dependence
on Asian economies, in that Japan does not import much from Asian economies, while
Japanese exports to Asia have increased. Asian real outputs showed a significant but
relatively short-lived response to the U.S. shock, which indicates China’s growing
influence on Asian economies compared with the U.S. influence. While strong financial
linkages are still observed between the U.S. and Asian economies, China’s influence inAsia surpasses that of the U.S. in terms of real output and inflation shocks, which needs
to be considered when determining whether to establish a regional monetaryarrangement in Asia
The remainder of this paper is organized as follows. Section 2 reviews themethodology of the GVAR analysis. Section 3 describes the data and empiricalapproach. The empirical results are presented and discussed in Section 4. Finally,
Section 5 concludes this study.

2. Methodology
The GVAR approach can be briefly summarized as a two-step approach.5 First,
individual country-specific augmented models are estimated as being conditional on therest of the world. In this step, all domestic macroeconomic variables are related not
only to corresponding foreign variables constructed to match the international tradepattern of the country under consideration but also to dominant variables that can
influence the remaining variables in the model directly and indirectly, but not vice versa.
The country-specific foreign variables and the dominant variable are treated as weakly
exogenous (or long-run forcing) for most economies when the number of countries N is
sufficiently large and the idiosyncratic shocks are weakly correlated. Second, theindividual country models are combined in a consistent and cohesive manner to form a
global model. The combined model is then used to generate forecasts or impulse
5

The following exposition of the empirical methodology is based on DdPS (2007) and Chudik andPesaran (2015).

6

response functions for all world economy variables simultaneously. Smith and Galesi(2014) provided a toolbox for constructing GVARs.

2.1 Country-Specific Models
1 countries in the global economy, indexed byAssume that there are

0,1,2, ⋯ , , where 0 serves as a reference country. For each country ,of domestic variables,∗

are related to the

1 vector of dominant unit

1 vector∗, , the1 vector of foreign variables, and the deterministic variable time trends

1,2, ⋯ , . This augmented VAR model is denoted as VARX* and expressed as

,

where

,

,

∑ℓ

,

,ℓ

,

Λ ,ℓ ∗,

∑ℓ

ℓ

ℓ

∑ℓ

,ℓ

ℓ

1 vector of idiosyncratic country-specific shocks;

is amatrices of lagged coefficients; Λ

,ℓ

∗

are

associated with the foreign-specific variables; and D

,ℓ

,

,ℓ

(1)

are

matrices of coefficientsarecoefficients associated with the common variables. The lag orders

matrices of
,

, and

of

the domestic, foreign, and dominant variables, respectively, are selected using the
Akaike information criterion (AIC).
The set of country-specific foreign variables
weights, , asbetween country

∗
,

∑

,

, . Specifically,

and country

trading partners, where

,

,

∗
,

is built using fixed tradeare calculated as the total trade

divided by the total trade of country
0 and ∑

with all of its

1 for all . The trade weights are,important for accommodating the effects of external shocks that could pass through allcountries’ output via trade channels. The set of country-specific foreign variables
represents the dynamics of global economic variables, which are assumed to affect andshape Asian countries’ macroeconomic variables.
Assume that the idiosyncratic shocks
and nonsingular covariance matrix Σ
idiosyncratic shocks are denoted as,,ℓare serially uncorrelated with mean 0, where0, Σ .

,

7

,ℓ

ℓ ,

. The

2.2 Dominant Variables
In modeling the dominant variable, a possible cointegration among theis first checked using the Johansen procedure. Consider the following

elements ofspecification for the dominant model:
∑ℓ

ℓ

(2)

ℓ

which can be equivalently written in the ECM as

Δ

∑

1

∑ℓ

where

ℓ ,

and

Γ Δ

are

vectors,

number of cointegrating relationships, Γthe lag length

(3)

,ℓ

,ℓ

denotes the

⋯

,ℓ

, and

is selected by the AIC information criterion. For cases in which

1 variables, Eq (3) clearly allows cointegration among the dominantcontainsvariables.

To allow for feedback effects from the variables included in the GVAR modelback to the dominant variables via cross-section averages, Eq (2) can be augmented
with lagged changes of the variables in the rest of the GVAR model,
whereis the1 vector of the variables included in the models of the∑non-dominant variables (defining,), andis anmatrix of weights

global cross-section averages:
∑ℓ

ℓ

∑ℓ

ℓ

Λ ℓ

,

(4)

ℓ

Assuming there is no cointegration among the common variables
cross-section averages

Δ

where
values of Δ

and theℓ , Eq (4) can be written as

1

∑

Γ Δ

∑

Δ

,

ℓ

(5)

is consistently estimated by least squares. Note that contemporaneous
do not feature in Eq (5).
8

2.3 Building the Global Vector Autoregressive Model (GVAR)
The conditional country-specific model Eq (1) and the marginal model Eq (5)can be combined and solved as a complete global VAR model.
To construct the GVAR model from the country-specific models, first define the∗

1 vector

∗
,

, ,

,

. Assuming for simple exposition that,Eq (1) can be rewritten as

,

where

,

,

, Λ,

,
∗

are

∑

,

and

,ℓ

ℓ

for ℓ

(6)

,

ℓ

,

1, ⋯ ,,

has a full row rank, namely rank

,

,ℓ
,

,Λ

,ℓ

ℓ

. Both,ℓmatrices, and

∑

ℓ

,

,ℓ

Second, collect all the country-specific variables together in the′vector

′

, ,

,ℓ

.
1 global

′

′

, ,⋯,

and

∑

, where,is the total number of theendogenous variables in the global model. Then, the country-specific variables can all
be written in terms of
,

,

where

, as
0,1,2, ⋯ ,
∗

is a

(7)

matrix of fixed constants defined in terms of thecountry-specific weights, . The matrix

links all country-specific and foreign

variables in the system. Because no subscript
countries in the system are stacked in, variables for allis attached to.

Third, substituting Eq (7) into Eq (6) yields:,

where both

,

,

∑ℓ

,

and

,ℓ

,ℓ

ℓ

are

∑ℓ

,ℓ

,

ℓ

, ,

dimension matrices. Stacking theseequations yields a “global” solution:

9

∑ℓ

ℓ

∑ℓ

ℓ

ℓ

(8)

ℓ

now appear on thewhere both the contemporaneous and lagged values ofmaxright-hand side of Eq (8) with

,

max

, and

,

⋮

ℓ

,

⋮

ℓ

ℓ

,

⋮

⋮

,

⋮

.

ℓ

1 vector

Defining the,′, then Eq (4) and Eq (8) for

can be written as
∑ℓ

ℓ

(9)

ℓ

where
0
ℓ

,

Finally, because

,

is a

,

ℓ

ℓ

ℓ

Λ ℓ
ℓ

,

.

dimensional matrix and will be a full rank, it isa nonsingular matrix. Therefore, the GVAR model in all the variables can be expressed
as
∑ℓ

ℓ

ℓ

(10)

where
0
ℓ

which is a block lower triangular matrix showing the causal nature of the dominantvariables. Eq (10) can be solved recursively forward to obtain the future values of.

3. Empirical Application10

3.1 Countries and Regions
To have a sufficiently long time series for reliable statistical inference, thisstudy uses quarterly data over the period from 1990Q1 to 2013Q4 for 20 countries fromdifferent regions of the world (see Table 3.1). The eight countries in the Euro area aregrouped together and treated as a single economy. The following ten Asian economies
are included: China, Hong Kong, Indonesia, Japan, Korea, Malaysia, the Philippines,
Singapore, Taiwan, and Thailand.

[Insert Table 3.1 around here.]

The regional variables are constructed from country-specific variables using thefollowing weighted averages:
∑

where

,,

,

indicates a variable of country,, ,in region , and,is the aggregation

weight.

3.2 Variables
The choice of variables in this study follows Pesaran et al. (2004) and DdPS
(2007): real output measured by real gross domestic product (RGDP); inflation rate(INF) measured by/∗ 100, where CPI is the consumer priceindex; money market interest rate measured by interest rate (IR); and real effective
exchange rate (REER). The RGDP is used as a proxy for real output, INF is used as theproxy for the general price level, and the IR is used as the proxy for money markets.
The REER is included to capture the multi-country nature of the analysis. The price ofoil is included to account for possible common factors. All these variables are
expressed in natural logarithms except IR, which is expressed as a percentage.
Seasonality is adjusted using the Census X12 method for all variables except REER andIR.
The country-specific foreign variables are built using the fixed trade weights,

based on the share of trade (exports plus imports). The regional variables are built
11using the aggregation weights based on the purchasing power parity’s adjusted GDP
series (PPP-GDP) weights. Both weights are constructed with annual data computed
over the sample period of 1990–2013. The data source for each variable is reported inTable 3.2.

[Insert Table 3.2 around here.]
0,1,2, ⋯ , , the country-specific domestic

For country∗,

variables,,and foreign

are
, ,

,

, ,

, ,

,

′ and

∗
,

∗
, ,

∗
, ,

∗
,

′.

The country-specific foreign variables are defined as
∗
,

where

∑

,

,

0 and ∑

, ,

,

∗
,

∑

,

,

and

∗
,

∑

,

, ,

1. The matrix of the trade weights is presented inTable 3.3.

[Insert Table 3.3 around here.]This study treats the oil price as the globally dominant variable. The global
dominance of the oil price implies that idiosyncratic shocks to the oil price would havea non-negligible effect on potentially any country in the world while the effect of asmall economy on the oil price is negligible. Therefore, oil price effectively becomes a
dynamic common factor for other economies. In this study, RGDP and INF are the two
feedback variables selected to enter the augmented ECM.

3.3 Estimation of the Country Models
The GVAR model assumes foreign and dominant variables are weakly
exogenous and the parameters are stable over time. Under weak exogeneity, theparameters of the country-specific models can be estimated consistently using the
reduced-rank estimation procedure. However, the Johansen (1988, 1995) reduced-rank12estimation procedure treats all the variables in the model as endogenous I(1). Thus, thisstudy estimates the individual VARX* models using the modified technique developed
by Harbo et al. (1998) and Pesaran et al. (2000). Following the estimation procedure,
we first conduct the cointegration test and then estimate the individual country models
subject to the reduced rank restrictions. We then derive the corresponding ECM and,
finally, use the ECM to conduct weak exogeneity tests.

3.3.1 Integration Properties of the Series
To select appropriate transformations of the domestic and foreign variables forinclusion in the country-specific cointegrating VAR models, the integration propertiesof the individual series under consideration are examined. Because the traditional
Dickey-Fuller (DF) test has poor power performance in small samples, this study
employs the Weighted Symmetric Augmented Dickey-Fuller (WS-ADF) test introduced
by Park and Fuller (1995). The WS-ADF unit root test uses the time reversibility ofstationary autoregressive processes to increase their power performance (Leybourne etal., 2005; Pantula et al., 1994). The lag length employed by the WS-ADF unit root test
is selected using the Akaike Information Criterion (AIC). Because quarterly data isemployed, this study sets the maximum lag length to four.
The WS-ADF test results for the level and first differences of all
country-specific domestic, foreign, and global variables in the GVAR model arereported in Table 3.4, which shows that most of the variables are integrated with order 1or I(1). To avoid over-differencing and efficiency loss in the remaining countries, all thevariables are treated approximately as I(1).

[Insert Table 3.4 around here.]

3.3.2 Rank of Cointegration SpaceFor each country model, the corresponding cointegrating VAR model isestimated and the rank of the cointegrating space is identified. Initially, the order of theindividual country VARX ∗variables’ lag order and,models are selected, whereis the domesticis that of the foreign variables in the VARX* models. The

variables’ lag order is selected according to the AIC. Due to data limitations, thedomestic variables have a maximum lag order of two, while that for foreign variables is13set to one.
Then, the cointegration rank is derived by employing the trace test and theasymptotic 5% critical values taken from MacKinnon et al. (1999). The deterministicsof the VARX* models were unrestricted intercept and restricted trend. Table 3.5
provides the orders of the VARX* models and the number of cointegrating
relationships. The cointegrating relationships can be interpreted as long-run
relationships among the domestic variables and between the domestic and foreign
variables.

[Insert Table 3.5 around here.]

3.3.3 Weak Exogeneity Test
The weak exogeneity of variables is tested using weak exogeneity tests from
Johansen (1992) and Harbo et al. (1998). The F-statistics for testing the weakexogeneity of all country-specific foreign and global variables are summarized in Table
3.6. No weak exogeneity assumptions can be rejected for most variables, where only 5of 52 exogeneity tests were statistically significant. Thus, the analysis was re-estimatedby assuming those five variables as endogenous, which showed that it did not affect the
number of cointegrating relationships in the model. Therefore, the variables are treatedas exogenous throughout the GVAR model.

[Insert Table 3.6 around here.]

4. Empirical Results
In this section, we study the dynamic properties of the GVAR model to assess
the time profile of the effects following various shocks. The purpose of this empiricalanalysis is to evaluate the extent to which the Asian integration process has been driven
by external or regional shocks based on the impulse response function analysis. In thisstudy, the U.S. is considered a possible source of the global shock, while Japan and
China are considered possible regional shock sources for Asian economies. We
consider the following one standard error positive shocks: (1) real output shock, (2)inflation shock, (3) interest rate shock, and (4) oil price shock. This study employs the14generalized impulse response function (GIRF), which is invariant to the variables’ orderand to the countries in the model, which is especially important in a large
macroeconomic system. We display the results of GIRF analysis over 20 quarters (i.e.,
5 years), with the bootstrap estimates of the GIRF and their associated 90% confidencebounds.

4.1 Real Output Shocks
We first analyze the impulse responses of real outputs to a one standard error
positive real output shock originating from China (Figure 4.1), Japan (Figure 4.2), andthe U.S. (Figure 4.3), which are equivalent to a positive rise in the real output of China,
Japan, and the U.S., respectively.
First, in Figure 4.1, all Asian economies exhibit positive and significant real
output responses to the Chinese real output shock except the Philippines, where theimpulse response is significantly positive only for the first few periods. This result
indicates that most Asian economies tend to be positively affected by a shock from
China, likely reflecting China’s role as a regional production hub and the growing
regional trade between China and neighboring economies. It is interesting to note that
not only the U.S. but also the Euro area shows a significantly positive response of real
outputs to Chinese real output shock. This result is consistent with the findings of Satoand Shrestha (2014) and Amador et al. (2015), which demonstrate China’s strong
participation in global value chains and the growing dependence of the U.S. andEuropean countries on China for their intermediate input imports.
Second, all Asian economies show positive and significant real output
responses to a Japanese real output shock only for an initial period. Subsequently, thesecountries’ impulse responses rapidly become small and insignificant. The impulse
responses of the U.S. and Euro area are not statistically significant at all. Thus, Japan’s
real output shock has a surprisingly small effect on Asian economies, likely because
Japan is not a major export market for Asian economies compared to China. Sato andShrestha (2014) empirically investigated trade and production linkages based on a
globally linked input–output table and demonstrated that Japan’ import-dependence on
Asian economies for intermediate inputs is far smaller than the degree of Asian
import-dependence on Japan, whereas Asian economies have a substantially highimport dependence on Japanese intermediate goods.
15Third, as found in the previous studies, a U.S. real output shock hassignificantly positive influences on Asian economies. In Figure 4.3, all Asian
economies can be seen to respond positively to the U.S. real output shock for at least
the first two years. A comparison between Figures 4.1 (Chinese real output shock) and
4.3 (U.S. real output shock) shows that the degrees of impulse response by real outputs
are mostly similar in all Asian economies except the Philippines. However, the periodof statistically significant response to Chinese real output shock is clearly longer thanthe corresponding responses to the U.S. real output shock. In terms of a real output
shock, China’s regional influence becomes comparable to, or even stronger than, theU.S. influence, which contrasts markedly with the findings of Sato et al. (2011),
Feldkircher and Korhonen (2014), and Dungey and Vehbi (2015).

4.2 Inflation Shocks
To further assess the degree of China’s economic influence on Asian
economies in comparison with the U.S. influence, we generated two additional shocks:
a one standard error positive inflation shock from China (Figure 4.4) and the U.S.
(Figure 4.5) to inflation, respectively.
In Figure 4.4, Hong Kong, Taiwan, Malaysia, and Korea can be seen to exhibit
positive and significant responses to Chinese inflation shock over the five-year time
horizon. Indonesia, Singapore, and Thailand also show significantly positive responses
to Chinese inflation shock for at least the first few quarters, but the responses of Japanand the Philippines are not statistically significant at all. Although not reported in thisstudy, we also attempted to estimate the impulse responses of Asian economies’ priceinflation to a Japanese inflation shock, which indicates that the response of Asian
economies’ price inflation is not statistically significant in most cases.
Figure 4.5 shows the impulse responses of Asian economies to a U.S. inflation
shock. It is interesting to note that the U.S. shock has a significantly positive effect on
Asian inflation only for the first one or two quarters in Hong Kong, Korea, Malaysia,
Thailand, and Taiwan. For other Asian economies, the U.S. shock has no significant
influences on domestic price inflation. In terms of the inflation shock, China has far
stronger influences than the U.S. on Asian regional economies.

4.3 Interest Rate Shocks16

In addition to the real and price linkages, it is necessary to assess the degree offinancial linkages. Under the GVAR framework, we investigate the degree of interest
rate shock transmission. Figure 4.6 shows that nominal interest rates of all Asian
economies respond positively and significantly to the U.S. interest rate shock at least
for the first several periods. In contrast, although not presented in this study, an interest
rate shock from either China or Japan has no significant effect on the nominal interest
rates in Asian economies, likely due to the China’s capital controls and Japan’s
near-zero interest rate policy since the end of the 1990s. In terms of financial linkages,
Asian economies are subject not to regional shocks but to global shocks originatingfrom the U.S.

4.4 Oil Price Shocks
Lastly, to assess the effects of an oil price shock, another possible source of
global shock, on Asian economies, a one standard error positive shock to oil prices isgenerated, and the effects of an oil price shock on real output (Figure 4.7) and inflation(Figure 4.8) are investigated. A one standard error positive shock results in a 0.1%
increase in the price of oil. Most real output of Asian economies first shows a positive
response but soon decreases sharply. The above responses are not statisticallysignificant for most Asian economies. On the other hand, although short-lived, an oil
price shock has a positive and significant effect on inflation in Asian economies.
Compared to its effects on real output, an oil price shock has stronger effects on Asian
economies by causing inflationary pressure. Furthermore, the short-lived responses toinflation support the finding of Galesi and Lombardi (2009) that inflationary effects ofoil price shocks are felt mostly in advanced countries, with less sizeable effects felt in
emerging economies.

[Insert Figures 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, and 4.8 around here.]

5. Concluding Remarks
The main goal of this study is to assess the feasibility of forming a regionalmonetary arrangement in the Asian region. By employing a GVAR model that covers
20 economies from all over the world, we generated two types of shocks, i.e., global
17

(U.S.) and regional (Japanese and Chinese) shocks. We investigated whether recent
regional economic growth and inflation dynamics are driven by external shocks or
self-sustaining development in Asia.
We found that Asian economies tend to show significantly positive and longer
responses to a Chinese real output shock than to a U.S. shock. While previous studies
found the Chinese economy has increasing importance in the global economy(Cesa-Bianchi et al., 2012; Feldkircher and Korhonen, 2014; Dreger and Zhang, 2014),
no studies have found that China’s influence surpass that of the U.S. in the context ofAsian monetary integration/union. By estimating a GVAR model that allows global
inter-linkages between domestic and foreign variables, we demonstrate that China’s
influence on the Asian economies are greater than the U.S. influence in terms of both
real output and inflation shocks, although the U.S. still has a greater financial effect on
Asian economies in terms of interest rate shocks. Furthermore Asian economies’
responses to a Japanese real output shock are far less statistically significant, which islikely due to Japan’s unilateral dependence on the Asian economies, in that Japan does
not import much from Asian economies.
The rising role of regional (Chinese) shocks in driving business cycles andinflation indicates that Asian economies meet some of the key preconditions inestablishing a regional monetary union. However, Asian economies are financiallyaffected by the U.S., in that nominal interest rates of Asian economies are significantly
influenced by a U.S. interest rate shock. To facilitate regional monetary arrangements,
China needs further financial liberalization and removal of capital controls tostrengthen its financial linkages with other Asian economies.

18

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21

Table 3.1: Countries and Regions in the Global VAR Model
Regions

Countries

Regions

Countries

Asian countries

China

Euro area

Austria

Hong Kong

BelgiumIndonesia

FinlandJapan

FranceKorea

Germany

Malaysia

Italy

Philippines

Netherlands

SingaporeSpain

TaiwanDeveloped countries

Thailand

U.K.
U.S.

22

Table 3.2: Data SourcesCountry

RGDP

CPI

IR

REER

Austria

CEIC

IFS

CEIC

IFS

Belgium

CEIC

IFS

CEIC

China

CEIC

CEIC

Finland

CEIC

FranceOil

Imports

Exports

PPP-GDP

IFS

DOT

DOT

WB

IFS

IFS

DOT

DOT

WB

CEIC

IFS

IFS

DOT

DOT

WB

IFS

CEIC

IFS

IFS

DOT

DOT

WB

CEIC

IFS

CEIC

IFS

IFS

DOT

DOT

WB

Germany

CEIC

IFS

CEIC

IFS

IFS

DOT

DOT

WB

Hong Kong

CEIC

IFS

CEIC

BIS

IFS

DOT

DOT

WB

Indonesia

CEIC

IFS

CEIC

BIS

IFS

DOT

DOT

WB

Italy

CEIC

IFS

CEIC

IFS

IFS

DOT

DOT

WB

Japan

CEIC

IFS

CEIC

IFS

IFS

DOT

DOT

WB

Korea

CEIC

IFS

CEIC

BIS

IFS

DOT

DOT

WB

Malaysia

CEIC

IFS

CEIC

IFS

IFS

DOT

DOT

WB

Netherlands

CEIC

IFS

CEIC

IFS

IFS

DOT

DOT

WB

Philippines

CEIC

IFS

CEIC

IFS

IFS

DOT

DOT

WB

Singapore

CEIC

IFS

CEIC

IFS

IFS

DOT

DOT

WB

Spain

CEIC

IFS

CEIC

IFS

IFS

DOT

DOT

WB

Taiwan

CEIC

CEIC

CEIC

CEIC

IFS

DOT

DOT

WB

Thailand

CEIC

IFS

CEIC

CEIC

IFS

DOT

DOT

WB

U.K.

CEIC

IFS

CEIC

IFS

IFS

DOT

DOT

WB

U.S.

CEIC

IFS

CEIC

IFS

IFS

DOT

DOT

WB

Price

Note: CEIC is the CEIC Global Database; IFS is the International Monetary Fund, International Financial
Statistics (IMF, IFS) CD-ROM edition; BIS is the Bank of International Settlements; DOT is the IMF
Direction of Trade; and WB is the World Development Indicator database of the World Bank.

23

Table 3.3: Trade Weights () Based on Direction of Trade Statistics

Country

Hong

China

Euro

Indonesia

Japan

Korea

Malaysia

Philippines

Singapore

TaiwanThailand

U.K.

U.S.

Kong
China

0

0.15740.49430.12250.2026

0.24570.10950.07830.11050.23770.12850.05370.2182Euro

0.164500.08470.11560.12970.11420.10530.11380.1091

0.09630.11490.64250.2671

Hong Kong

0.14240.022700.02020.04410.05030.04540.06280.07980.08770.04580.02450.0256

Indonesia

0.02090.01450.006800.03990.03620.0391

0.02230.03450.01600.04030.00490.0149Japan

0.16500.09030.08950.220800.18470.16090.18870.10900.17400.23840.04200.1792

Korea

0.10260.03510.03500.08380.0817

00.04890.06030.05610.05060.03980.01380.0617

Malaysia

0.03330.01870.0176

0.0616

0.03890.028800.04280.17910.03120.07210.01030.0313

Philippines

0.01450.00710.01110.01410.01980.01610.018200.02480.01450.02140.00360.0149Singapore

0.03010.0241

0.04460.13990.03500.04070.17330.085700.06110.07710.01600.0349Taiwan0.07790.02700.0546

0.03890.06930.04450.04890.0666

0.061000.04190.01150.0525Thailand0.02400.01670.01650.04490.04630.01790.05840.04140.05190.021700.00840.0229U.K.

0.02550.31360.02400.01770.02790.02180.02330.01820.02850.01870.026500.0768U.S.

0.19930.27270.12140.12010.26490.19900.16890.21910.15580.19070.15330.16890Note: Trade weights are computed as shares of exports and imports, displayed in columns by region (such that a column, but not a row, total 1).

24

Table 3.4: WS-ADF Unit Root Test for Domestic, Foreign, and Global Variables
China

Euro

Hong

Indonesia

Japan

Korea

Malaysia

Philippines

Singapore

TaiwanThailand

U.K.

U.S.

Kong
Domestic
RGDP

-2.70

-1.37

-2.95

-1.85

-2.57

-0.91

-0.79

-0.19

-2.19

-0.82

-1.77

-1.02

-1.61

-2.61*-4.41*-4.71*-5.58*-5.38*-5.67*-5.48*-4.19*-6.04*-6.46*-6.07*-3.60*-3.74*

-2.07

-2.75

-1.14

-6.82*

-1.19

-2.67

-3.09

-1.44

-1.74

-2.49

-2.67

-3.00

-2.10

INF

-3.80*-8.61*-4.48*-4.57*-7.15*-7.89*-8.81*-4.55*-5.20*-7.28*-8.61*-3.55*-5.00*

REER

-2.03

-2.46

-1.31

-1.88

-2.66

-3.22

-2.27

-1.88

-0.65

-3.10

-2.95

-2.52

-1.72

-6.35*-6.08*-3.69*-6.87*-4.38*-5.30*-7.17*-5.22*-5.23*-5.21*-7.59*-4.53*-7.84*

-1.92

-2.46

-3.32*-4.64*

-1.81

-2.85

-3.55*-2.96-3.31*-2.24-3.48*-2.21-4.40*-5.41*-5.27*-4.72*-6.40*-3.61*-8.73*-4.86*-5.41*-5.29*-3.87*-6.16*-5.42*-4.15*

-1.86

-1.51

-0.99

-1.31

-1.13

-1.35

-1.96

-1.50

-0.96

-1.44

-1.24

-1.29

-1.16

-4.99*-4.41*-3.85*-5.37*-4.93*-4.67*-5.44*-5.26*-4.74*-4.69*-5.12*-4.98*-4.68*

-1.57

-2.13

-2.05

-2.00

-2.12

-2.01

-2.13

-1.99

-2.09

-1.91

-2.12

-2.37

-1.97

-5.11*-7.04*-4.04*-7.53*-4.82*-6.89*-7.96*-8.12*-8.16*-4.63*-7.87*-8.19*-6.32*-3.60*

-2.95

-2.30

-3.24

-2.87

-2.71

-3.00

-3.26*

-2.63

-2.66

-2.50

-3.05

-2.59

-5.33*-4.59*-4.78*-5.54*-5.60*-5.13*-5.46*-4.95*-5.87*-5.04*-5.55*-4.64*-5.08*

RGDP
INF

REER
IR
IR
Foreign
RGDP*
RGDP*
INF*
INF*
IR*
IR*
Dominant
Oil Price
Oil Price-2.29-6.01*

Note: * indicates significance at a 5% level of significance.

25

Table 3.5: VARX* Order and Cointegrating Relationship in Country-Specific Models
Lag order of

Lag order of foreign

Number of cointegrating

domestic variables

variables

relations

China

2

1

1

Euro

2

1

2

Hong Kong

1

1

1

Indonesia

2

1

3

Japan

2

1

2

Korea

2

1

2

Malaysia2

1

1

Philippines

2

1

3

Singapore

1

1

2

Taiwan1

1

2

Thailand

1

1

2

U.K.

2

1

2

U.S.

2

1

2

Note: The rank of the cointegrating orders for each country/region is computed using Johansen’s trace statistics
at the 95% critical value level.

26

Table 3.6: Weak Exogeneity Tests of Country-Specific Foreign and Global Variables
Country

F testCriticalCountry-specific foreign and global variables

values

RGDP

INF

IR

Oil Price

China

F(1,77)

3.97

5.96

0.03

0.62

0.11

Euro

F(2,76)

3.12

0.33

0.58

1.89

0.17

Hong KongF(1,73)

3.97

0.80

0.86

0.46

0.19

Indonesia

F(3,81)

2.72

1.49

0.18

1.54

0.92

Japan

F(2,82)

3.11

0.70

0.78

0.20

0.69

Korea

F(2,82)

3.11

1.53

2.73

0.10

4.73

Malaysia

F(1,73)

3.97

0.51

0.02

0.81

1.55

Philippines

F(3,75)

2.73

0.67

1.87

2.52

1.98

SingaporeF(2,76)

3.12

0.80

0.23

0.52

1.28

TaiwanF(2,82)

3.11

0.10

0.65

0.14

2.64

ThailandF(2,82)

3.11

3.27

1.24

3.55

0.89

U.K.

F(2,82)

3.11

0.11

0.63

0.02

6.47

U.S.

F(2,72)

3.12

0.22

1.12

0.77

2.58

Note: Critical values are at the 5% level of significance.

27

Figure 4.1: GIRFs of Real Output to a Positive Chinese Real Output Shock28

Figure 4.2: GIRFs of Real Output to a Positive Japanese Real Output Shock29

Figure 4.3: GIRFs of Real Output to a Positive U.S. Real Output Shock30

Figure 4.4: GIRFs of Inflation Rates to a Positive Chinese Inflation Shock31

Figure 4.5: GIRFs of Inflation Rates to a Positive U.S. Inflation Shock32

Figure 4.6: GIRFs of Interest Rates to a Positive U.S. Interest Rate Shock33

Figure 4.7: GIRFs of Real Output to a Positive Oil Price Shock34

Figure 4.8: GIRFs of Inflation Rates to a Positive Oil Price Shock
China

0.2

Hong Kong

0.15

0.15

0.1

0.1

Indonesia

0.5
0

0.05
-0.5

0.05
0

0

-1

-0.05

-0.05
-0.1

-0.1
0

4

8

12

16

20

Japan

0.1

-1.5
0

4

0.1

0.06

0.05

0.04

12

16

0

-0.1

-0.04

-0.15

-0.06
4

8

12

16

Philippines

0.2

0.1

0.1

0.08

20

12

16

20

12

16

20

16

20

Malaysia

-0.05
-0.1
-0.15
4

8

12

16

20

Singapore

0.12

0.15

16

0

0

20

12

0.05

-0.2
0

8

0.1

-0.05

-0.02

4

0.15

0

0.02

0

20

Korea

0.15

0.08

8

0

4

8

Taiwan

0.15
0.1

0.06

0.05

0.05

0.04

0

0.02

-0.05

0

0

-0.1

-0.02

-0.15

-0.04
0

4

8

12

16

20

Thailand

0.15

-0.05
0

4

8

12

16

Euro

0.1
0.06

0.05

0.04

0

0.02
0

-0.05

-0.02

-0.1

-0.04

-0.15

-0.06
0

4

8

12

16

20

0

4

8

35

12

16

20

4

8

United States

0.12
0.1
0.08
0.06
0.04
0.02
0
-0.02
-0.04
-0.06

0.08

0.1

0

20

0

4

8

12