Regionalism in East Asia: The Role of North East Asian Nations
Fithra Faisal Hastiadi
Waseda University, Graduate School of Asia-Pacific Studies
Abstract
Regionalism has now become a very popular phrase since this action has taken place
into every inch of the World, East Asian region is no exception. For the past few years,
regionalism has been progressing in East Asia in the form of Free Trade Agreements(FTAs) and Economic Partnership Agreements (EPAs). The likes of Japan, Korea andChina, as the economic front runners, are regarded to be the key actors in stimulating
regional economic growth through some opulent trade arrangements. In this sense, thetriangular trade agreement between Japan, Korea and China will become a significant
ingredient that can cope with the necessary condition in creating East Asian welfare.
Unfortunately, in the absence of such agreement, the present trade scheme between
Japan, Korea and China is not sufficient to meet the target. The inefficient intra
regional trade scheme is uncovered through Engle-Granger Cointegration and Error
Correction Mechanism which operate as long run and short run measurement
respectively. In order to accelerate the phase of growth, the more institutionalized
approach is needed. This triangular trade arrangement then become a major boost thatcome up with the spillover effect for the ASEAN4 which represents the South East Asian
nations as whole. In measuring the spillover effect, a static fixed effect model isestimated using the Two Stage Least Squares (2SLS)
Key words:

Triangular Trade, Engle-Granger Cointegration, Error Correction
Mechanism, Fixed Effect, Two Stage Least Squares

1

A. Introduction
In this new millennium, regionalism has begun to emerge in East Asia. East AsianCountries have been focusing on ways to expand intra regional trade that include: theestablishment of Regional Trade Agreements (RTAs) in the form of Free Trade
Agreements (FTAs) and Economic Partnership Agreements (EPAs). The trend towards
regionalism has created a profound regional and indeed global significance (Harvey andLee, 2002). Japan, Korea and China are regarded as the key actors for such action in
East Asia.
Being acknowledged as the economic front runners, Japan, China and Korea are
assumed to have heavy responsibility for the economic welfare in the East Asian region.
It is very obvious that East Asian regionalism cannot be put into practice without thesecountries’ strong support. Unfortunately, the lack of institutional arrangements among
these giant countries has stalled the overall welfare effect for the East Asian
communities. The present driving force of the China-Japan-Korea (CJK) relationship isthe market by which in some sense is not enough therefore the more institutionalized
approach is needed to join these activities so that it can sustain the economic growth in
the long run. The main focus of the institutionalization in trade is to make thesecountries grow together with which can make positive externalities throughout the East
Asian region. In the long run it is expected that CJK will lead to regionalism in EastAsia.
The structure of this paper proceeds as follows. The first section studies the
economic structures and trade patterns in the CJK. The next section examines the effectof openness in the CJK to economic growth in these particular countries. The third
section analyzes the prospects of the CJK increased welfare in creating spillover effectto ASEAN4, which in this paper serves as a proxy for ASEAN countries.
B. Japan, China and Korea Economic Relation
Tracing back the relations since the post war era, economic ties between Japan,
Korea and China has evolved in somewhat gradual ways. The evolution of tradeactivities emerged from the likes of China, which has a substantial transformation of
trade structures. In the early 90’s, primary commodities accounted for more than one
third of China’s total export to Japan and Korea. In this new millennium, it is still top
Chinese export to Japan and Korea, but it is persistently followed by the fast growth ofmachinery and transport (Chan and Chin Kuo, 2005). From this point of view, trade
within the north East Asian region is deemed to have substantial movement as a result2

from the shift of trade towards a more industrialized structure. The emergence of China
as a regional manufacturing center is a dominant factor that contributes the trade shift.
The overall picture of the trade amongst these countries is described in figure 1. It isclear that trade activity is very intense by which performs as the major contributing
factor for economic growth in the region. The vast amount of trade has been very likely
steered up by the amount of FDI flows among them with Japan as the sole leader of it(figure 2). In other words, the creation of economic transformation in China and Korea
that geared up the trade was enchanted by Japan’s role in making investment in thosecountries.
Figure 1. Trade among Japan, China and Korea(2006, $billion)

Source: Watanabe (2008)
Figure 2. Investment among Japan, China and Korea(2005, $billion)

Source: Watanabe (2008)3B.1. Measuring the short and the long run equilibrium of export to GDP
To some extent, trade is almost synonymous to a country’s welfare. Morespecifically, some research pointed out export as an engine of economic growth. From
this stand point, it is important to measure export sustainability to the economy, whichin this section export among the CJK become the main focus.
As already explained earlier, Japan, China and Korea are experiencing golden
period in doing export among them. Economic welfare is the most notable goal whichlinks in this activity, but is it sufficient to boost the economy in the long run? A puremarket driven activity without specific regional trade agreement might sometime create
bias. It is clear that Japan, Korea and China are lacking of such agreement among them
(Urata and Kiyota, 2003) as described in the table 1.
Table 1. Japan, China and Korea FTAs/EPAs

Countries

China

Situation

Countries

Concluded

Chile, ASEAN, Hong Kong, Macao

Under Negotiations
Under Considerations

Korea

SACU
Iceland, India, Japan-Korea-China, FTAAP,
Switzerland

Concluded

Chile, Singapore, EFTA, ASEAN, USA

Under Negotiations

India, Mexico, Canada, EU

Under Considerations

ConcludedJapan

NZ, Australia, Pakistan, Singapore, GCC,

Under Negotiations
Under Considerations

FTAAP, China, Mercosur, NZ, South Africa,
Japan-China-Korea, Australia, GCC
Singapore, Mexico, Malaysia, Philippines,
Chile, Thailand, Brunei, Indonesia
India, Vietnam, Australia, Switzerland,
Korea, GCC, ASEAN
FTAAP, Japan-China-Korea, South Africa

Source: Japanese Ministry of Economy, Trade and Industry, 2007

To make an effective regionalism, Japan, China and Korea should support eachother. Therefore, intra regional cooperation within the CJK must take place by which4can create sustainable growth in East Asian region. The following sections serve toprove export sustainability to economic growth, in the absence of trade arrangements,
for the short and the long run. Engle-Granger Cointegration and Error Correction
Mechanism 1 test are then employed for this cause.
B.1.1. Defining the Long Run Equilibrium: Engle Granger Cointegration Test
In doing Engle Granger Cointegration test, this paper divides the export relationshipin to three parts which are described in the following equations:i. China and Japan Export Relationship
JPGDP =
β 0 + β1 ExportCH + ut

(1)

CHGDP =
β 0 + β1 ExportJP + ut

(2)

ii. Korea and Japan Export Relationship
KRGDP =
β 0 + β1 ExportJP + ut

(3)

JPGDP =
β 0 + β1 ExportKR + ut

(4)iii. China and Korea Export Relationship
CHGDP =
β 0 + β1 ExportKR + ut

(5)

KRGDP =
β 0 + β1 ExportCH + ut

(6)

In these equations, JPGDP, CHGDP and KRGDP are Japan’s GDP, China’s GDP,and Korea’s GDP respectively while Export JP, Export CH and Export KR are thevariables of export destinations to Japan, China and Korea. It would be possible tocointegrate Export and GDP since the trend in export and GDP would offset to eachother, creating a stationary residual. The residual is called a cointegration parameter. In
the data, if we find that the initial regression of the residual (ut) gives stationarity itmeans that ut is stationary at order 0 (level) and it is notated as I(0). But if ut is stationer
in first difference, the variables of Export and GDP will be cointegrated in the first

1

This test employs time series quarterly data of GDP and for Japan, China and Korea ranging from 1985 to 2004.
The data is taken from CEIC database5difference which can be notated with I(1).
Table 2. Cointegration Parameters

Dependent Variables
GDP (Japan)

GDP (China)

GDP (Korea)

Export to Japanna

Stationer

Stationer

Export to China

Stationerna

Stationer

Export to Korea

Stationer

Stationerna

Independent Variables

From table 2 we can see that, GDP and export relationship in the CJK yields
stability in the long run. It is proven by the stationarity of the error term in each of thecases. The cointegration test that proves long run equilibrium describes that the model is
not spurious. Export is proven to be the engine of economic advancement in thesecountries. It approves some previous research as the likes of Heller and Porter (1978),
Feder (1983), Ram (1985), Dorasami (1996), Ghatak, Subrata, Milner, Utkulu (1997)
and Ekanayake (1999) of export and economic growth relationship.
B.1.2 Defining the Short Run Equilibrium: Error Correction Model
We long run equilibrium is called Error Correction Mechanism (ECM). The modelof ECM is as have seen the long run relationship between Export and GDP. However, inorder to make it objective, we should also see the short run since it is still plausible toperceive disequilibrium.
Thus, U t GDPCountryX − β 0 − β1 ExportCountryY could be=
noted as equilibrium error. This error then could be used to relate the behavior of theshort run Japanese GDP to its long run. The technique to correct short-run
disequilibrium to the follows:∆GDPCountryX = β 0 + β1∆ExportCountryY + β 2ut −1 + et(7)

ut −1 is a cointegrated error lag 1, or could be noted mathematically as:=
U t −1 GDPCountryX t −1 − β 0 − β1 ExportCountryYt −1(8)

In this equation, ∆GDPCountryX is the difference in GDP for Japan, Korea andChina, while ∆ExportCountryY is the difference in export from country X to Country6

Y. As for example, ∆GDPJapan = β 0 + β1∆ExportChina + β 2ut −1 + et applies for theeffect of Japan’s export to China on Japan’s GDP. From the above model we can seethat the long run relation between Export and GDP in Japan, China and Korea would bebalanced by the previous error. Below is the output for each country’s regressions:
Table 3. Equilibrium Errors

Dependent Variables
Independent Variables
Equilibrium error for Export toJapan
Equilibrium error for Export to
China
Equilibrium error for Export toKorea

GDP (Japan)

GDP (China)

GDP (Korea)na-1.0 9 ***-0.23 *

-0.18 ***

na

-0.48 ***

0.017773-1.33 ***

na

Note: Statistical significance is indicated by *(10%), **(5%), and ***(1%)

i. JapanIn the short run, there is an equilibrium error for Japan’s Export to China with its
relation to Japan’s GDP. The coefficient of residual gives negative sign (-0.18), whichmeans that Japan’s Export to China is below the long run equilibrium. This will only
lead to a rise of export for the following periods. But it is important to note that theabsolute value of the coefficient (adjustment rate) is very small (0.18). This suggeststhat Japan’s Export to China is moving in a slow phase to reach the long run
equilibrium.
As for the relationship between Japan and Korea, the equilibrium error of the export
trend is not significant. These suggest that Japan’s GDP is adjusting to the change inJapan’s export to Korea in the same period of time. In other words, Japan and Korea
relationship in terms of export has already reached steady state level.
ii. China
The residuals for the relationship between China’s GDP with China’s Export toJapan and Korea are significant. These suggest that there is an equilibrium error in the
short run. The negative signs put the Export for a constant rise to reach the long run
equilibrium. In China’s case, the adjustment rate or the phase of acceleration for the7

long run equilibrium is very fast. It can be seen through the absolute value of theequilibrium error coefficients which are 1.09 and 1.33 for China’s relationship to Korea
and Japan respectively.
iii. Korea
Korea’s case is somewhat similar to China. The residuals for the relationshipbetween Korea’s GDP with Korea’s Export to Japan and China are significant. It yields
similar explanation with China’s case. However, the adjustment rate for the case of
Korea is slower than China’s but it is still faster than Japan’s. It gives the absolute valueof 0.23 and 0.48 for Korea’s trade relationship to Japan and China respectively.
B.1.3. Interim conclusion
From the ECM, we can conclude that North East Asian region is not moving at thesame phase to reach the long run equilibrium, which in this case Japan is the slowest
one. The insignificant value of acceleration rate for the case of Japan trade relationshipwith Korea is also important point to note since it can be interpreted as an exhausted
Korean market for Japanese products (steady state condition). These facts are very
crucial since it diminishes Japan’s role as the sole leader in the north East Asia.
Although whoever the leader is not to important, but the stalled effect of a country’s
economic growth in these region will only serve as stumbling blocks in creating East
Asian welfare. In order to strengthen regional welfare and accelerate the phase ofadjusting, economic integration must take place.
C. The Openness in Trade
Greater economic interdependence between Japan, China and Korea will act well as
the base of creating regionalism. In this sense, triangular trade agreements thatdismantle trade barriers will smooth the progress of improved trade flows among thesecountries by means of greater market access. But unfortunately, this supporting
environment only operates as fact in a sheet. The process of regionalism in this area isproven to be difficult.
These countries may have aggressively reached other countries in making FTA’sand EPA’s but none of which have been progressing among them (see table 1). The
reason of it will be a subject for another research, while this section tries to focus on theeffect of such agreement 2 to the economy. The lack of trade arrangements that2

Regional trade agreement provides openness to some sectors of economy

8liberalize the sector of economy is being noted as the main factor that contributes intra
regional trade ineffectiveness in north East Asia. This hypothesis will be proved in thefollowing sections to come.
C.1 Openness with customized RPL index
Export lead growth approach that has been done in the previous section withcointegration and error correction model has actually provided the basis to measure
openness 3 of a country, but in some ways this alone is not enough. It only works forconfirming the paradigm of trade as an engine of growth but it is not sufficient tomeasure a more robust pattern of openness. Therefore, we then may have to address
Dollar’s Relative Price Level (RPL index).
This index is a measure of outward orientation of an economy that is based oninternational comparisons of price levels compiled for 121 countries by Summers andHeston (1988). They price the same basket of consumption goods in domestic currency
in different countries and then convert the measure into US dollars using the official
exchange rate. Using the US as the benchmark country, the index of country i's relative
price level (RPL) is:
RPLi = 100 X Pi/Pus X 1/e(10)
Where e is the exchange rate (no. of units of domestic currency per unit dollar) and Pi isthe consumption price index for country i and Pus is the consumption price index for US.
Hence, one could use cross-country variations in these price levels to measure inwardor outward-orientation resulting from trade policy. With using the same analogy, thispaper then customizes the RPL index into this formula:
RPLi = 100 X Pi/Ptp X 1/e(11)
Where Ptp is the consumption price index for the trading partner and e is the exchange
rate (no. of units of domestic currency per unit of trading partner currency). The
customized RPL is then become a powerful tool to analyze trade openness between the
trading countries.
C.2 Error Correction Mechanism (ECM) of RPL index and GDP
As already explained in the previous section, ECM provides the description of short

3

Several cross-country studies investigating outward orientation and growth have used export growth as a proxy foroutward orientation. The main examples of this approach are Michaely (1977), Heller and Porter (1978), Feder(1983), Ram (1985) and more recently Levin and Raut (1997).

9

run shock. In this particular case 4, we examine the openness vis a vis trade liberalization
trend in north East Asia region.

∆GDPCountryX = β 0 + β1∆RPLCountryY + β 2ut −1 + et

(12)This equation mimics equation 7, but the previous dependent variable is substituted
from export to RPL in order to suit the goal which is to measure the openness.
∆GDPCountryX is the difference in GDP from Japan, Korea or China, and∆RPLCountryY is the difference in RPL from a country X to Country Y.
∆RPLCountryY measures the openness of trade from of country X towards Y. Belowis the outputs for each country:
Table 4. Cointegration Parameters

Dependent Variables
Independent Variables
Equilibrium error forOpenness to Japan
Equilibrium error forOpenness to China
Equilibrium error forOpenness to Korea

GDP (Japan)

GDP (China)

GDP (Korea)na-1.23 ***

-1.31 ***

-1.15 ***

na

-0.97 ***

-0.72 **

-1.24 ***

na

Note: Statistical significance is indicated by *(10%), **(5%), and ***(1%)

From this particular test we can see that generally trade openness is affecting acountry’s GDP in a positive way. But in the short run, trade openness in the CJK is still
below the equilibrium. This suggests that trade openness is still finding its form in thisarea. Although we might not see regionalism which liberalize trade in the short run, but
the trend towards openness in trade vis a vis regionalism is progressing in a respectfulmanner. We can see this through the adjustment rate for the long run equilibrium (thecoefficients of residuals) that yields an average of 1.1, consequently we might see
regionalism in North East Asia happen in the future.

4

This test employs time series quarterly data of Exchange rate, CPI, Export for CJK ranging from 2001 to 2005, thedata is taken from CEIC data base10

D. The Spillover Effect from Japan-Korea-China Triangular Trade to ASEAN 4
As giants of Asia, the growth of Japan, Korea and China will most likely create
positive effect to the neighboring countries. Regionally speaking, the growth of North
East Asia will boost the East Asian growth as whole, in this sense we might want toexercise its effect to ASEAN countries. To simplify things, this paper limits the effect to
ASEAN4 since these countries have the same economic characteristics. This paper
employs static panel data 5 model for this purpose. The following sections provide theanalysis.
D.1 Examining the spillover effect through panel data modelA static panel data model can be specified as follows:(13)
Yit= Xitβ+λt+ηi+εit t=1,...,T i=1,...,N
Where:
λt and ηi are time and individual specific effects respectively, x it is a vector of the
explanatory variables, (i) is the time component of the panel, (N) is the cross-section
dimension (or the number of cross-section observations), and N x T is the total
number of observations. The idea is to run the models in order to have a consistentestimator for the β coefficients, and the model (fixed or random) choice depends on
the hypothesis assumed for the relationship between the error-term (εit ) and theregressors (x it ). The static panel data analysis developed in the empirical section of
the paper was based on two basic panel models, the fixed (FE) and the random (RE)effect models. The FE estimator uses a transformation in order to remove theunobserved effects (αi ) and any time-constant explanatory variable. A general
representation of a FE model is:yit=

β i xit + α i + ε it

(14)

Where i= 1,...N and t= 1,...T , where (x) represents the explanatory variables, (y) is thedependent variable and (εit ) the error term.
If we consider the average over time we have the following:y it= β i x it + α i + ε it(15)

Subtracting (14) from (15) for each (t) we have:5

The panel data is analyzed annually from 1989 to 2007 which consist of ASEAN 4’s Export, Import, Consumption,

Investment, Government expenditure, GDP, and GDP of Japan, China, Korea. The data is taken from WDI online
database11

yit − y=β i ( xit − x it ) + ε it + ε it
it



y it
or =

βi x it + ε it

(16)
(16a)

The FE transformation is called the within transformation and the FE estimator or
the within estimator, which is the ordinary least square (OLS) estimation of equation(16a), the pooled OLS. Under the assumption of strict exogeneity for the explanatory
variables ( E (ε it / xit , α i ) = 0) the FE estimator is unbiased. If any explanatory
variable is constant over time for all (i), it is swept away by the FE transformation( x it = 0) the OLS estimation by FE also requires that the errors are homoskedastic andserially uncorrelated over time.
The RE estimator is more adequate if we think that the unobserved effect is notcorrelated with all the explanatory variables and the estimation is carried on by ageneralized least square (GLS) estimation. The equation representing the RE model is:yit = β0 +βixit +αi +εit
(17)
If one thinks of the unobserved effect ( αi ) as uncorrelated with each explanatory
variable (x it ) using a transformation (FE estimator) to eliminate (αi) will result ininefficient estimators. Estimation of equation (17) for (αi) uncorrelated with the
explanatory variables is what we call the RE model. If one defines the composite error
terms as (υit =α +ε ), equation (17) can be written as:
y it = β0 +βi x it + uit(18)

In this case we have to remember that ( uit ) are serially correlated over time and thepooled OLS estimator is not the choice since it ignores the positive serial correlationand the idea is to use the GLS to take into account to resolve the serial correlation
problem. The GLS estimation will be a pooled OLS estimation of the transformedmodel, which can be represented as follows:

yit − λ y i =β 0 (1 − λ ) + β i ( xit − λ x it ) + (uit − λ ui )(19)

Where λ =1 − σ ε2 (σ ε2 + T σ α2 )  for ( x it = 0) 0 < λ < 1
One of the advantages of using such transformation and the RE model is that itallows for explanatory variables that are constant over time. By examining equation(18) one can relate the RE estimator (pooled OLS known as POLS) and FE where the12

POLS is obtained for the case where λ = 0 (the unobserved effect, αi is not important)while the FE is the estimator for λ = 1. The choice between the FE and the RE
estimators is based on whether the unobserved effects (αi ) can be considered asparameters to be estimated or as an outcome of a random variable, suggesting the use ofa FE or a RE model respectively. Since the time periods (1989-2007) exceed theindividual observations (Indonesia, Malaysia, Thailand, Philippines) therefore fixed
effect model is considered as the most appropriate method (Nachrowi and Usman,2008). The model is described as follows:

Yit = α + β X it + γ 1W1t + γ 2W2t + γ 3W3t + ... + γ NWNt + δ1Zi1 + δ 2 Zi 2 + δ 3 Zi 3 + ... + δ t ZiT + eit (20)Where:Yit=

GDP growth of ASEAN 4 for time t and country i

Xit=

Independent Variables (ASEAN 4 consumption growth, investment
growth, government expenditure growth, export-import growth andJapan-China-Korea 6 GDP growth for time t)

W and Z are dummy variables which are defined as follows:
it

W

Z

it

it

it

=1

for country i, where i = Indonesia, Malaysia, Philippines, Thailand=0

for others=1

for Period t where t = 1989, 1990..., 2007=0for others

The above structural equation is actually a simultaneous equation 7 in which employs
causality relationship. To see the simultaneity, the above model can be decomposed into
six parts:

Yt =β1 + β 2Ct + β3 I t + β 4Gt + β5 X t + β 6 M t + β 7 JGDPt + β8CGDPt + β9 KGDPt

6

Japan, Korea and China GDP are included in the structural equation(21)

referring to Tran Van Hoa’s (2003)

assessment in the model
7

The model is simultaneous because we cannot determine C, I, G,X, M or Y without knowing the other13Ct =+
β1 β 2Ct −1 + β3Yt

(22)

I t =β1 + β 2 rt + β3Yt

(23)

Gt =+
β1 β 2Yt −1 + β3Yt

(24)

Xt =
β1 + β 2 EX t + β3Ct + β 4 JGDPt + β5CGDPt + β 6 KGDPt

(25)

Mt =
β1 + β 2 EX t + β3Ct + β 4 JGDPt + β5CGDPt + β 6 KGDPt(26)

Equation 21 describes the effects of ASEAN 4 consumption (Ct), investment (It),government expenditure (Gt), export (Xt), import (Mt) growth and the North East AsianGDP growth (JGDPt, CGDPt, KGDPt) on ASEAN 4 GDP growth (Yt). From the model,
it is clear that consumption growth, investment growth, government expenditure growth,export growth and import growth have their own determinants that simultaneously form
the structural equation. Consumption growth (Ct) is formed by last year’s consumption
growth (Ct-1 ) and the present GDP growth (Yt), Investment (It) on the other hand is
influenced by the interest rate (rt) and the GDP growth (Ct). It is also expected thatexchange rate (EXt), consumption growth (Ct) and trading partners economic growth(JGDPt, CGDPt, KGDPt) have some influences on trade flows (Xt and Mt) in ASEAN 4.
From the structural equation, we can divide the variables into two, endogenous andpredetermined (exogenous). The first one is treated as stochastic while the latter as non
stochastic. To see which simultaneous model that can satisfies the need, we have toaddress the identification process. If K is the number of exogenous variables within themodel, k is the number of exogenous variables within the equation and M is the numberof endogenous variable within the model, so the criteria to state whether an equation is
unidentified, just identified, or over identified are describe as follows:
If K-k < M-1, so the equation is unidentified
If K-k = M-1, so the equation is exactly identified
If K-k > M-1, so the equation is over identified

14

Based form the above criteria, table 5 summarizes the order condition from the system:
Table 5. Order condition
No

Equation

Criteria

Conclusion

1

Yt

11 > 5

Over Identified

2Ct

13 > 1

Over Identified

3It

13 > 1

Over Identified

4Gt

13 > 1

Over Identified

5Xt

10 > 1

Over Identified

6Mt

10 > 1

Over Identified

For the case of over identified, we might want to employ two stage least squares(2SLS) 8 approach as an elegant way to deal with such problem. 2SLS regression
analysis, as suggested by Angrist and Imbens (1995), assumes that there is a secondary
predictor that is correlated to the problematic predictor but not with the error term.
Given the existence of the instrument variable, 2SLS regression analysis uses thefollowing two methods: In the first stage of the two-stage least squares 2SLS regression
analysis, a new variable is created using the instrument. In the second stage of the 2SLS
regression analysis, the model-estimated values from stage one are then used in place of
the actual values of the problematic predictors to compute an OLS model for the
response of interest. Below is the detailed procedure of 2SLS:In stage one, least square regression on the reduced form equation has to take place bywhich it can yields Ct-1, Yt-1, rt, EXt, JGDPt, CGDPt, KGDPt as the instrumental
variables, therefore equation 21 up to 26 has to be transformed into reduced form
equation as the followings:

Yt = Π1 + Π 2Ct −1 + Π 3Yt −1 + Π 4 rt + Π 5 EX t + Π 6 JGDPt + Π 7CGDPt + Π 8 KGDPt(27)

Ct = Π 9 + Π10Ct −1 + Π11Yt −1 + Π12 rt + Π13 EX t + Π14 JGDPt + Π15CGDPt + Π16 KGDPt (28)I t = Π17 + Π18Ct −1 + Π19Yt −1 + Π 20 rt + Π 21 EX t + Π 22 JGDPt + Π 23CGDPt + Π 24 KGDPt (29)8

Two-stage least squares regression (2SLS) is a method of extending regression to cover models which violate

ordinary least squares (OLS) regression's assumption of recursivity, specifically models where the researcher must
assume that the disturbance term of the dependent variable is correlated with the cause(s) of the independent
variable(s)15Gt = Π 25 + Π 26Ct −1 + Π 27Yt −1 + Π 28 rt + Π 29 EX t + Π 30 JGDPt + Π 31CGDPt + Π 32 KGDPt (30)X t = Π 33 + Π 34Ct −1 + Π 35Yt −1 + Π 36 rt + Π 37 EX t + Π 38 JGDPt + Π 39CGDPt + Π 40 KGDPt (31)M t = Π 41 + Π 42Ct −1 + Π 43Yt −1 + Π 44 rt + Π 45 EX t + Π 46 JGDPt + Π 47CGDPt + Π 48 KGDPt (32)Note: Π isβ
1− β

∧ ∧ ∧ ∧ ∧ ∧From stage one, we get Y t , C t , I t , G t , X t , M t as the fitted values with which we
can run for the second stage. In stage two, these fitted values are then plugged in to themain equation. The last step is to run least squares on each of the above equations to get
2SLS estimation as described below in table 6.
Table 6. Two Stage Least Squares Regression Output

Dependent Variables

Y

Independent Variables

C

I

G

X

M

C

0.386 ** nanana-0.530 *0.926 ***

I

0.069 *

na

na

na

na

na

G

0.173 ** na

na

na

na

na

X

0.065

na

na

na

na

na

M

0.03

na

na

na

na

na

Y

na

0.794 *** -0.08 0.692 *** nanaInstrumental variables
Y (Japan)0.559 ** nanana

2.654*** 3.084 **Y (China)

0.352 ** nanana

1.174 *** 0.642

Y (Korea)

0.218 *

na

na

na

-4.765

0.394

C (-1)

na

0.01

na

na

na

na

r

na

na

0.138 nanana

Y (-1)

nana

na-0.268 ** na

na

EX

na

na

na

na

0

0

Note: Statistical significance is indicated by *(10%), **(5%), and ***(1%)

From the output above we can conclude that the North East Asian (Japan, Korea andChina) economic growth boost the ASEAN4 economic growth, it confirms the16

proposition of this paper. Investment flows, in the form of FDI, has also operated as adominant integrating power in East Asia as whole. Although we cannot find legitimate
determinant for FDI 9 in the output, but it is clear that FDI is trade related in nature(Wong, 2004). With its essentially open and outward-looking economies, the region ishighly dependent on foreign investment for its economic growth. But still, the boosting
power is not as much as in the spillover effect from the giant countries of Japan, Korea
and China. Japan, in terms of GDP growth, has the biggest influence towards ASEAN4
followed by China and Korea at the second and third place. This fact is described by thecoefficient parameter that gives the value of 0.559, 0.352 and 0.218 for Japan, China
and Korea respectively.
The ranking of influence is presumably caused by the number FDI inflows to
ASEAN from these countries as described below in table 7. The only bias is on China
and Korea, even though the cumulative FDI from Korea to ASEAN4 was bigger thanChina’s, but it does not seem to be reflected on the ranking of influence. As for this, it isassumed that the high economic growth rate of China had been the major contributing
factor (Urata, 2008) that overtook the influence of Korea’s cumulative FDI flow to
ASEAN4. However, such factor is not enough to surpass 10 Japan’s influence to
ASEAN4’s economic growth since Japan’s FDI contribution to ASEAN4 outweighed
China’s by more than one hundred folds.
Table 7. FDI flows to ASEAN 4 (US$ million)

Total
Host country

Indonesia

Thailand

Malaysia

Phillipines

Cummulative
1995-2003

Source CountryJapan

288.06

8,096.02

4,761.11

3,055.68

16200.87

Korea

331.88235.5898.51

238.13

904.1

China

-36.7850.16

120.72

4.07

138.17

Source: ASEAN secretariat

9

it is described by the insignificant value of interest rate and GDP growth towards investment (table 6)

10

From the ECM simulation as confirmed earlier, I found that China has taken over Japan’s role in East Asia. But

this is true if we address the long run effect. This section only measures the present condition in the absence of theintertemporal problem.

17

The story goes hand in hand with the flying-geese hypothesis that was developed by
Japanese economist, Kaname Akamatsu (1935). This model has beeen frequently
proposed to examine the patterns and characteristics of East Asian economic integration.
The premise of the flying-geese pattern suggests that a group of nations in this region
are flying together in layers with Japan at the front layer (Xing, 2007). The layers
signify the different stages of economic development achieved in various countries. In
the flying-geese model of regional economic development, Japan as the leading gooseleads the second-tier geese (China, Korea) which, in their turn, are followed by thethird-tier geese (ASEAN4).
Another important thing to note is the insignificant value of trade flows within
ASEAN4 in terms of creating GDP growth. These are intriguing facts since thesevariables, especially export are considered as the main determinant of GDP growth. In
terms of export, it is suspected that the effect of rivalry between ASEAN4 members andChina is the main factor which creates insignificant value. This point is supported by
Roland-Host and Weiss (2004) that pointed out China’s emergence for creating shortand medium term direct and indirect competition between ASEAN and China. They
argued that ASEAN and China are experiencing intensified export competition inprominent third markets. This can lead to painful domestic structural adjustments within
the ASEAN in the short run.
E. Conclusion
We have made an interim conclusion that export leads the overall growth in North
East Asia. However, it is important to note that Japan’s phase of adjustment towards
long run equilibrium is quite slow compared to the likes of Korea and China. This only
yields as a stumbling block in forming regionalism in East Asia. The hard task is about
making these countries move together in the same phase, which is why regionalism has
to take place.
Since regionalism is an abstract term, the use of RPL index is essential. RPL index
is a proxy of outward orientation of a country or in other words it is a representation ofregionalism. Regionalism in this case goes hand in hand with openness in which it
creates trade arrangements that liberalize some sectors in the economy. The ECM
simulation gives a clear picture of the current form of openness which is below theequilibrium. It suggests that the trend towards regionalism is still far behind. It
somewhat confirms the ineffectiveness of current triangular trade in North East Asia. It
is expected that regionalism can eliminates such bias in trade.

18

Moreover, since North East Asian countries has a big scale of economy, its
economic development will substantially affect the neighboring countries in East Asia
specifically ASEAN4. It is demonstrated by the large share of China-Japan-Korea
growth that affects ASEAN4’s GDP.
In the short run, there is a rivalry competition between China and ASEAN.
However, in the long run regionalism is expected to accommodate export growth for
East Asia as whole. The growing significance 11 of China, Japan and Korea market for
ASEAN4 will serve as the basis for regionalism. Thus, a unified East Asia could
accelerate the momentum of overall trade liberalization, boost global economic growth,and contribute to international peace.

11

It is shown from table 6 at export and import column equation in which ASEAN 4 trade tends to rely on themarket size in North East Asia (Japan, Korea and China)

19

APPENDIX
Output 1- Engle Granger Cointegration testi.

China and Japan Export Relationship

Stationarity of ut (error term) in level (China’s GDP and China’s Export to Japan) :ADF Test Statistic -8.506548

1%

Critical Value*-3.5745

5%

Critical Value-2.9241

10% Critical Value-

2.5997*MacKinnon critical values for rejection of hypothesis of a unit root.
Stationarity of ut (error term) in level (Japanese GDP and Japanese Export to China) :ADF Test Statistic -3.63403

1%

Critical Value*-3.51645%

Critical Value-2.8991

10% Critical Value-2.5865*MacKinnon critical values for rejection of hypothesis of a unit rootii.

Korea and Japan Export Relationship

Stationarity of ut (error term) in first difference(Korea’s GDP and Korea’s Export to Japan)
ADF Test Statistic -4.495617

1%

Critical Value*-3.57785%

Critical Value-2.9256

10% Critical Value-2.6005*MacKinnon critical values for rejection of hypothesis of a unit root.
Stationarity of ut(error term) in first difference (Japan’s GDP and Japan’s Export to Korea)
ADF Test Statistic -12.3212

1%

Critical Value*-3.51765%

Critical Value-2.8996

10% Critical Value-2.5868*MacKinnon critical values for rejection of hypothesis of a unit root.

iii.

China and Korea Export Relationship

Stationarity of ut (error term) in level (China’s GDP and China’s Export to Korea) :ADF Test Statistic -8.527721

1%

Critical Value*-3.5745

20

5%

Critical Value-2.9241

10% Critical Value-2.5997*MacKinnon critical values for rejection of hypothesis of a unit root.
Stationarity of ut (error term) in first difference (Korea’s GDP and Korea’s Export to China)
ADF Test Statistic -5.511553

1%

Critical Value*-3.57785%

Critical Value-2.9256

10% Critical Value-2.6005*MacKinnon critical values for rejection of hypothesis of a unit root.

Output 2- Error Correction Mechanism- Export
i. Japan
Dependent Variable: D(JGDP)
Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(EXPORT_CHINA)

79.34343

14.21718

5.580811

0.0000RESIDCHINA(-1)-0.188456

0.051381-3.667828

0.0005C

319.2721

601.0857

0.531159

0.5969Dependent Variable: D(JGDP)
Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(EXPORT_SKOREA)

24.36957

35.63698

0.683828

0.4962

D(RESIDSKOREA(-1))

0.017773

0.115652

0.153676

0.8783C

687.5205

744.3848

0.923609

0.3587ii. China
Dependent Variable: D(CHINA_GDP)
Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(CHINAX_KOREA)

8.115691

1.518337

5.345117

0.0000RESIDCK(-1)-1.095939

0.141987-7.718576

0.0000C-299.6504

336.1793-0.891341

0.3775

Dependent Variable: D(China_GDP)
Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(CHINAX_JAPAN)

3.115135

0.499213

6.240097

0.000021

RESIDCJ(-1)-1.339769

0.136642-9.804952

0.0000C-194.4002

311.7928-0.623491

0.5361

iii. Korea
Dependent Variable: D(KOREA_GDP)
Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(KOREAX_JAPAN)

0.013880

0.013709

1.012480

0.3168D(RESIDKJ(-1))-0.230808

0.121473-1.900076

0.0640C

2364.437

1908.209

1.239087

0.2219Dependent Variable: D(KOREA_GDP)
Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(KOREAX_CHINA)

0.011478

0.008709

1.317948

0.1943

D(RESIDKC(-1))-0.480382

0.150663-3.188459

0.0026

C

1597.449

1956.605

0.816439

0.4186

Output 3- Error Correction Mechanism-Openness
i. China
Dependent Variable: D(CGDP)
Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(RPL_CHINA_JAPAN)-21.01026

15.83907-1.326483

0.2075RESIDCHINA_JAPAN(-1)-1.237266

0.278334-4.445251

0.0007C

277.6646

891.3166

0.311522

0.7603

Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(RPL_CHINA_KOREA)

0.800948

2.017440

0.397012

0.6978RESIDCHINA_KOREA(-1)-1.246601

0.277345-4.494771

0.0006

C

703.6690

1049.855

0.670254

0.5144

Dependent Variable: D(CGDP)

22

ii. Japan
Dependent Variable: D(JGDP)
Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(RPL_JAPAN_CHINA)

2829.407

2261.036

1.251377

0.2328RESIDJAPAN_CHINA(-1)-1.153234

0.252245-4.571876

0.0005

C

188.6636

771.5965

0.244511

0.8107

Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(RPL_JAPAN_KOREA)

6.250816

29.32263

0.213174

0.8345RESIDJAPAN_KOREA(-1)-0.725212

0.291557-2.487374

0.0272

C

451.0064

1067.183

0.422614

0.6795

Dependent Variable: D(JGDP)

iii. Korea
Dependent Variable: D(KGDP)
Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(RPL_KOREA_CHINA)

27746.39

89550.82

0.309840

0.7616RESIDKOREA_CHINA(-1)-1.313607

0.265966-4.939012

0.0003C

2663.265

2886.817

0.922561

0.3730

Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(RPL_KOREA_JAPAN)-2959.973

6915.377-0.428028

0.6756

Dependent Variable: D(KGDP)

23

RESIDKOREA_JAPAN(-1)-0.973663

0.265459-3.667854

0.0028C

2992.972

2971.181

1.007334

0.3322

Note:- The RPL_CountryX_CountryY is the openness of country X to country Y- Resid is the equilibrium error- JGDP, CGDP, KGDP stands for Japan’s GDP, China’s GDP and Korea’s GDP respectively

Output 4- Two stage Least Squares with Fixed Effecta. GDP growth equation
Estimation Method: Two-Stage Least Squares
Included observations: 18
Total system (balanced) observations 72
Coefficient

Std. Error

t-Statistic

Prob.

C(9)

-4.442803

1.463603-3.035523

0.0035C(1)0.3861300.1462512.640183

0.0105C(2)0.069554

0.036615

1.899605

0.0623C(3)0.172738

0.075332

2.293019

0.0254C(4)0.065033

0.100882

0.644643

0.5216C(5)0.030237

0.0821190.368207

0.7140C(6)0.218533

0.110082

1.985188

0.0517C(7)0.559444

0.279331

2.002800

0.0497C(8)0.352340

0.146693

2.401888

0.0194C(10)-3.446255

1.383094-2.491700

0.0155C(11)-4.479309

1.519311-2.948250

0.0045C(12)-4.052083

1.455409-2.784155

0.0072

Determinant residual covariance82.57287

Equation: OGDPG_INA = C(9) + C(1)*CEG_INA + C(2)*FDIG_INA +C(3)*GOVEX_INA + C(4)*OXGR_INA + C(5)*OIMG_INA + C(6)*KGDPG_INA + C(7)*JGDPG_INA + C(8)*CGDPG_INA
Observations: 18
R-squared

0.909899

Mean dependent var

4.930058

Adjusted R-squared

0.829809

S.D. dependent var

4.954718

24

S.E. of regression

2.044032

Durbin-Watson stat

2.935520

Sum squared resid

37.60262

Equation: OGDPG_MALAY = C(10) + C(1)*CEG_MALAY + C(2)*FDIG_MALAY + C(3)*GOVEX_MALAY + C(4)*OXGR_MALAY +C(5)*OIMG_MALAY + C(6)*KGDPG_MALAY + C(7)*JGDPG_MALAY + C(8)*CGDPG_MALAY
Observations: 18
R-squared

0.610977

Mean dependent var

6.515177

Adjusted R-squared

0.265180

S.D. dependent var

4.211082

S.E. of regression

3.609809

Sum squared resid

117.2765

Durbin-Watson stat1.358905

Equation: OGDPG_PHILP = C(11) + C(1)*CEG_PHILP + C(2)*FDIG_PHILP + C(3)*GOVEX_PHILP + C(4)*OXGR_PHILP + C(5)*OIMG_PHILP + C(6)*KGDPG_PHILP + C(7)*JGDPG_PHILP +C(8)*CGDPG_PHILP
Observations: 18
R-squared-0.015997

Mean dependent var

3.825081

Adjusted R-squared-0.919106

S.D. dependent var

2.347669

S.E. of regression

3.252269

Sum squared resid

95.19526

Durbin-Watson stat0.731796

Equation: OGDPG_THAI = C(12) + C(1)*CEG_THAI + C(2)*FDIG_THAI+ C(3)*GOVEX_THAI + C(4)*OXGR_THAI + C(5)*OIMG_THAI +C(6)*KGDPG_THAI + C(7)*JGDPG_THAI + C(8)*CGDPG_THAI

Observations: 18
R-squared

0.860603

Mean dependent var

5.158724

25

Adjusted R-squared

0.736694

S.D. dependent var

4.858676

S.E. of regression

2.493150

Sum squared resid

55.94217

Durbin-Watson stat1.420589

b. Consumption growth equation
Estimation Method: Two-Stage Least Squares
Included observations: 18
Total system (balanced) observations 72
Coefficient

Std. Error

t-Statistic

Prob.

C(3)

1.886302

0.966225

1.9522400.0552C(1)0.010831

0.089199

0.121430

0.9037C(2)0.794404

0.098395

8.073635

0.0000C(4)1.452987

1.057763

1.373642

0.1742C(5)0.427812

0.865384

0.494361

0.6227C(6)0.469461

0.945216

0.496670

0.6211

Determinant residual covariance2932.878

Equation: CEG_INA = C(3) + C(1)*CEG_INA(-1) + C(2)*OGDPG_INA
Observations: 18
R-squared

0.635897

Mean dependent var

5.867054

Adjusted R-squared

0.587350

S.D. dependent var

4.787579

S.E. of regression

3.075439

Sum squared resid

141.8749

Durbin-Watson stat

2.413869

Equation: CEG_MALAY = C(4) + C(1)*CEG_MALAY(-1) + C(2)*OGDPG_MALAY
Observations: 18
R-squared

0.623171

Mean dependent var

6.702001

Adjusted R-squared

0.572927

S.D. dependent var

4.758304

S.E. of regression

3.109589

Sum squared resid

145.0432

Durbin-Watson stat1.290308

Equation: CEG_PHILP = C(5) + C(1)*CEG_PHILP(-1) + C(2)*OGDPG_PHILP
Observations: 18
R-squared-0.415674

Mean dependent var

3.507703

Adjusted R-squared-0.604430

S.D. dependent var

3.707069

26

S.E. of regression

4.695600

Durbin-Watson stat

2.146657

Sum squared resid

330.7299

Equation: CEG_THAI = C(6) + C(1)*CEG_THAI(-1) + C(2)*OGDPG_THAI
Observations: 18
R-squared

0.847820

Mean dependent var

4.622024

Adjusted R-squared

0.827529

S.D. dependent var

4.562344

S.E. of regression

1.894723

Sum squared resid

53.84965

Durbin-Watson stat1.173911c. Investment growth equation
Estimation Method: Two-Stage Least Squares
Included observations: 18
Total system (balanced) observations 72
Coefficient

Std. Error

t-Statistic

Prob.

C(3)

-1.867181

8.790209-0.212416

0.8324C(1)0.138560

0.408225

0.339420

0.7354C(2)-0.082525

0.503042-0.164052

0.8702C(4)-7.199823

5.830350-1.234887

0.2213C(5)0.070865

6.146088

0.011530

0.9908C(6)-0.320883

5.756789-0.055740

0.9557

Determinant residual covariance

18020.15

Equation: FDIG_INA = C(3) + C(1)*IRATE_INA + C(2)*OGDPG_INA
Observations: 18
R-squared-0.059537

Mean dependent var-0.008512

Adjusted R-squared-0.200808

S.D. dependent var

2.449041

S.E. of regression

2.683694

Sum squared resid

108.0332

Durbin-Watson stat

2.567984

Equation: FDIG_MALAY = C(4) + C(1)*IRATE_MALAY + C(2)*OGDPG_MALAY
Observations: 18
R-squared

0.003674

Mean dependent var-7.033585

Adjusted R-squared-0.129170

S.D. dependent var

30.59956

27

S.E. of regression

32.51583

Durbin-Watson stat1.045501

Sum squared resid

15859.19

Equation: FDIG_PHILP = C(5) + C(1)*IRATE_PHILP + C(2)*OGDPG_PHILP

Observations: 18

R-squared-0.137349

Mean dependent var

1.055374

Adjusted R-squared-0.288996

S.D. dependent var

3.508930

S.E. of regression

3.983827

Sum squared resid

238.0632

Durbin-Watson stat1.993670

Equation: FDIG_THAI = C(6) + C(1)*IRATE_THAI + C(2)*OGDPG_THAI
Observations: 18

R-squared-0.451528

Mean dependent var

0.169909

Adjusted R-squared-0.645065

S.D. dependent var

0.488997

S.E. of regression

0.627188

Sum squared resid

5.900473

Durbin-Watson stat0.960616

28

d. Government Expenditure growth equation
Estimation Method: Two-Stage Least Squares
Included observations: 18
Total system (balanced) observations 72
Coefficient

Std. Error

t-Statistic

Prob.

C(3)

1.778940

1.293928

1.374837

0.1738C(1)-0.267646

0.133567-2.003831

0.0492C(2)0.692052

0.140959

4.909603

0.0000C(4)4.107842

1.451289

2.830479

0.0062C(5)1.536244

1.193846

1.2868020.2027C(6)3.014080

1.326933

2.271464

0.0264

Determinant residual covariance43029.50

Equation: GOVEX_INA = C(3) + C(1)*OGDPG_INA(-1) + C(2)*OGDPG_INA
Observations: 18
R-squared

0.480215

Mean dependent var

3.830123

Adjusted R-squared

0.410910

S.D. dependent var

5.976912

S.E. of regression

4.587411

Sum squared resid

315.6651

Durbin-Watson stat0.773085

Equation: GOVEX_MALAY = C(4) + C(1)*OGDPG_MALAY(-1) + C(2)*OGDPG_MALAY
Observations: 18
R-squared

0.238466

Mean dependent var

6.832606

Adjusted R-squared

0.136928

S.D. dependent var

5.753334

S.E. of regression

5.344942

Sum squared resid

428.5261

Durbin-Watson stat2.470848

Equation: GOVEX_PHILP = C(5) + C(1)*OGDPG_PHILP(-1) + C(2)*OGDPG_PHILP
Observations: 18

29

R-squared

0.287284

Mean dependent var

3.174227

Adjusted R-squared

0.192255

S.D. dependent var

4.440049

S.E. of regression

3.990477

Sum squared resid

238.8586

Durbin-Watson stat

1.297934

Equation: GOVEX_THAI = C(6) + C(1)*OGDPG_THAI(-1) + C(2)*OGDPG_THAI
Observations: 18
R-squared-0.241350

Mean dependent var

5.092879

Adjusted R-squared-0.406863

S.D. dependent var

3.808766

S.E. of regression

4.517626

Sum squared resid306.1341

Durbin-Watson stat2.575608

e. Export growth equation
Estimation Method: Two-Stage Least Squares
Included observations: 18
Total system (balanced) observations 72
Coefficient

Std. Error

t-Statistic

Prob.

C(6)

-8.129106

6.062389-1.340908

0.1848C(1)3.69E-05

0.000554

0.066615

0.9471C(2)-0.530424

0.309451-1.714084

0.0914C(3)0.415795

0.343162

1.211660

0.2302C(4)2.654227

0.771437

3.440626

0.0010C(5)1.173756

0.387208

3.031330

0.0035C(7)-4.764901

4.531193-1.051578

0.2970C(8)-9.535192

4.572898-2.085153

0.041130

C(9)-6.804976

Determinant residual covariance4.534820-1.500605

0.13852217709.

Equation: OXGR_INA = C(6) + C(1)*EXRATE_INA + C(2)*CEG_INA +C(3)*KGDPG_INA + C(4)*JGDPG_INA + C(5)*CGDPG_INA
Observations: 18
R-squared

0.262991

Mean dependent var

7.427942

Adjusted R-squared-0.044097

S.D. dependent var

11.77837

S.E. of regression

12.03526

Sum squared resid

1738.169

Durbin-Watson stat3.050610

Equation: OXGR_MALAY = C(7) + C(1)*EXRATE_MALAY + C(2)*CEG_MALAY + C(3)*KGDPG_MALAY + C(4)*JGDPG_MALAY+ C(5)*CGDPG_MALAY
Observations: 18
R-squared

0.296584

Mean dependent var

10.12617

Adjusted R-squared

0.003494

S.D. dependent var

7.252298

S.E. of regression

7.239618

Sum squared resid

628.9448

Durbin-Watson stat1.430857

Equation: OXGR_PHILP = C(8) + C(1)*EXRATE_PHILP + C(2)*CEG_PHILP + C(3)*KGDPG_PHILP + C(4)*JGDPG_PHILP +C(5)*CGDPG_PHILP
Observations: 18
R-squared

0.586774

Mean dependent var

7.051526

Adjusted R-squared

0.414596

S.D. dependent var

9.486591

S.E. of regression

7.258352

Sum squared resid632.2041

Durbin-Watson stat1.348059

Equation: OXGR_THAI = C(9) + C(1)*EXRATE_THAI + C(2)*CEG_THAI+ C(3)*KGDPG_THAI + C(4)*JGDPG_THAI + C(5)*CGDPG_THAI

Observations: 18

31

R-squared-0.047590

Mean dependent var

9.190491

Adjusted R-squared-0.484086

S.D. dependent var

6.274162

S.E. of regression

7.643377

Sum squared resid

701.0545

Durbin-Watson stat1.815659

f. Import growth equation
Estimation Method: Two-Stage Least Squares
Included observations: 18
Total system (balanced) observations 72
Coefficient

Std. Error

t-Statistic

Prob.

C(6)

-11.91410

7.377299-1.614967

0.1113C(1)0.000261

0.000674

0.386791

0.7002C(2)0.926809

0.376569

2.461192

0.0166C(3)0.393946

0.417592

0.943375

0.3491C(4)3.084096

0.938759

3.285290

0.0017C(5)0.642521

0.471192

1.363607

0.1775C(7)-9.104824

5.513992-1.651222

0.1037C(8)-11.25835

5.564743-2.023158

0.0473C(9)-10.06721

5.518407-1.824296

0.0729Determinant residual covariance6956999.

Equation: OIMG_INA = C(6) + C(1)*EXRATE_INA + C(2)*CEG_INA +C(3)*KGDPG_INA + C(4)*JGDPG_INA + C(5)*CGDPG_INA
Observations: 18
R-squared

0.270711

Mean dependent var

8.773515

Adjusted R-squared-0.033159

S.D. dependent var

15.67493

S.E. of regression

15.93270

Sum squared resid

3046.210

Durbin-Watson stat2.824407

Equation: OIMG_MALAY = C(7) + C(1)*EXRATE_MALAY + C(2)*CEG_MALAY + C(3)*KGDPG_MALAY + C(4)*JGDPG_MALAY+ C(5)*CGDPG_MALAY
Observations: 18

32

R-squared

0.691792

Mean dependent var

10.78023

Adjusted R-squared

0.563372

S.D. dependent var

12.19513

S.E. of regression

8.058278

Sum squared resid779.2302

Durbin-Watson stat1.699899

Equation: OIMG_PHILP = C(8) + C(1)*EXRATE_PHILP + C(2)*CEG_PHILP + C(3)*KGDPG_PHILP + C(4)*JGDPG_PHILP +C(5)*CGDPG_PHILP
Observations: 18
R-squared

0.454302

Mean dependent var

5.675504

Adjusted R-squared

0.226928

S.D. dependent var

8.133494

S.E. of regression

7.151333

Sum squared resid

613.6987

Durbin-Watson stat1.217869

Equation: OIMG_THAI = C(9) + C(1)*EXRATE_THAI + C(2)*CEG_THAI+ C(3)*KGDPG_THAI + C(4)*JGDPG_THAI + C(5)*CGDPG_THAI

Observations: 18
R-squared

0.579300

Mean dependent var

7.898078

Adjusted R-squared

0.404009

S.D. dependent var

12.06181

S.E. of regression

9.311773

Sum squared resid

1040.509

Durbin-Watson stat1.532069

33

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