East Asian Currency Area: A
Bayesian Dynamic Factor ModelAnalysis
Nguyen Ngoc Toan
Graduate School of Economics
Kyoto University

Background and Objective
There has been a resurgence of interest in aconcerted monetary arrangement and currency unionin East Asia in the aftermath of the regional crisis.
The successful issuance of the EuroThe East Asian crisis shows that uncoordinated efforts
could hardly win massive speculation
Increasing regional integration

This paper assesses the feasibility and desirability offorming a currency area in East Asia by checking thesymmetry of business cycles.

1Why symmetry of business cycles isimportant?
Joining a currency area involves a benefit and cost
trade-off: the benefit is increasing trade andinvestment; the cost is foregoing independent
monetary policy.
 Monetary policy is a major macroeconomic
instrument to stabilize the economy, counter adverseshocks.
 If business cycles are symmetric, that is, they suffer
largely from common shocks, a regional common
policy is sufficient to counter shocks. Otherwise, one
may have to rely on independent policy once sourcesof shocks are mainly country-specific.
How to measure shock symmetry:
Structural VAR Approach
Methodology: identifying structural shocks
underlying aggregate variables (output,price…) using structural VAR due toBlanchard & Quah (1989) and computing
bilateral correlations.
 Previous studies: Bayoumi & Eichengreen(1994), Sato et al (2003), Chow & Kim(2003)
2

Structural VAR: Disadvantages
A representative country is needed as a proxy
for a region (e.g. Japan for East Asia,
Germany for EU).
 Bilateral correlation rather than regionalsymmetry.
 No separation of regional and world shocks
 Inherent problems with VAR approach(estimation, identification)
How to measure shock symmetry:
Principle Component Approach
Methodology: Using principle component
analysis to measure the degree of confluence
in several macroeconomic variables
 Previous studies: Goto & Hamada (1994),
Goto (2002)
 Disadvantages: no distinction between
regional and world causes.
3

How to measure shock symmetry:
My Approach







In my model, aggregate output is decomposed into(unobserved) common and idiosyncratic components: worldcomponent, regional component and country-specificcomponent.
Intuition: fluctuations in aggregate output are theconsequences of shocks induced by either world or regional
factors or factors that are specific to a particular country.
Subsequent variance decomposition provides insight of therole of each components in output variance.
Europe is used as a natural benchmark for comparision

My Model: Advantages
Do not base on bilateral but on regionalcommon factors. No representative country isneeded.
 Separating the effects of world and regional
factors.
 Analysis of world and regional business cycle
dynamics is possible.
4

The Model: Assumptions (1)Aggregate output could be decomposed into world
component, regional component and countryspecific component. These components arecontemporaneously uncorrelated.
 World and regional components influence differentlyin different countries, as indicated by corresponding
coefficients.
yit, =αiWt +βiRtr +εit,

The Model: Assumptions (2)The components follow stationary univariate
first-order autoregressive representation.
Wt = aWt − 1 + ηtw , ηtw ~ N (0, σw2 )Rtr = br Rtr−1 + ηtr , ηtr ~N (0, σR2 r )εi ,t = ci εi,t −1 + ηi,t , ηi,t ~N (0, σi2 )5The Model: State-Space Representation

It is straightforward to cast the equations into state
space form

ξt = F ξt −1 + νt

yt = H ξtwhere F and H are relevant coefficient matrices andξt = (Wt , Rt1, Rt2 , Rt3 , Rt4 , ε1,t , ε2,t ,..., εn ,t )′The Model: Estimation (1)Note that in the above equations, only yt is known.
Other variables are not known so standard estimation
techniques are not applicable.
 Traditional method using standard Kalman filter andlog likelihood maximization is difficult to perform
when cross-session dimension grows large.
 Bayesian approach with Gibbs sampling simulation
allows us to work with large cross section data andlarge number of unknown parameters.
6

The Model: Estimation (2)Bayesian econometrics treats unknown parameters
as random variables.
 The variables to be estimated are:

The stacked state vector
The parametersɶξ = ( ξ 1 , ξ 2 ,. . . . . . ξ T ) ′φ = (a,br , ci , σw2 , σR2 r , σi2 )ψ = (αi , βi )The posterior joint density of the random variables
ξ, φ, ψ | Y )conditional on data is p(ɶThe Model: Estimation (3)

The parameters are estimated by posterior simulation using
Gibbs sampler
 Conditional on the parameter vectors φ and ψ, draw statevector ξ from the conditional distribution p(ξ|φ, ψ,Y)using Durbin & Koopman (2002) simulation smoother.
 Conditional on the state vector ξ, draw parameter vector φfrom the conditional distribution p(φ|ξ,ψ,Y).
 Conditional on the state vector ξ and the parameter vector
φ, draw parameter vector ψ from the conditionaldistribution p(ψ|ξ,φ,Y).
Step 2 and 3 are carried out using independent Normal –Gamma priors.These steps are iterated S times, of which thefirst So times are discarded as burning-in replications.

7

First step: Draw state vector ξ from theconditional distribution p(ξ|φ, ψ,Y) (1)Following Carter & Kohn (1994), p(ξ|φ,ψ,Y) is given byT −1p(ɶξ | Y ) = p(ξT | Y )∏p(ξt | ξt +1, yt ), where 
yt = (y1,y2,....,yt )′t =1

Because our model is GaussianT ) in stacked form
ξt | yt ~ N (ξt |T ,Pt |T ) or ɶξ | Y ~N (ɶξT , P ); ɶξT = (ξ1|T , ξ2|T ,...., ξT −1|T ,ξT |T )
ξt |T = E (ξt | yt

First step: Draw state vector ξ from theconditional distribution p(ξ|φ, ψ,Y) (2)Recursive Kalman filter to derive ξtT| andPt |T
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
ξt +1|t = F ξt |tPt +1|t = FPt |t F ′ + Q
J t = P t |t F ′P t −+ 11 |tξ t |T = ξ t^|t + J t ( ξ t + 1 |T − ξ t + 1 |t )P t |T = P t |t + J t ( P t + 1 |T − P t + 1 |t )J t′8

First step: Draw state vector ξ from theconditional distribution p(ξ|φ, ψ,Y) (3)In principle, we could draw ξɶ directly.
However, generating ɶξ from ɶξ | Y ~N (ξɶ P ) is not
T,Tefficient and may sometimes face technical problems
with the matrix.
Durbin & Koopman (2002) has developed amore efficient simulation smoother whichfacilitate the drawing of ɶξ

Durbin & Koopman (2002) simulation smootherFirst, draw a random vector v+from p(v) = N (0,Q )++and recursively generate stacked vector ɶξ andY
 Second, compute ɶξ = E (ɶξ | Y ) and ɶξ = E (ɶξ | Y )using Kalman filter and smoother.
ξ Computing ɶξ = ɶξ − ɶξ + ɶξ , we obtain a draw of ɶfrom distribution
+T

T+T+++

T

9

Identification Issues

Two related identification problems should be solved
when estimating the system:The signs of the common components and their associatedcoefficients are not separately identified. We handle thisby requiring one of the coefficients for each component tobe positive.
The scale of the those components and coefficients are notseparately identified either. We follow the convention toovercome this by normalizing the variances in world andregional component equations to unity.

DataThis model is applied on an annual data set of
34 countries covering four regions: East Asia,Europe, North America and South America
for the period from 1960 – 2002.
 The data is logged and first-differenced,
demeaned and standardized to obtain mean
zero and unit variance. Estimation program iswritten in Matlab code.
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Sensitivity Analysis and Convergence
Beside first-differencing, Hodrik – Prescott(1980) filter is used to ensure the results are
not sensitive to filtering methods.
 Geweke (1992) numeric standard error andRaftery and Lewis (1992) Z-test confirm
Gibbs sampling convergence.
 The Gibbs sampler is iterated 12,000 times, ofwhich the first 2,000 is discarded as burningin replications.


Result: Component Dynamics (1)Figure 1: World Common Component

Figure 2: East Asian Regional Component

1.5

2

1

1

00.5
−1
0−2−0.5−3−1−1.5
1960

−4

1965

1970

1975

1980 1985
Year1990

1995

2000

2005

−51960

1965

1970

1975

1980 1985
Year1990

1995

2000

2005

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Result: Component Dynamics (2)Figure 4: North American Regional Component
21.5
10.5

Figure 3: European Regional Component
02−0.51.5−1−1.5

1−20.5−2.5−3
1960

0

1965

−0.5

1970

1975

1980 1985
Year1990

1995

2000

2005

2000

2005

Figure 5: South American Regional Component
0.6

−10.4
0.2

−1.5

0−2−0.2

−2.5
1960

−0.4

1965

1970

1975

1980 1985
Year1990

1995

2000

2005
−0.6
−0.8
−1−1.2
1960

1965

1970

1975

1980 1985
Year1990

1995

Result: Persistent Properties
Regional components:
Persistence is highest in
Europe, followed by SouthAmerica, East Asia andNorth America.
Country-specificcomponents (not shown):
Persistence is lowest in
Europe, followed by Southand North America andEast Asia.

World

0.4724

East Asia

0.2415

Europe0.5092

NorthernAmerica
SouthernAmerica

0.23080.357812

Why Variance Decomposition isNecessary?
To measure the role of world, regional and countryspecific shocks in output volatility, variancedecomposition analysis is conducted.
 If a large share of variance is explained by theregional component, a regional policy may serve allcountries well.
 If a large share of variance is explained by the world
component, joining a broader currency area may bejustified.
 Otherwise, an independent monetary policy isnecessary to reduce the cost of adjustment to shocks.
Variance Decomposition: Computation
Var(yi ) = αi2Var(W) + βi2Var(Rr ) +Var(εi )
11σi2
VarW
( )=;Var(Rr ) =;Var(εi ) =
1−a2
1−br2
1−ci2
αi2
β i2
σ i2
Var (y i ) =++1 − a2
1 − br2
1 − c i2
S iw =αi2
1−a 2

Var (y i ); S ir =βi2
1 −br2

Var (y i );Si =σ i2
1 − ci2

Var (y i )13Output Variance Decomposition
East AsianJapan
Korea
China
HongkongSingapore
Malaysia
Indonesia
Philippines
ThailandTaiwanAverage
EuropeGermany
Belgium
Finland
Neitherlands
France
Italy
IrelandSpain
Portugal
Luxemburg
Austria
GreeceAverage
North America
USCanada
MexicoAverage
Latin America
BrazilArgentina
Chile
Colombia
Peru
Uruguay
Paraguay
Venezuela
Bolivia
Average

World

Regional%%

Countryspecific
%

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
6.285.89
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

Variance Decomposition: General Results
Surprisingly, the world component, on average,
merely accounts for less than 10 percent offluctuations in all regions.
 Country-specific factors account for a large share inoutput variance in all regions. They explain 64
percent and 52 percent of variance in East Asia andEurope respectively.
 Regional components explain significant shares ofvariance in East Asia, Europe and North America.
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Variance Decomposition: East Asia
On average, East Asian regional component
explains 32% of output variance.
 The regional component has negligible role inexplaining output variance in China andJapan.
 Quite strong synchronization (48%) is foundbetween East Asian NICs, except for Taiwan,
where world shocks and country-specificshocks are more influential.
East Asia vs. Europe

In general, East Asia exhibits less business cycle symmetry
than Europe: variance share of regional component is lower
and of country-specific component is higher.
Adjustment speed to country-specific shocks in East Asia isslower and associating cost of adjustment is probably bigger.
However, the gap between East Asia and Europe is not large.
If we compare the highly synchronized group of Korea,HongKong, Singapore, Malaysia, Indonesia and Thailand
with European average, the group appears even more suitablefor a currency area.

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Result: Juxtaposition
Bayoumi & Eichengreen (1994), Goto & Hamada(1994) and Goto (2002) find that East Asia is asplausible candidate as Europe for a currency area.
 Sato et al (2003) find less persuasive support for acurrency area in Asia and claim that only a subgroupof East Asian countries are possible candidates formonetary integration. They also find that adjustment
speed to shocks is faster in East Asia.
 Chow and Kim (2003) shows that in East Asia,
country-specific shocks are more important andtherefore, joining a currency area is not optimal.
Conclusion
East Asia is less plausible for a currency area
than Europe.
 However, a subgroup of countries in East
Asia might be suitable for a currency area
since they show higher degree ofsynchronization.
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Limitations and Scope forImprovement
Since we work with output volatility in lieu ofstructural shocks, information on shocks might beconflated with policy responses.
 The model can be extended to map the components'
disturbances into structural shocks, may be by usinga Factor-Augmented VAR framework.
 The model could also be extended to capture theevolution of synchronization over time.
 More aggregate variables, such as consumption,investment and price could be introduced into themodel.
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