East Asian Currency Area: A Bayesian Dynamic Factor
Model Analysis
Toan Nguyen 1
Economic Research Section, 1st Floor
Engineering Building No 4
Kyoto Univeristy
Yoshida Honmachi, Sakyo-ku
Kyoto, Japan 606-8501
E-mail: toanjp@economics.mbox.media.kyoto-u.ac.jp

This Version: April 2007

1

I thank Professor Kimio Morimune at Kyoto University, Japan for his invaluable support and Professor Gary Koop at
University of Strathclyde, Scotland for his helpful suggestions in writing this paper. I am also indebted to JamesLeSage for his Matlab-based Econometric Toolbox.

Abstract

There has been recently increasing interest in the establishment of a common currency area
in East Asia in the aftermath of the East Asian financial crisis. In this paper, we examine thedesirability and feasibility of forming a currency area in the region by checking the symmetry ofshocks as an important criterion of the Theory of Optimum Currency Area. We employ a Dynamic
Factor Model to decompose aggregate output into global, regional and country-specificcomponents and estimate the model using Gibbs sampling simulation. Persistent properties of
those components are examined and variance decomposition analysis is performed to investigate
the role of each component in output variance. Based on variance analysis, we find that East Asia
countries, on average, are less plausible candidates for a currency area than European counterparts.
However, a subgroup of countries in East Asia are as qualified as those in Europe. Given theongoing integration in East Asia, it is not premature to prepare for such a currency area in thisregion.
Keywords: East Asia, Currency Area, Bayesian, Dynamic Factor Model, Gibbs Sampling
JEL classification numbers: F33, F42

I. Introduction
There has been a resurgence of interest in a concerted monetary arrangement and currency
union in East Asia in the aftermath of the regional crisis. In both academia and policy circle, theissue of establishing a regional currency area has attracted increasing attention (see for example,
Kwan (1998), Kuroda (2004)). Indeed, as an initial step, the ASEAN+3 (ASEAN plus Japan,
Korea and China) has agreed, in the so-called Chiang Mai Initiative, upon a network of bilateralswap agreements which allow East Asian countries to borrow fund from each other. The issuanceof Asia regional accounting currency (ACU) has been put forward.
This paper assesses the feasibility and desirability of forming a currency area in East Asia.
The theory of Optimum Currency Area (OCA) has been an important guideline for assessing thepossibility of a currency area. Joining a currency area typically requires abandoning the monetary
independence of member countries in favor of a common policy. The desirability of a currency
area depends largely on the cost of this abandonment against the benefits one can reap. The workof Mundell (1961), McKinnon (1963), Kenen(1969) and others have culminated in a host of
criteria, upon which one could judge whether a country is suitable, in economic sense, for acurrency union. Among these criteria, the symmetry of shocks seems to be of paramountimportance. If shocks are symmetric, the need for separate monetary policies is minimal since acommon policy serves all countries reasonably well. Otherwise, the cost of shock adjustment
might be large and one might need an independent monetary policy as an anti-cyclical instrument,
unless adjustment to shocks is fast. In this paper, we examine the symmetry of output shocks inEast Asia and its persistence properties in comparision with Western Europe (hereafter refered toas Europe). If the degree of shock symmetry in East Asia appears to be close to that of Europe, it
might be safe to conclude that East Asia might well constitute a currency area.

While most of studies on East Asia currency area are descriptive, few have attempted toapproach the issue empirically. Bayoumi & Eichengreen (1994) apply a bivariate structural VAR(SVAR) model à la Blanchard & Quah (1989) to identify supply and demand disturbances
underlying aggregate output and price. They compute bilateral correlations between each country
disturbances with that of Japan. Higher correlation means that shocks are more symmetric and that
it is more plausible to join a currency area. Their result indicates that East Asia is as suitable for acurrency area as the EU members. Sato et al (2003) extend the SVAR model to three and fivevariables, incorporating real exchange rate, price and foreign output. Compared to Europe, theyshow that it is less suitable for East Asia as a whole to establish a currency area than has beensuggested in earlier studies. Only sub-groups of East Asia countries, such as East Asia NICs and
ASEAN members are possible candidates for further monetary integration as their underlying
shocks are symmetric and adjustment speed to shocks is fast. Japan appears in their study as
having no significant correlation in supply, exchange rate and demand shocks with other East
Asian economies. Chow & Kim (2003) modify the SVAR analysis using an alternative
identification scheme to break output disturbances into global, regional and country-specificshocks. They find that East Asian aggregate outputs are strongly influenced by country-specificshocks whereas the role of regional shocks is insignificant. Goto & Hamada (1994) and Goto
(2002), on the other hand, implement principal component analysis to measure the degree ofconfluence in several macroeconomic variables in 14 East Asian countries (ASEAN 10 plus Korea,
China, Taiwan, HongKong). They find that macroeconomic variables in East Asia are strongly
synchronized and synchronization with Japan has been increasing since the 1990s. Accordingly,
they claim that East Asia is well suited for a currency area and the matter is how to realize it. Thus,
result has so far been mixed and it is not clear whether recent attempt for monetary integration isjustified.
These studies, however, have several disadvantages. First, SVAR analysis bases its
judgement on bilateral correlations of disturbances and often requires a representative country as aproxy for regional shocks (Germany in Europe and Japan in East Asia, for example). Since what
we are concern is regional rather than bilateral, judgements based on bilateral measurement might
not be appropriate. Shocks in the representative country might also be poor proxies of regional
ones. Moreover, in East Asia, there is no country naturally taking the role that Germany does in EU.
Second, both SVAR and principal component approaches focus exclusively on correlation without
distingushing between global and regional causes. There might be the case that global shocks
affect countries in a region simultaneously so as to induce high level of correlation. What iscomputed as regional shocks might actually be global shocks. Joining a broader monetary area
might be desirable in this case.
In this paper, we take another approach using a Dynamic Factor Model to decompose
aggregate output into (unobserved) common components and idiosyncratic component. These
common components and their coefficients are called factors and factor loadings respectively inthe literature and we would use these terms interchangeably. Pioneered by Sargent & Sims(1977)
and Geweke(1977), Dynamic Factor Model has been widely used to extract common factors froma set of economic time series, such as constructing coincident economic indicators (Stock andWatson, 1991) or world business cycle (Gregory et al, 1997). In our model, the aggregate outputs
are broken down into world, regional and country-specific components. Our intuition is that,
output volatility is the consequence of shocks induced by either global factors, regional factors orfactors that are specific to the country itself. If most of volatilities are determined by regional
factors, a regional common policy is sufficient to counter shocks. A common policy would,however, fail to deal with shocks that are mainly country-specific. We conduct variancedecomposition analysis to clarify the role of each component in inducing output fluctuations.
Traditionally, a dynamic factor model is cast in state-space form and estimated iteratively
using standard Kalman filter and log likelihood maximization. However, the procedure appears
difficult to perform when cross-session dimension grows large with a large number of unknown
parameters. We do not follow this convention. Instead, we exploit the advantage of Bayesian
Gibbs sampling simulation, which, though computationaly heavy, allows us to work with large
cross section data and large number of unknown parameters. This method is applied on a data setof 34 countries covering four regions: East Asia, Europe, North America and South America. Dueto the lack of data, we limit to our study on aggregate output only and leave other aggregates for
future works. Since OCA criteria are qualitative rather than quantitative, it is necessary to compare
East Asian estimation with a successfully establised currency area. Given the success of Euro zone
formation, Europe is a natural benchmark for our assessment.
This approach has several advantages over the previous researches. First, since our method
does not base on bilateral correlations but on the composition of output variance, our measure
better captures common shocks shared by countries in a region. No representative country isneeded. Second, the dynamic factor framework allows us to introduce the global factor into themodel to account for global shocks. By separating global and regional factors, we are able to takecare of pure regional shocks which would have been mixed up with global shocks if we applied
SVAR procedure. Lastly, in our framework, analysis of the dynamics of global and regional
business cycles is possible. In this paper, we collate the cycles with historical facts and investigate
the their pesistence properties.
To anticipate the result, we find that East Asia as a whole is a less plausible candidate for acurrency area than Europe. Specifically, shocks in East Asia are less symmetric than in Europe
indicated by less share of output variance explained by regional component. Also, forcountry-specific shocks, East Asia seems to adjust more slowly, suggesting higher cost for theabandonment of monetary autonomy. However, a subgroup of East Asia countries appear more
synchronized than the European average, implying that these countries are suitable for a currency
union and could proceed to form one in the first place. Interestingly, Japan and China, despite theireconomic power, are 'outliers' with little synchronization with other countries in East Asia.
The rest of the paper is organized as follow. Section II describes the model, estimation
technique and data processing. Section III analyzes estimation result and performs variancedecomposition analysis. The final section, as usual, is for conclusion.
II. The Model
Our model is built on several assumptions. First, we assume that aggregate output could be
decomposed into world component, regional component and country-specific component. World
component represents global shocks that affect virtually all countries. Typical examples of global
shocks are two oil price shocks in mid 1970s and early 1980s, causing global economic recessions.
Regional component, similarly, embodies regional shocks affecting simultaneously on countries
inside the region but are relatively innocuous to outside countries. The East Asian financial crisisand the unification of Germany are examples of regional shocks in East Asia and Europe
respectively. Country-specific component represents shocks that occur and influence within acountry due to its own structural or institutional causes. Further, we assume that these components
are contemporaneously uncorrelated. This assumption is necessary for our model to be identifiable.
The model covers four main regions: East Asia, Europe, North America and South America.
Global shocks would be shared by all four regions while regional shocks are specific to a region

only. Both global shocks and regional shocks influence differently in different countries, as
indicated by corresponding coefficients. The model is as follow:

yi,t =αiWt +βiRtr +εi,t, (t =1,2,...T; r =1,2,3,4; i =1,2,...n)(1)

where yi,t stands for aggregate output of countryi ;Wt stands for the world component and Rr ,t isregion r component ( r =1 for East Asia, r =2 for Europe, r =3 for North America and r =4 forSouth America). n is the number of countries andT is sample size. The error term εi,t is considered
as country-specific component in country i .Wt , Rtr and εi,t are contemporaneously uncorrelated.
αi and βi are coefficients (or factor loadings of the factorsW and R ) measuring relative impact of

the world and regional components on countryi aggregate output.
Second, we assume that the components follow stationary univariate first-order
autoregressive representation:
Wt = aWt −1 + ηtw , ηtw ~N (0, σw2 )(2)

Rtr = br Rtr−1 + ηtr , ηtr ~N (0, σR2 r )(3)εi,t = ci εi,t −1 + ηi,t , ηi ,t ~N (0, σi2 )(4)Where ηtw , ηtr and ηi,t are serially and contemporaneously uncorrelated Gaussian
disturbances with corresponding variances: σw2 , σR2 r (r = 1,2, 3, 4) , σi2 (i = 1,2,...n − 1, n ) .
Since this paper focuses on the cyclical pattern of the components, it is reasonable toassume them to be stationary (as we show later in data processing section, we first difference
aggregate output and test for stationarity using the popular ADF test). For simplicity and for saving
the degree of freedom, I assume the components are first-order autogressive. It should be notedthat the model, in principle, works well for the general case of AR(p) process.

With the above assumptions, it is straightforward to cast the system (1)-(4) into state spaceform:ξt = F ξt −1 + νt

(5)yt = H ξt(6)where ξt is a (n + 5) × 1 state vector and ξt = (Wt , Rt1, Rt2 , Rt3 , Rt4 , ε1,t ,ε2,t ,...,εn ,t )′ , yt is a n × 1
observation vector andyt = (y1,t , y2,t ,..., yn ,t )′ ; F and H are relevant coefficient matrices; νt is a(n + 5) × 1 state disturbance vector. νt = (ηtw , ηt1, ηt2 , ηt3 , ηt4 η1,t , η2,t ,...., ηn ,t )′ andνt ~ N (0,Q )(7)whereQ is a diagonal variance-covariance matrix.
For the ease of reference, we denote the stacked state vectors: ξ = (ξ1, ξ2,......ξT )′ ,Yi = (y1,y2,....,yT )′ . We also denote vectors of the unknown parameters in (5) as φ = (a, br , ci , σw2 , σR2 r , σi2 )and the unknown parameters in (6) as ψ = ( αi , βi ) with r = 1, 2, 3, 4 , i = 1, 2,...n .
If we happened to know ξ , the state space system (5) - (6) collapses to a set of separate
linear regressive equations (1)-(4) with known explaining variables, of which the procedure toestimate is well-known. We should be cautious, however, that the error term εi,t in (1) is
autocorrelated and can not be estimated directly. However, (1) could easily be transformed tousual regression equation with uncorrelated disturbance by simply multiplying both side of (1)with (1 − ci L) , noting that (4) could be rewritten as (1 − ci L)εi,t = ηi,t :(1 − ci L)yi,t = αi (1 − ci L)Wt + βi (1 − ci L)Rtr + (1 − ci L)εi ,toryi∗,t = αiWi,∗t + βi Rir,t∗ + ηi,t , ηi,t ~ N (0, σi2 )(8)where yi∗,t = (1 − ci L)yi,t ,Wi,∗t = (1 − ci L)Wt and Rir,t∗ = (1 − ci L)Rtr
Unfortunately, ξ is unobserved. Traditionally, a state space system could be estimated byusing Kalman filter to derive sample log likelihood conditional on the unknown parameters. The
log likelihood is then maximized numerically with respect to these parameters until convergence.
However, a weakness of this method is that log likelihood maximization is hard to perform when
the number of unknown parameters becomes large. Stock and Watson (1998, 2001) propose a
two-step procedure for state space model with very large cross-section dimension using principal
components. Forni et al (2000) formulate an identification and estimation scheme for ageneralized dynamic factor model, in which cross-section dimension goes to infinity. In this paper,
we take the advantage of Bayesian Gibbs sampling procedure which allows us to easily estimate
large cross-section state space system with large number of unknown parameters.
In Bayesian econometrics, unknown parameters are treated as random variables driven byunderlying stochastic distributions. In our model, unknown parameter vectors φ , ψ and statevector ξ are random variables whose mean and variance are to be estimated. Whereas traditional
Kalman filter maximization infers ξ conditional on the parameters, Bayesian inference on ξ isbased on joint distribution of ξ and the unknown parameters φ and ψ . That is, we have to takedraws from the joint posterior distribution p(ξ, φ , ψ | Yi ) . Direct drawing from this joint
distribution is impossible, however, since p(ξ, φ , ψ | Yi ) does not take any well-known form.
Nevertheless, the fact the state space system would collapse to estimable linear regressive
equations when ξ is known suggests that it is possible to take draws from the conditional
density p(φ | ξ, ψ,Yi ) and p(ψ | ξ, φ,Yi ) using independent Normal Gamma prior. If we could takedraws from the conditional density of ξ , p(ξ | φ , ψ,Yi ) , draws from the joint posterior
distribution p(ξ, φ , ψ | Yi ) could be derived using a Gibbs sampler.
The conditional density p(ξ | φ , ψ,Yi ) could be obtained through a simulation smoother.
Following Carter & Kohn (1994), p(ξ | φ , ψ,Yi ) is given by (we hereafter omit φ , ψ inconditional part for ease of denotation).

T −1p(ξ | Yi ) = p(ξT | Yi )∏p(ξt | ξt +1, yjt ), where k
yt = (y1,y2,....,yt )′

(9)

t =1

Because our model is Gaussian, the distribution of ξ givenYi is also Gaussian and could be
written out more clearly as:

iT ) in stacked form
ξt | yjt ~ N (ξt |T ,Pt |T ) or ξ | Yi ~N (ξT , P(10)i ); ξT = (ξ1|T , ξ2|T ,...., ξT −1|T ,ξT |T ) ;for t = T ,T − 1,T − 2,....,1 ; ξt |T = E (ξt | yt
iT = (P1|T , P2|T ,...., PT −1|T ,PT |T ) .
Pt |T = cov(ξt | yit ) and P
We can directly compute ξt |T and Pt |T using recursive Kalman filter and smoother algorithm

with initial values ξ1|0 and P1|0 :ξt |t = ξt |t −1 + Pt |t −1H ′(HPt |t −1H ′ + R)−1(yt − H ξt |t −1 )Pt |t = Pt |t −1 − Pt |t −1H ′(HPt |t −1H ′ + R)−1 HPt |t −1(11)

ξt +1|t = F ξt |t
Pt +1|t = FPt |t F ′ + Q

andJ t = Pt |t F ′Pt−+11|t
ξt |T = ξt^|t + J t (ξt +1|T − ξt +1|t )(12)

Pt |T = Pt |t + J t (Pt +1|T − Pt +1|t )J t′

The Kalman filter runs (11) forward with t = 1,2,...T and (12) backward witht = T − 1,T − 2,...1 . Details of the derivation of Kalman filter and smoother is presented in, forexample, Hamilton (1994), chapter 13. Under the assumption that ξ is stationary and theeigenvalues of F lie inside the unit circle, ξ1|0 is zeros and P1|0 is given by:vec(P1|0 ) = [I r 2 − (F ⊗F )]−1 vec(Q )(13)where ⊗ denotes Kronecker product.
In principle, we could draw ξ directly from the distribution in (10). However, Durbin andKoopman (2002) has developed a more efficient simulation smoother which facilitate the drawing

of ξ . In our model, their algorithm runs as follow:
First, draw a random vector v + from the density p(v ) = N (0,Q ) in (7) and from v +++recursively generate stacked vector ξ and Yi using (5)-(6) with ξ1|0 and P1|0 computed from (13).
+++Second, compute ξT = E (ξ | Yi ) and ξT = E (ξ | Yi ) using Kalman filter and smoother(11) and (12).
+
+
+
+
+
iT , where P
iT = cov(ξ + | Yi + ) . As
Finally, since ξT = E (ξ | Yi ) , −ξT + ξ gives Pi ) . Computing
iT does not depend on data, P
iT = cov(ξ + | Yi + ) = cov(ξ | Y
PiT ) .
ξ = ξT − ξT+ + ξ + , we obtain a draw of ξ from distribution N (ξT , P
Given the conditional distributions p(ξ | φ , ψ,Yi ) , p(φ | ξ, ψ,Yi ) and p(ψ | ξ, φ,Yi ) ,Gibbs sampling simulation is implemented to estimate ξ, φ and ψ from joint distribution densityp(ξ, φ , ψ | Yi ) . Specifically, the Gibbs sampler would proceed in three steps:
1. Conditional on the parameter vectors φ and ψ , draw state vector ξ from theconditional distribution p(ξ | φ , ψ,Yi ) using Durbin and Koopman (2002) simulation smoother.
2. Conditional on the state vector ξ , draw parameter vector φ from the conditional
distribution p(φ | ξ, ψ,Yi ) .
3. Conditional on the state vector ξ and the parameter vector φ , draw parameter vector ψfrom the conditional distribution p(φ | ξ, ψ,Yi ) .
Step 2 and 3 are carried out using independent Normal - Gamma priors.
These steps are iterated S times, of which the first So draws are discarded as burning-in
replications to remove the effect of initial values. The initial values of φ and ψ are selected
arbitrarily in the unit circle. To confirm simulation convergence, we compute Geweke (1992)

numeric standard error and Raftery and Lewis (1992) convergence diagnostics. We also repeat the
Gibbs sampler with different initial parameter values to check if the effect of initial values is
actually removed.
Two related identification problems should be noted when estimating the system, however.
First, the signs of the common components and their associated coefficients in (1) are notseparately identified. We handle this by requiring one of the coefficients for each component to bepositive. Second, the scale of the those components and coefficients in are not separately identified
either. We follow the convention to overcome this by normalizing the variances of ηtw and ηtr in(2)-(3) to unity.
The model is estimated using annual data from 10 East Asian countries, 12 European
countries, three North America countries and nine South America countries over the period 1960 2002. GDP data is used as a proxy of aggregate output. For East Asian countries, we select Japan,
Korea, China, HongKong, Taiwan and ASEAN 5 of Singapore, Thailand, Malaysia, Indonesia andthe Philippines. For the purpose of our paper, we select only members of European Monetary
Union (Austria, Belgium, Finland, France, Ireland, Italy, Luxemburg, Netherlands, Greece,
Germany, Portugal and Spain) to present Europe. Three North America countries (NAFTA) are
Canada, Mexico and the United States. Countries in South America group includes major
members of the South American Community of Nations (Argentina, Bolivia, Brazil, Chile,
Colombia, Paraguay, Peru, Uruguay and Venezuela). Data is drawn from World Bank World
Development Indicator 2004 CD-ROM and IMF International Financial Statistics 2005 CD-ROM.
Before estimation, the data is filtered to generate detrended series. Although there areseveral detrending methods in the literature, we choose to use the most simple one: all the series
are logged and first-differenced to achieve stationarity. We confirm the stationarity of these series

by applying ADF test for unit root. The tests failed to detect the presence of unit root in any series.
To verify whether our result is robust with different filter scheme, we repeat our estimation withdata filtered by another method, the Hodrick-Prescott (1980) filter. We take log of the data andapply this filter with parameter 100, keeping in mind that the Hodrick-Prescott filter might
generate spurious correlation series (Harvey & Jaeger, 1993) and spurious cycles (Cogly & Nason,
1995). Detrended series are then standardized to obtain zero mean and unit variance. This isnecessary to ensure that all series receive equal weights and every country is treated equallyirrespective of its relative economic size. Estimation program is written in Matlab code using somemodules from Jame LeSage's Econometric Toolbox.
III. Result:
The Gibbs sampler is iterated 12,000 times, of which the first 2,000 is discarded asburning-in replications. Geweke (1992) numeric standard error and Raftery and Lewis (1992)
Z-test confirm Gibbs sampling convergence. Numeric standard errors are sufficiently small.
Raftery and Lewis (1992) test (quantile 0.025, precision level 0.005 and associated probability of0.95) indicates that the Gibbs sampler converges after less than 5000 replications. We also check if
the initial values have worn off or not, using an informal procedure. Particularly, we repeat oursimulation with random initial parameter values. Results are identical across repetition. Ourestimation on Hodrick-Prescott filtered data and first-differenced data shows similarity in almost
aspects. Specifically, both data produce similar dynamics of world and regional components,
factor loading coefficients and their relative variance shares. While we base our assessment on
first-differenced data, we will, however, refer to Hodrick - Prescott filtered data result wherever
the discrepancy is significant and might lead to different interpretation. Estimated parameters arepresented in Table 1 and Table 2. In Table 1, we show the autocorrelation coefficients of the worldand regional components estimated from equations (2) and (3). Factor loading coefficients andcountry-specific autocorrelation coefficients are shown in Table 2. The dynamics of world andregional components are illustrated in Figure 1, 2, 3 and 4.
Common component dynamics
Before investigating the relative role of the components in output volatility, it is necessary
to take into account their dynamics. The purpose of this step is twofold. First, examining the
dynamics of the components reveals whether the constructed common components do exhibit
known cycles associated with major world and regional ups and downs in the past decades. If they
appear to be poor indicators of world and regional business cycles, either our assumptions or
estimation or both are inappropriate. Second, by analyzing the autocorrelation coefficients of thecomponents, we are able to measure the persistence of world, regional and country-specific shocks.
We interpret this persistence properties as measures of the speed of adjustment to shocks.
Figure 1 shows the dynamics of the world component, which could be interpreted as theworld business cycle. Interestingly, the unobserved world factor well describe known economic
events: the steady expansion of the early 1960s; the first steep fall of the mid 1970s due to the firstoil price shock; the second big downturn of the early 1980s following the second oil price shock,the debt crisis in Latin America and the tight monetary policies in major industrial economies; therecession in the early 1990s and the recovery in the late 1990s, similar to those in Kose et al (2000)
and Gregory et al (1997). The steepest drops in the figure coincide with the two severe recessions
in mid 1970s and early 1980s. While Kose et at (2000) find that the recessions of mid 1970 andearly 1980 are about of the same degree of severity, our world component indicates that the later ismore disastrous.
<Figure 1 here>, <Figure 2 here><Figure 3 here>, <Figure 4 here>
East Asia regional factor is presented in Figure 2. Comparatively, the East Asia regional
component seems to be less volatile except in 1997-1998 due to the East Asian financial crisis.
Two other less severe output drops are found in mid 1970s and mid 1980s. The former might bereasonably explained by the first oil shock. The latter is associated with the regional recession in
1985-1986 in many of East Asia countries, such as HongKong, Singapore, Malaysia and thePhilippines (in 1985, GDP growth rate of the Philippines fell to -7.6 percent (Lim and Bautista,
2002)). This is consistent with the common view that East Asia had experienced a long period ofhigh and stable economic growth until the onset of the regional chaos (World Bank, 1993).
We depict the European regional component in Figure 3. The impact of first and second oil
crises are clear in Europe. Unlike world common component, we find the first oil shock is more
severe than the second to Europe. The early 1990s downturn associating with German unificationand Exchange Rate Mechanism crisis was also more severe than the second oil price shock. We
show North and South American regional components in Figure 4 and Figure 5. As we can see,North America cycle appears most volatile. The troughs and peaks of this cycle coincides
remarkably with the US business cycles: recession in 1970, 1975, 1980, 1982, 1991, 2001 andexpansion in 1973, 1981, 1984, late 1990s.
While the component dynamics reflect historical events, an investigation of their
persistence property might provide insight of the adjustment speed. The persistence of the factors
are measured by the first-order autocorrelation coefficients in Table 1. Larger coefficients mean
higher degrees of persistence and imply longer effects of past shocks. Thus, we interpret thepersistence property of the components as an indicator of adjustment speed.
The European regional component appears to be the most persistent. Its autocorrelationcoefficient is 0.51, indicating that adjustment to regional shocks in Europe is slow. North America
responds fastest to regional shocks. Its autocorrelation coefficient is just 0.23. East Asia adjustsslightly slower, with the coefficient of 0.24 (if we use the Hodrick - Prescott filtered data, East
Asia is even more responsive to regional shocks). South America is in the middle, with thecoefficient of 0.36.
<Table 1 here>
Since a currency area involves a common monetary policy, regional adjusment speed is not
influential on the cost of monetary policy abandonment. What matters is the responsiveness toasymmetric or country-specific shocks. In our model, these shocks are captured bycountry-specific components. Table 2 shows that European countries adjust fastest to
country-specific shocks, followed by South America, North America and East Asia. European
integration might be responsible for this rapid adjustment since factor movement, trade andinvestment within Europe probably alleviate adjustment process. Smaller countries often adjust
faster to shocks. Adjustment speeds are fastest in the Netherlands, Portugal and Greece andslowest in Luxemburg, France and Ireland. In East Asia, asymmetric shocks have longest effects inJapan and the Philippines, reflecting structural and institutional rigidity in these countries. Korea
responds fastest to country-specific shocks.
<Table 2 here>
Variance Decomposition
To measure the role of world, regional and country-specific shocks in output volatility, we
conduct variance decomposition analysis. Under the assumption that the components are
orthogonal, it is straightforward to decompose output variance into three parts corresponding to
the three components, noting thatVar (ηtw ) = 1 and Var (ηtr ) = 1 as assumed:

Var (yi ) = αi2Var (W ) + βi2Var (Rr ) + Var (εi )Var (W ) =

1
1
σi2
;
(
)
;
(
)
Var
R
=
Var
ε
=
r
i
1 − a2
1 − br2
1 − ci2(14)(15)

From (14) and (15), the share of output variance explained by those components arecomputed as

Var (yi ) =
S =
w
i

αi2
1−a 2

Var (yi )

αi2
βi2
σi2
+
+
1 − a 2 1 − br2 1 − ci2
;S =
r
i

βi2
1−br2

Var (yi ); Si =

σi2
1−ci2

(16)

Var (yi )Where r = 1, 2, 3; i = 1, 2,...n ; S iw , S ir and Si are the respective shares of world
component, regional component and country-specific component in country i output variance.
The share of each component in output variance provides information on the symmetry ofshocks, upon which one determines whether a country should join a regional currency area or not.
If a large share of volatility in a country is explained by regional component, its shocks aresynchronized with regional ones and the cost of forgoing independent monetary policy would besmall. If the world component takes relatively large share, joining a broader monetary arrangement
might be more appropriate. In case a large share of volatility is explained by country-specificvariance, the country experiences asymmetric shocks and a regional currency area membership
might be costly.
<Table 3 here>
Variance shares (in percentage) attributable to each component are presented in Table 3.

Surprisingly, the world component, on average, merely accounts for less than 10 percent offluctuations in all regions. For many countries in East Asia such as Korea, China, and Thailand, theworld shocks have virtually no impact . In the context of accelerating globalization, the findingthat regional and country-specific factors are responsible for the most part of output variance isstriking.
Country-specific factors account for a large share in output variance in all regions. They
explain 63.5 percent and 52.3 percent of variance in East Asia and Europe respectively. For many
countries, country-specific factor explains almost all output variance. In East Asia, Japan, thePhilippines and China are most influenced by country-specific shocks. These factors account for atleast a half of output variance in every country in East Asia. In Europe, the role of country-specificshocks is also significant in all countries, especially in Ireland where almost shocks are
domestically spawned. Country-specific shocks are main source of volatility in South America.
Only in the U.S., are country-specific shocks insignificant. This does not necessarily imply thatshocks in the U.S. are mostly external. What it probably means is that most of shocks in the US
spill over into global and regional shocks given the power of the U.S. economy.
Regional components explain significant shares of variance in all regions except SouthAmerica. With no surprise, North America is the most synchronized region, specifically between
the United States and Canada. In both countries, the regional factor explains more than two thirdsof variance, probably due to strong integration between the economies. Mexico appears like anoutlier as the regional factor accounts for just two percent of its output variance. Europe also
appears synchronized with average regional share of 42 percent. Notably, the core countries of the
EMU (Austria, Belgium, Germany, France, Italy and the Netherlands) exhibits strong common
shocks, as shown by large shares (62%) of regionally rooted volatilities. The regional factor,

however, accounts for just a little output variance in Finland and Ireland; both are geographically
far from the core. Regional factors are of little importance in South America.
In East Asia, the share of variance explained by regional factors is lower than in Europe.
On average, regional factors accounts for 32 percent of output variance in East Asia. However,they have negligible impact on output variance in China and Japan. This is hardly surprising as
both China and Japan have experienced quite different development paths. While China had
undergone a centrally planned system before transitioning to market economy, Japan has been far
more developed than other countries in the region. Quite strong synchronization is found between
East Asian NICs, except for Taiwan. In Taiwan, world shocks and country-specific shocks are
more important than regional shocks. In other NICs (Korea, HongKong, Singapore, Malaysia,
Indonesia and Thailand), regional factors, on average, accounts for roughly 48 percent of output
variance. The findings are consistent on both first-differenced data and Hodrick - Prescott filtereddata.
Though East Asia shows certain degree of shock symmetry, it is hardly possible from thelight of OCA criteria to say for sure whether East Asia is indeed qualified for a currency area.
Fortunately, we can exploit the success of the European Monetary Union as a nateral benchmark
for comparision. If the role of regional factors in East Asia comes somewhat close to that in Europe,
we might expect that it is feasible to replicate their success in East Asia. Otherwise, it might bedifficult in the near future for such a plan to succeed. In general, East Asia exhibits less symmetry
than Europe: variance share of regional component is lower and of country-specific component ishigher, implying that East Asia is not as qualified as Europe for a currency area. Moreover, as
shown above, adjustment to country-specific shocks in East Asia is slower and therefore,
associating cost of adjustment is probably higher. However, the gap between East Asia and Europe

is not large (32% vs. 42%). If we compare the highly synchronized group of Korea, HongKong,
Singapore, Malaysia, Indonesia and Thailand with European average, they appears to be potential
candidates for a currency area. Obviously, this group cannot be compared with the European core
where regional factor accounts for as large as 62 percent of output variance. It should be noted that
Europe has achieved this level of synchronization after a long period of preparation and integration.
Given the ongoing integration in East Asia, a vision of East Asia currency area would not be
considered as utopian. Since not all East Asian countries are equally synchronized, a group ofcountries with higher degree of synchronization might form a currency union in the first place.
It is worthwhile to put our findings in juxtaposition. Bayoumi & Eichengreen (1994) findthat East Asia is as plausible candidate as Europe for a currency area. They base their findings oncorrelation analysis of supply and demand disturbances identified by a structural VAR framework
à la Blanchard & Quah (1989). Goto & Hamada (1994) and Goto (2002) find the same conclusion
by exploring a principle component analysis. Sato et al (2003) extend the work of Bayoumi &Eichengreen (1994) using a three and five variables SVAR model. They find less persuasive
support for a currency area in Asia and claim that only a subgroup of East Asian countries are
possible candidates for monetary integration. They also find that adjustment speed to shocks isfaster in East Asia. Chow & Kim (2003) modify the SVAR framework to identify shocks as global,
regional and country-specific. Their result shows that in East Asia, country-specific shocks are
more important and therefore, joining a currency area is not optimal. We find ourself in similar
stance with Sato et al (2003) that, compared with Europe, East Asia is a less suitable but close
candidate for a currency area. However, we distinguish between adjustment speeds to regional
shocks and to country-specific shocks. We find the latter is slower in East Asia.
IV. Conclusion

In this paper, we examine the feasibility of forming a currency area in East Asia.
Particularly, we check the symmetry of shocks by employing a Dynamic Factor Model todecompose aggregate output into three distinct components: world component, regional
component and country-specific component. Our view is that, if output volatility in East Asia isdominantly driven by regional factors, then imposing a common currency would be desirable andentail little cost. On the contrary, if sources of fluctuations are largely country-specific, sacrifying
monetary autonomy would be disastrous as a common policy is not sufficient to counter
asymmetric shocks. In case world factors are important in determining output volatility, joining abroader currency arrangement might be more desirable.
Our result shows that East Asia is less plausible for a currency area than Europe in general.
Furthermore, East Asian countries are less responsive to country-specific shocks so that
adjustment process would be more costly and require longer time. However, a subgroup of East
Asian countries including Korea, HongKong, Singapore, Malaysia, Indonesia and Thailand might
be good candidates for a currency area since they appear highly synchronized. Our result is less
optimistic than those of Bayoumi and Eichengreen (1994), Goto and Hamada (1994) and Goto
(2002), where East Asia is as suitable as Europe for a currency area. However, we are not as
pessimistic as Chow and Kim (2003), whose result shows that it is not possible for East Asia toform a currency area due to shock asymmetry. We find our result similar to Sato et al (2003). As
Europe has experienced long integration and preparation process, a similar route might also bring
East Asia closer to a currency union. A subgroup of more synchronized countries should go first,
followed by other countries. Given East Asian accelerating integration in both trade and finance, it
is not too early to prepare for such a move. Forming a currency area, however, requires not only
economic condition but also political consensus, which might be more difficult to achieve in East

Asia.
There is still room for further improvement. First, since we work with output volatility inlieu of structural shocks, information on shocks could be conflated with policy responses. Ourmodel could be extended to map the components' disturbances into structural shocks to avoid suchconflation. A Factor-Augmented VAR model in the light of Bernanke et al (2005) and Stock andWatson 2005 might be the direction to take. Second, the model could also be extended to allow
time-variant coefficients to capture structural changes. Finally, more aggregate variables, such as
consumption, investment and price could be introduced into the model once data are available. We
leave these for future works.

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Table 1: World and Regional Autocorelation CoefficientsWorld

0.4724

East Asia

0.2415

Europe0.5092

North America

0.2308South America

0.3578

Table 2: Country Factor Loadings and Autocorrelation Coefficients

East Asian
Japan
Korea
China
Hongkong
Singapore
Malaysia
Indonesia
Philippines
ThailandTaiwanAverage
EuropeGermany
Belgium
Finland
Neitherlands
France
Italy
IrelandSpain
Portugal
Luxemburg
Austria
GreeceAverage
North America
US
CanadaMexicoAverage
Latin America
BrazilArgentina
Chile
Colombia
Peru
Uruguay
Paraguay
Venezuela
Bolivia
Average

World

Regional

Country-specific

0.20
0.02
0.01
0.31
0.10
0.11
0.02
0.06
0.00
0.33
0.12

0.19
0.68
0.00
0.51
0.56
0.80
0.62
0.37
0.64
0.35
0.47

0.66
0.19
0.31
0.49
0.22
0.42
0.31
0.56
0.47
0.28
0.39

0.20
0.21
0.13
0.19
0.17
0.19
-0.05
0.08
0.20
0.17
0.17
0.21
0.16

0.45
0.57
0.22
0.56
0.57
0.50
-0.04
0.38
0.51
0.48
0.55
0.35
0.42

0.26
-0.31
0.42
0.09
0.34
0.29
0.50
0.44
0.04
0.48
-0.26
-0.12
0.18

0.15
0.16
0.23
0.18

0.89
0.74
0.14
0.59

0.32
0.48
0.30
0.36

0.25
0.22
0.14
0.34
0.14
0.21
0.11
0.27
0.07
0.19

0.11
0.18
0.14
0.17
0.16
0.24
0.11
0.12
0.05
0.14

0.41
0.05
0.31
0.27
0.37
0.38
0.50
-0.04
0.26
0.28

Table 3: Output Variance Decomposition

East Asian
Japan
Korea
China
Hongkong
Singapore
Malaysia
Indonesia
Philippines
ThailandTaiwanAverage
EuropeGermany
Belgium
Finland
Neitherlands
France
Italy
IrelandSpain
Portugal
Luxemburg
Austria
GreeceAverage
North America
US
CanadaMexicoAverage
Latin America
BrazilArgentina
Chile
Colombia
Peru
Uruguay
Paraguay
Venezuela
Bolivia
Average

World%

Regional%

Country-specific
%

7.13
0.07
0.02
14.24
1.36
1.44
0.06
0.41
0.00
17.92
4.26

5.15
48.11
0.00
31.85
37.84
67.15
45.92
14.82
54.99
16.02
32.19

87.72
51.81
99.98
53.91
60.80
31.41
54.02
84.78
45.01
66.05
63.55

15.73
8.75
2.71
6.28
5.89
6.16
0.26
1.81
6.19
3.03
4.49
6.63
5.66

83.10
64.84
8.19
55.52
71.52
46.35
0.23
37.96
42.60
26.17
48.23
19.71
42.03

1.17
26.41
89.10
38.21
22.60
47.50
99.51
60.23
51.20
70.79
47.28
73.66
52.30

3.32
4.24
7.23
4.93

95.66
77.13
2.29
58.36

1.02
18.63
90.48
36.71

10.42
9.08
2.54
19.21
2.69
9.92
1.94
12.01
0.58
7.60

1.72
5.49
2.47
4.60
3.19
11.68
1.65
2.17
0.27
3.69

87.86
85.43
95.00
76.19
94.12
78.41
96.41
85.82
99.15
88.71

Figure 1: World Common Component
1.5

1

0.5

0

−0.5

−1

−1.5
1960

1965

1970

1975

1980
1985
Year

1990

1995

2000

2005

Figure 3: European Regional Component

Figure 2: East Asian Regional Component

2

2

1.5
1

1
0

0.5

−1

0
−0.5

−2

−1
−3

−1.5
−4

−5
1960

−2

1965

1970

1975

1980 1985
Year

1990

1995

2000

2005

−2.5
1960

1965

1970

1975

1980 1985
Year

1990

1995

2000

2005

Figure 4: North American Regional Component
Figure 5: South American Regional Component

2
0.6

1.5
0.4

1
0.2

0.5
0

0
−0.2

−0.5

−0.4

−1
−1.5

−0.6

−2

−0.8

−2.5

−1

−3
1960

1965

1970

1975

1980 1985
Year

1990

1995

2000

2005

−1.2
1960

1965

1970

1975

1980 1985
Year

1990

1995

2000

2005