1

Malaysia Exports to Singapore:

Causal Relationship with GDP and Exchange Rate

By

Siti Zarinah Salmo (G0000000)

Masripan Salleh (G0921339)

Lokman Effendi Ramli (G0927179)

and Dr. Abdullah Al-Hajjaji (G1111111)

Management Center, International Islamic University, Malaysia

Abstract

The main objective of this study is to examine the impact on Malaysia’s Exports

to Singapore as a result of Singapore’s GDP performance and volatility of

Exchange Rate of Ringgit Malaysia (against US Dollars). Concurrently, this

study investigates causal relationship between Malaysia’s Exports (RXSPO) as

dependent variable and Singapore’s GDP (RSPOGDP) and Exchange Rate

(RRMUSD) as independent variables. The data for this study was obtained from

International Financial Statistic (IMF) World Economic Database, Central Bank

of Malaysia and Department of Statistic, Malaysia. Cointegration, Error

Correction and Causal Granger techniques are adopted extensively for this

study. The findings of this study reveal that 97.54% of total variations of

Malaysia’s Real Exports to Singapore can be explained by the proposed

regression model with Singapore’s GDP and Exchange Rate as the independent

variables. In addition, the model is proven statistically significant and

cointegrated in the long run, where an increase of 1% in Singapore’s GDP is

expected to attribute to 0.8353% increase of Malaysia’s Export to Singapore;

and an increase of 1% in the Exchange Rate (appreciation of RM to USD) is

expected to decrease Malaysia’s Exports to Singapore by 0.7341%. Error

Correction Term (ECT) Coefficient of -0.58631 suggests that Malaysia’s export

to Singapore will converge towards its long run equilibrium level in a moderate

speed after any volatility in Singapore’s GDP or Exchange Rate. Both

Singapore’s GDP and Exchange Rate have one direction Causal Granger

relationship to Malaysia’s Exports to Singapore whereas both Singapore’s GDP

and Exchange Rate establish bi-direction of Causal Granger relationship

between them.

2

1.0 INTRODUCTION

Malaysia, as an open economy, has been very much dependent on foreign trade

to achieve its economic development goals. Foreign trade has accounted for a

significant and rising trend of its gross domestic product (GDP) in the last three

decades, indicating that international trade has been playing an important role in

the development of Malaysian economy. The share of merchandise trade in GDP

was 73% in 1970, increased by 172% in 1995, and increased further by 202% in

20001. If we consider the share of the merchandise trade in GDP as an indicator

of trade liberalization, Malaysia certainly has gone through a relatively rapid

process of trade liberalization and globalization. Indeed, the decision made by

Malaysia to implement the export-oriented development strategy beginning in

1980s has been the major vehicle that has transformed Malaysia from the

primarily commodity based economy to an industrial based economy.

According to World Economic Forum (WEF), currently Malaysia was ranked 24th

best trading nation in the world (Enabling Trading Index – ETI), with a trade value

of USD 281.3 billion2. However, Malaysia world trade share was reduced from

1.2% to 1.1% as compared to the previous year due to the global economic

challenges. In order to maintain its trading position and trade volumes, Malaysia

must optimize any available opportunities to export more of its products,

especially to its leading trading partner, Singapore.

As expected, its traditional bilateral trading partners (Singapore, USA, EU27,

Japan and China) are the main contributors (as per the table below)3.

1 Yusoff, Mohammed, Malaysia Bilateral Trade Relations and Economic Growth, International Journal of Business

and Society,

2 The Global Enabling Trade Report 2010, World Economic Forum,

http://www.weforum.org/en/initiatives/gcp/GlobalEnablingTradeReport/index.htm 3 Ibid

3

Since 2008, Singapore has taken over USA to become Malaysia’s largest

bilateral trading partner. Export to Singapore alone represented 14% of total

national exports where 58.3% of exports to Singapore are mainly electrical and

electronic components and refined petroleum products. In-line with global

practice, most transactions use USD as the preferred currency4.

Malaysia-Singapore relationship as a trading partner is very significantly

important due to close and symbiosis relationship since prior independence

where Singapore was part of Malaysia. Both countries have common culture and

interest and equally important, they are sharing a lot of similarities in history,

economy, social and politic.

In terms of bilateral relationship, Singapore relies heavily on Malaysia for the

supply of raw materials & components and finished and unfinished products. As

an established trading hub for the Asean region, Singapore would re-export

4 The Global Enabling Trade Report 2010, World Economic Forum,

http://www.weforum.org/en/initiatives/gcp/GlobalEnablingTradeReport/index.htm

4

Malaysia’s products to other part of the world in particular electronics and

petroleum products; and agriculture based products, such as palm oil, foods,

fruits and vegetables. On the other hand, Malaysia is heavily dependent on

Singapore’s reputation and infrastructure, ranked as the world’s no. 1 in trade

enabling nation. Therefore, it is critical for Malaysia to continue expanding its

global trade via Singapore until such time when Malaysia achieves full

international recognition or could match Singapore’s outstanding performance in

international trade.

Since Malaysia’s Exports to Singapore is critical for attaining continued growth in

international trade, it is therefore a motivation for this study to investigate the

impact on Malaysia’s Exports to Singapore as a result of Singapore’s GDP

performance and the volatility of Exchange Rate.

Problem Statement

In the WEF report on “Global Enabling Trade” published recently (2010),

Malaysia ranked 24th in the world as compared with its trading partner, Singapore

which is ranked 1st in the world5. As such, it is crucial for Malaysia to explore

thoroughly the factors affecting Malaysia’s Exports to Singapore so that trade

volumes (exports) could be sustained or further improved. For this study,

Singapore’s GDP and volatility of Exchange Rate are being investigated for their

causal relationship with Malaysia’s Exports. We hope that the outcome of this

study could lead to better understanding on how bilateral trade (exports) could be

further improved with Singapore.

5 The Global Enabling Trade Report 2010, World Economic Forum,

http://www.weforum.org/en/initiatives/gcp/GlobalEnablingTradeReport/index.htm

5

Objectives of Study

The objectives of the study are:

To examine the effect of Singapore’s GDP and volatility of Exchange Rate

to Malaysia’s Exports to Singapore.

To examine causal Granger relationship among the variables in the linear

model.

To come up with a cointegrated linear model of Malaysia’s Exports where

Singapore’s GDP performance and Exchange Rate as variables.

To advise the Government of Malaysia on how to exploit Singapore’s

global position to increase Malaysia’s Exports to Singapore.

2.0 LITERATURE REVIEW

We found that there are many literatures available on this subject where volatility

of exchange rate and Gross Domestic Product (GDP) has some impact on trade.

The following summarizes some of the literatures we have reviewed:

Yusoff M (2005) conducted a study to determine the effects of exchange rates on

Malaysian trade balance with the USA, Japan and Singapore using cointegration

and error correction techniques. The short run dynamics were analyzed by using

VECM to see the impact of exchange rate on the trade volume. His findings

confirmed that trade balance, real exchange rates and income are cointegrated

and the estimated long run trade balance indicated that exchange rate is

significant and has a positive sign. In addition, the depreciation of RM could

stimulate exports and discourage imports; and therefore could improve trade

balance for Malaysia, as in the case with Singapore.

Sukar, A (1999) noted that the effect of exchange rate volatility was a

controversial issue in international economics and there was no real consensus

on the direction or the size of the exchange rate volatility and trade relationship.

6

He also stated that the exchange rate volatility increased risk and uncertainty and

therefore could hamper the flow of trade and investment. His findings confirmed

that volatility in real exchange rates has an adverse effect on the trade flow in the

long run. The empirical results based on cointegration analysis showed that USA

export volume is cointegrated with foreign income, real exchange rate, and

exchange rate risk. It confirmed that there was indication of significant negative

relationship between US export volume and exchange rate volatility.

Halicioglu F (2007) conducted empirical study on the dynamics of Turkish

bilateral trade between Turkey and her trading partners using a cointegration

approach. He cited that there was relationship between exchange rate and the

trade balance but stressed that empirical results have been rather ambiguous.

He noted a theoretical suggestion that changes in exchange rate would effect on

trade flows, price and volume. His findings stated that the empirical results

suggest non-existence of the J-curve effect at disaggregate and aggregate

levels. The results of some bilateral long-run relationships suggested that

currency depreciation might improve trade balance.

Rey S (2006) investigated the impact of nominal and real effective exchange rate

volatility on exports of six Middle Eastern and North African (MENA) countries to

EU countries for the period 1970 to 2002. The cointegration results indicate a

significant relationship (either negative or positive) between exports and

exchange rate volatility. Further, the cointegration results show that exports are

cointegrated with the relative price (real effective exchange rate), GDP and

exchange rate volatility. Using an error correction model, he stated the short run

dynamics shows that Granger-causality effects of the volatility of exchange of

rate on real exports are significant. An interesting citation, he referenced

McKenzie (1997) that effects of the nominal exchange rate volatility on Australian

exports change according to the direction of trade; positive effects on exports to

USA, Japan, United Kingdom and Singapore.

Sekkat & Masnour (2005) examined the likelihood of asymmetric shocks in

Europe originating from the impact of exchange rate fluctuations on trade. It

7

conceptualized that for such asymmetric shocks to occur, exchange rate

fluctuations must have different impacts across sectors. The asymmetric shock

may occur if the effect of any change in the Euro policy differs across countries.

This literature has no direct relationship to the study that we undertake that

focuses on relationship between exports and GDP and volatility of exchange rate.

Breden, Fountas & Murphy (2003) conducted analysis both the long-run and

short-run relationship between merchandise export volume and its determinants,

foreign income, relative prices and exchange rate volatility using cointegration

and error correction techniques. They found that exchange rate volatility has no

effect on the volume of trade in the short run but a significant positive effect in the

long-run. They could conclude that the decline in intra-EU exchange rate volatility

associated with the single currency would lead to a long-run fall in Irish exports to

the EU. This study restricts itself to examining the effect of exchange rate

variability on exports, although an examination of overall trade would allow better

analysis of the contributing factors that influence the trade.

This paper analyses the long-run and short-run relationship between export

volume and its determinants; namely, relative prices, foreign income and

exchange rate variability, using the techniques of cointegration and error-

correction methods. The results show that exchange rate volatility has no effect

on the volume of trade in the short-run but a significant positive effect in the long

run.

Based on the above literatures, we could conclude that various studies have

been done to study the impact of exchange rate, GDP and other factors on

exports. This experience and knowledge are invaluable and could be utilized for

us to conduct our study to investigate the impact on Malaysia’s Exports to

Singapore in relation to Singapore’s GDP and the volatility of Exchange Rate

(USD-RM).

8

3.0 MODEL AND DATA ANALYSIS

3.1 MODEL SPECIFICATIONS

The study focuses on Malaysia’s Exports to Singapore in relation to volatility of

Exchange Rate (RM-USD) and Singapore’s GDP. The model underlying our

empirical analysis is based on the following simple export equation:

lnYt = α + β1lnX1t + β2lnX2t + εt

Alternatively, we could use

ln(RXSPOt) = + ln(RSPOGDPt) + In(RRMUSDt) + εt (Equation 3.1.1)

where,

RXSPO is the Malaysia’s Exports to Singapore in real price

RSPOGDP is Singapore’s Gross Domestic Product (GDP) in real price

RRMUSD is Exchange Rate (number of US dollars per Ringgit Malaysia).

Hypotheses

Impact of Singapore’s GDP on Malaysia’s exports to Singapore

Ho : Singapore’s GDP not influence Malaysia’s exports to Singapore

H1: Singapore’s GDP influence Malaysia’s exports to Singapore

Impact of exchange rate on Malaysia’s exports to Singapore

Ho : RRMUSD not influence Malaysia export to Singapore

H2: RRMUSD influence Malaysia’s exports to Singapore

9

3.2 DATA ANALYSIS

We will attempt this study in two parts. The first part in this empirical study is to

identify the stochastic properties by running the following tests:

Normality Test: Normality test using Jarque-Bera (JB) statistics. The null

hypothesis; Ho: the residuals are normally distributed. An alternative hypothesis,

H1: residuals are not normally distributed.

Residual Autocorrelation Test: We employ Busch-Godfery LM-test and Durbin-

Watson test, with the null hypothesis; Ho: no autocorrelation in the disturbance

term, and alternative hypothesis, H1: has autocorrelation in the disturbance term.

Heteroskedasticity Test: We use White test with null hypothesis; Ho: the

variance of the disturbance term is homoskedastic, and alternative hypothesis,

H1: the variance of the disturbance term is heteroskedastic.

Parameter Stability Test: We use both CUSUM Test and CUSUM of Square

Test to produce informative plots about the stability of the structure. Null

hypothesis; Ho: the parameter is constant and alternative hypothesis, H1: the

parameter is not constant. Expectation of the CUSUM statistics is zero under null

hypothesis of constant parameters.

The second part of this statistical analysis is to perform relevant tests based on

time series data as follows:

Unit Root Test: We use both Augmented Dickey Fuller (ADF) test and Phillips-

Perron (PP) unit root tests to test for stationary of the variables or for determining

the variables order of null hypothesis; Ho: the variable is non-stationary and

alternative hypothesis, H1: : the variable is stationary. If the variables are

integrated of order one, I(1), then proceed with cointegration tests.

10

Cointegration Test: We use Johansen and Jusellius test to identify whether

variables are cointegrated where, null hypothesis; Ho: the variables are not

cointegrated and alternative hypothesis, H1: the variables are cointegrated. If two

variables are cointegrated of order one, they can be modeled by an ECM to

determine the long-run relationship between two or more variables.

Based on above, the following is the estimated equation:

∑ ∑

∑

∑ (Equation 3.1.2)

Granger Causality Test: We use Block Exogeneity-Wald test to identify whether

variables has Granger causality relationship among them. The hypotheses are as

below:

Granger causality relationship Singapore’s GDP to Malaysia’s Exports to

Singapore:

Ho: Singapore’s GDP has no causal Granger relationship to Malaysia’s export to

Singapore.

H1: Singapore’s GDP has causal Granger relationship to Malaysia’s Exports to

Singapore

Granger causality relationship Exchange Rate to Malaysia’s Exports to

Singapore:

Ho: Exchange Rate has no causal Granger relationship to Malaysia’s Exports to

Singapore.

H2: Exchange Rate has causal Granger relationship to Malaysia’s Exports to

Singapore.

11

Granger causality relationship Malaysia’s Exports to Singapore to

Singapore’s GDP:

Ho: Malaysia’s exports to Singapore has no causal Granger relationship to

Singapore’s GDP

H3: Malaysia’s exports to Singapore has causal Granger relationship to

Singapore’s GDP

Granger causality relationship Malaysia’s Exports to Exchange Rate:

Ho: Malaysia’s Exports to Singapore has no causal Granger relationship

Exchange Rate

H4: Malaysia’s Exports to Singapore has causal Granger relationship to

Exchange Rate

3.3 EXPECTED RESULTS

The Impact of Singapore’s GDP on Malaysia Exports to Singapore

Singapore’s GDP is expected to have a significant positive relationship with

Malaysia’s Exports to Singapore, where any increase in the GDP is expected to

increase in the demand for Malaysian products.

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Billi

on

Malaysia's Exports to Singapore Vs Singapore GDP

Malaysia's export to Singapore (RM) Singapore GDP (USD)

12

The Impact of Exchange Rate (USD/RM) on Malaysia Exports to Singapore

The volatility of Exchange Rate is expected to have a significant negative

relationship with Malaysia’s Exports to Singapore, where any decrease in the

value of Ringgit Malaysia is expected to increase in the demand for Malaysian

products.

3.3 DATA AND SOURCES

This study uses annual data for the period of 1980-2009. Data on Malaysia’s

Exports to Singapore was obtained from Department of Statistic, Malaysia. Data

on Singapore’s GDP and CPI are obtained from International Financial Statistics

(IFS) World Economic Database (WEO, April 2010). Data for the Exchange Rate

of USD-RM is gathered from the Central Bank of Malaysia, where:

i) Malaysia’s Export to Singapore (RXSPO) = Malaysia’s nominal

exports to Singapore / Consumer Price Index of Malaysia *100;

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

0.0

20.0

40.0

60.0

80.0

100.0

120.0

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Billion

Malaysia's Exports to Singapore Vs Exchange Rate (RMUSD)

Malaysia's export to Singapore (RM) Exchange Rate (RM per unit USD)

RMUSD

13

ii) GDP of Singapore (RSPOGDP) = Current GDP of Singapore in USD

/ Consumer Price Index of Singapore *100;

iii) RRMUSD=Real exchange rate between USD and RM (defined as

(Pm.NEXt)/Pt, where Pm is Malaysia’s CPI, Pt is the US CPI, NEXt is

the nominal exchange rate (the number of RM per USD)

4.0 RESULTS AND DISCUSSION

4.1 DESCRIPTIVE ANALYSIS

Jarque-Bera Test

Jarque-Bera Test is goodness-of fit measure of departure from normality based

on the sample kurtosis and skewness. The results show that we fail to reject null

hypothesis Ho. This means the residuals are normally distributed at 99%

confidence level.

LRXSPO LRSPOGDP LRRMUSD

Mean 3.464141 4.062485 -1.085895

Median 3.714946 4.372534 -1.052091

Maximum 4.380008 5.111981 -0.707088

Minimum 2.220952 2.853741 -1.370822

Std. Dev. 0.751970 0.719900 0.221257

Skewness -0.388859 -0.307918 0.050867

Kurtosis 1.598058 1.721944 1.606147

Jarque-Bera 3.212858 2.515849 2.441470

Probability 0.200603* 0.284243* 0.295013*

Sum 103.9242 121.8745 -32.57684

Sum Sq. Dev. 16.39831 15.02943 1.419681

Observations 30 30 30

*Represents statistically not significance at the 1% level based on adjusted data due to economic crisis during 1997-2003.

Note: The critical values for the tests are based Monte Carlo simulation by using Matlab software, n=30

14

In order to get a normally distributed residual, we have to identify the outliers and

create dummy variables with strong economic reasons (i.e. economic crisis

during 1997-1998). It does neither make sense nor practical to eliminate every

disturbing observation. Alternatively, we could increase the number of

observations from the present 30 observations.

Residual Autocorrelation Test

Based on Busch-Godfery LM-test, the BGLM has p-value of 0.0106, which

means that we fail to reject the null hypothesis at 1% significant level. We

conclude there is no autocorrelation in the disturbance term.

Test for Heteroskedasticity

Based on the White test where the p-value is 0.0758, we fail to reject the null

hypothesis of homoskedastic disturbance at 1% significant level. We conclude

that the variance of the disturbance term is homoskedastic.

Test for Stability

The CUSUM statistics are zero under the null hypothesis of constant parameter.

The CUSUM statistics plotted in with 5% confidence bounds (as below). The

Graph shows CUSUM statistics revolve around zero within its confidence bounds

the null hypothesis of parameters constancy are not rejected. The results show

that the parameters are constant.

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 4.778646 Probability 0.009477 Obs*R-squared 11.21867 Probability 0.010600

White Heteroskedasticity Test:

F-statistic 2.458109 Probability 0.071681

Obs*R-squared 8.468346 Probability 0.075853

15

The CUSUM of Square statistics are zero under the null hypothesis of constant

parameter. The CUSUM of Square statistics plotted in with 5% confidence

bounds (as below). The Graph showed CUSUM of Square statistics revolves

around zero within its confidence bounds the null hypothesis of parameter

constancy is not rejected. Based on the results, the model,

ln(RXSPOt) = + ln(RSPOGDPt) + In(RRMUSDt) + εt

is stable at 5% significance bound.

-16

-12

-8

-4

0

4

8

12

16

84 86 88 90 92 94 96 98 00 02 04 06 08

CUSUM 5% Significance

-0.4

0.0

0.4

0.8

1.2

1.6

84 86 88 90 92 94 96 98 00 02 04 06 08

CUSUM of Squares 5% Significance

16

Relationship between dependent variable (RXSPO) and independent variables

(RSPOGDP and RRMUSD) is investigated using classical multi-regression

model. It is carried out as a comparison to the results which are derived from

other approaches.

Results of the estimate regression show that the variables are significant in

the long run (based on equation 3.1.1)

Summary of result

Coefficient Standard. Error Prob.

Constant -0.7265 0.1302 0.0000*

Log real SPOGDP (LRSPOGDP)

0.8353 0.0576 0.0000*

Log real exchange Rate (RRMUSD)

-0.7341 0.1876 0.0006*

F-Statistics 535.4767 0.0000*

R-squared 0.9754

Durbin-Watson 0.6763

Notes; *(**)(***) indicate statistical significance at the 1%, (5%),(10%) levels respectively Dependent Variable = Real Malaysia’s Exports to Singapore (LRXSPO)

R-square (R2) of 0.975409 showing that 97.5% of total variation of Malaysia’s

Exports to Singapore (LRXSPO) can be explained by the linear model with real

Singapore’s GDP (LRSPOGDP) and Exchange Rate (LRRMUSD) as

independent variables. Overall model is significant at 1% significant level. All

independent variables are also significant at 1% significant level. Durbin-Watson

(DW) of 0.67632 shows that there is positive autocorrelation in the model.

From the model, LRSPOGDP has positive impact on Malaysia’s Exports to

Singapore. An increase of 1 % of Singapore’s GDP is expected to increase

0.8353 % of Malaysia’s Exports to Singapore (ceteris paribus). Exchange rate

has negative impact on Malaysia’s Exports to Singapore. An increase of 1% in

exchange rate (RM-USD) is expected to reduce 0.7341% of Malaysia’s Exports

to Singapore (ceteris paribus). In other word, when RM appreciates against USD

17

there is expected reduction in Malaysia Exports to Singapore (since Malaysian

goods become more expensive).

The regression equation can be written as follows:

LRXSPO = α + 0.8353*LRSPOGDP - 0.7341*LRRMUSD

Relationship between dependent variable (RXSPO) and independent variables

(RSPOGDP and RRMUSD) is also investigated by using cointegration, error

correction model and Granger causality effect.

Unit Root Test

Augmented Dickey-Fuller (ADF) and Phillip-Perron (PP) Unit Root Tests

(based on equation 3.1.2)

Variables ADF PP

Level First Difference

Second Difference

Level First difference

Second difference

LRXSPO -0.6579 -3.8683** -0.8841 -3.8683**

LRSPOGDP -1.8977 -2.8878 -5.5857* -1.4989 -2.8965 -6.1304*

ΔLRSPOGDP -2.8704 -5.6432* -2.8804 -6.1761*

LRRMUSD -2.4003 -4.2709* -1.7069 -4.1872*

Note: The critical values for the tests are based MacKinnon (1996).

*represent statistical significance at the 1% level.

** represent statistical significance at the 5% level.

Based on the results of ADF and PP tests, we fail to reject the null hypothesis

(the variable is non-stationary). Since LRSPOGDP was found stationary at

second difference I(2), we use the DLRSPOGDP. At 1% significance level, the

first-differences of these variables are found to be stationary. As such, we reject

the null hypothesis of non-stationary. This means we accept the alternative

hypothesis where the variables are integrated of order one I(1). Then we proceed

to perform cointegration test.

18

Cointegration Test

Johansen-Juselius Cointegration Tests Result (based on equation 3.1.2)

Hypothesiz

e No. of

CE(s)

Trace

Statistics

Max-Eigen

Statistics(lamda)

Critical Values (5%)

Trace Max-

Eigen

None* 71.85999 36.74577 29.7972 21.1316

At Most* 35.11422 33.55567 15.4947 14.2646

At Most 1.5585 1.5585 3.8415 3.8415

* Rejection of the hypothesis at the 0.05 level.

CE : Cointegration equation

Note: We set lag order to five, which is sufficient to whiten the noise process. The

critical values are taken from Mackinnon-Haug-Michelis (1999)

The results show that null hypothesis of no cointegration is rejected at the 5%

level by both trace and the Max-Eigen value versions of Johansen approach. The

test indicates that these variables are cointegrated. As such, the long run

equilibrium relationship between Malaysia’s Exports to Singapore with real GDP

and Exchange Rate exists.

Johansen procedure is used to obtain the long run coefficient of the model.

Based on the results (table below), however, both LRSPOGDP and LRRMUSD

are not significant at 5% significant level.

19

Normalized Cointegrating Evigen vector (based on equation 3.1.2)

One

cointegrating

Equation

Log

Likelihood

143.3037

LRXSPO LRSPOGDP LRRMUSD

1.0000 -1.752546 -1.731456

(0.0000) (0.20722) (0.776846)

Short Run Analysis : An Error Correction Model The results showed that the error term ECT in the short–run is statistically

significant at 10% with a negative sign, confirming that a long-run equilibrium

relationship exists between the variables. The motive of this analysis is to

discover whether the short-run dynamics are influenced by the estimated long-

run equilibrium conditions, which is cointegrating vectors.

Short run Vector Error Correction Model (VECM) (based on equation 3.1.2)

Variable Coefficient Standard Error

t-ratio Probability

ECT(-1) -0.586310 0.3125674 -1.942455 0.0644***

ΔLRXSPO(-3) 0.509998 0.261864 1.947564 0.0704***

ΔLRSPOGDP(-3) -0.975592 0.444821 -2.193225 0.0445**

ΔLRRMUSD(-3) 1.465882 0.515253 2.844978 0.0123**

Intercept 0.069814 0.065372 1.067952 0.3024

R2 0.736552

DW 2.07976

F-statistics 4.193725 0.006425*

Notes; (*)(**)(***) indicate statistical significance at the 1%, (5%),(10%) levels respectively Based on the table, the VECM model is :

ΔLRXSPO = α + 0.50998*ΔLRXSPO(-3) -0.975592ΔLRSPOGDP(-3)

+1.465882*ΔLRRMUSD(-3) - 0.58631*ECT(-1)

20

R-square (R2) of 0.7366 showing that 73.66% of total variation of ΔMalaysia’s

Exports to Singapore (ΔLRXSPO) can be explained by the linear model with real

ΔSingapore’s GDP (ΔLRSPOGDP) and ΔExchange Rate (ΔLRRMUSD) as

independent variables. Overall model is significant at 1% significant level. All

independent variables are also significant at 1% significant level. Durbin-Watson

(DW) of 2.07976 shows that there is no autocorrelation in the model.

From the model, ΔLRSPOGDP has negative impact on ΔMalaysia’s Exports to

Singapore. An increase of 1 % of ΔSingapore’s GDP is expected to decrease

0.9756 % of ΔMalaysia’s Exports to Singapore (ceteris paribus). ΔExchange rate

has positive impact on ΔMalaysia’s Exports to Singapore. An increase of 1% in

ΔExchange Rate (RM-USD) is expected to increase 1.4659% of ΔMalaysia’s

Exports to Singapore (ceteris paribus).

A crucial parameter in the estimation of the short-run dynamic model is the

coefficient of the error-correction term (ECT), which measures the speed of

adjustment of Malaysia’s export to Singapore to its equilibrium level. The results

show that the parameter of the ECT in the model is statistically significant and

correctly signed. This confirms that the Malaysia’s Exports to Singapore has an

automatic adjustment mechanism. Error Correction Term (ECT) coefficient of -

0.58631 suggests that Malaysia’s Exports to Singapore will converge towards its

long run equilibrium level in a moderate speed due to any volatility in Singapore’s

GDP or Exchange Rate.

Granger Causality Test Pairwise Granger Causality Test

Independents LRXSPO LRSPOGDP LRRMUSD

Dependents Chi-Sq (Prob) Chi-Sq (Prob) Chi-Sq (Prob)

LRXSPO 5.4762(0.0647) 6.7623(0.034)

LRSPOGDP 1.2181(0.5439) 5.6627(0.059)

LRRMUSD 2.8769(0.2372) 4.6177(0.0994)

21

Ho: Singapore’s GDP has no causal Granger relationship to Malaysia’s Exports to

Singapore.

Reject null hypothesis at 10% significant level (0.0647<0.1). So, Singapore’s

GDP has causal Granger relationship to Malaysia’s Exports to Singapore (ceteris

paribus) at 10% Significant level (p<0.1).

Ho: Exchange Rate has no causal Granger relationship to Malaysia’s Exports to

Singapore.

Reject null hypothesis at 10% significant level (0.034<0.1). So, Exchange Rate

has Causal Granger relationship to Malaysia’s Exports to Singapore (ceteris

paribus) at 10% Significant level (p<0.1).

Ho: Malaysia’s Exports to Singapore has no causal Granger relationship to

Singapore’s GDP.

Since we fail to reject null hypothesis at 10% significant level (0.5439>0.1),

Malaysia’s Exports to Singapore has no causal Granger relationship to

Singapore’s GDP (ceteris paribus) at 10% significant level.

Ho: Exchange Rate has no causal Granger relationship to Singapore’s GDP.

Since we reject the null hypothesis at 10% significant level (0.059<0.1) Exchange

Rate has causal Granger relationship to Singapore’s GDP (ceteris paribus) at

10% significant level (p<0.1).

Ho: Malaysia’s Exports to Singapore has no causal Granger relationship to

Exchange rate.

Since we fail to reject null hypothesis at 10% significant level (0.2372<0.1),

Malaysia’s Exports to Singapore has causal Granger relationship to Exchange

Rate (ceteris paribus) at 10% significant level.

Ho: Singapore’s GDP has no causal Granger relationship to Exchange Rate.

22

Since we reject null hypothesis at 10% significant level (0.0994<0.1), Singapore’s

GDP has causal Granger relationship to Exchange Rate (ceteris paribus) at 10%

significant level (p<0.1).

Based on causal Granger analysis, Singapore’s GDP and Exchange Rate have

one direction Causal Granger relationship to Malaysia’s Exports to Singapore.

However, both Singapore’s GDP and Exchange Rate have bi-directional Causal

Granger relationship between them.

5.0 CONCLUSION AND RECOMMENDATIONS

This paper utilizes an empirical analysis to examine the effect of Singapore’s

GDP and the volatility of Exchange Rate (RM-USD) to Malaysia’s Exports to

Singapore, utilizing 30 observation samples from 1980 to 2009. The first step of

data analysis involves identifying the stochastic properties of the data by running

Normality test, Autocorrelation test, Heteroskedastic test and Parameter test.

Malaysia's Exports to Singapore

(RLXSPO)

Singapore's GDP

(RSPOGDP)

Exchange Rate

(RRMUSD)

23

Then, we investigated the relationship between dependent variable and

independent variables through Unit Root test, Cointegration test, Error Correction

model and Causal Granger test.

We found that the stochastic properties of the data are excellent where the

residuals are normally distributed; no autocorrelation, homoskedastic and the

parameters are stable. The results show that the variables are stationary and

cointegrated. The results indicate that the long run equilibrium relationship

between Malaysia’s Exports to Singapore with Singapore’s GDP and Exchange

Rate exist. The results also show the error term ECT in the short–run is

statistically significant at 10% with a negative sign, and again, confirming that a

long-run equilibrium relationship exists between the variables.

The analyses show that the parameter of the ECT in the model is statistically

significant and correctly signed. This confirms that the Malaysia’s Exports to

Singapore has an automatic adjustment mechanism. Error Correction Term (ECT)

Coefficient of -0.58631 suggests that Malaysia’s Exports to Singapore will

converge towards its long run equilibrium level in a moderate speed in-line with

any volatility in Singapore’s GDP or Exchange Rate.

Results of the estimate regression show that the variables are significant in

the long run (based on equation 3.1.1)

The model derived from regression equation is as follows:

LRXSPO = α + 0.8353*LRSPOGDP - 0.7341*LRRMUSD + εt , R2 = 0.975

From this model, 97.5% of total variation of Malaysia’s Exports to Singapore

(LRXSPO) can be explained by the linear model with real Singapore’s GDP

(LRSPOGDP) and Exchange Rate (LRRMUSD) as independent variables.

24

From the model, LRSPOGDP has positive impact on Malaysia’s Exports to

Singapore. An increase of 1 % of Singapore’s GDP is expected to increase

0.8353 % of Malaysia’s Exports to Singapore (ceteris paribus). Exchange Rate

has negative impact on Malaysia’s Exports to Singapore. An increase of 1% in

the Exchange Rate (RM-USD) is expected to reduce 0.7341% of Malaysia’s

Exports to Singapore (ceteris paribus). In other words, when RM appreciates

against USD there is expected reduction in Malaysia Exports to Singapore (since

Malaysian goods become more expensive).

Based on VECM, the cointegrated model derived from that approach is as follows:

ΔLRXSPO = α + 0.50998*ΔLRXSPO(-3) -0.975592ΔLRSPOGDP(-3)

+1.465882*ΔLRRMUSD(-3) - 0.58631*ECT(-1)

The overall model is significant at 99% confidence level.

Based on causal Granger analysis, Singapore’s GDP and Exchange Rate have

one direction Causal Granger relationship to Malaysia Exports to Singapore.

However, both Singapore’s GDP and Exchange Rate have bi-directional causal

Granger relationship between them.

Areas for Improvement

Treatment of Data

Since Singapore’s GDP was effected by the Asian Financial crisis (1997-1998)

and the Global Economic crisis in 2008 (sub-prime), the crises could have cause

significant impact to the VECM model. Therefore, if these crises were represented

accordingly in the model (in the forms of proxies or dummy variables), it could

cause an improvement in the VECM model.

Similarly, the impact of Exchange Rate regime could influence the VECM model

as a result of daily changes and fluctuations in the Exchange Rate; and

25

misalignment of Exchange Rate (due to government policies & strategies to

influence free trade market) and the fixed rate (currency pegging) such as,

intervention by Government of Malaysia to peg 1USD = RM3.80 during financial

crisis). Further, other external factors such as Iraq wars, SARS, Tsunami, Euro

meltdown, etc could have cause some impact to the model. We believe the model

could be improved further if these elements are addressed accordingly and the

data being handled appropriately.

6.0 REFERENCES

Bredin, D., Fountas, S., and Murphy, E., 2003, An Emphirical Analysis of Short-run and Long-run Irish Export Function: Does Exchange Rate Volatility Matter?, International Review of Applied Economics, Vol. 17, N0. 2. Halicioglu, F., 2007, The J-Curve Dynamics of Turkish Bilateral Trade: A Cointegration Approach, Journal of Economic Studies, Vol.34 No. 2, 2007, pp.103-119. Osman, H., Hafiez, A.A., and Ramayah, T., 2009, Exports to Arab-Speaking Countries: Determinants of The Performance of Malaysia Companies, International Journal of Management Vol.26 No. 3. Rey, S., 2006, Effective Exchange Rate Volatility on Exports to EU, Journal of Economic Development, Vol. 31, No. 2 Sukar, A.H., 1999. US Exports and Time-Varying Volatility of Real Exchange Rate, Global Finance Journal, 12 (2001), North-Holland. Pp.109-119. The Global Enabling Trade Report 2010, World Economic Forum, http://www.weforum.org/en/initiatives/gcp/GlobalEnablingTradeReport/index.htm

Yusoff, M., (2005), Malaysia Bilateral Trade Relations and Economic Growth, International Journal of Business and Society,

Yusuf, M. (2009), Bilateral Trade Balance, Exchange Rate and Income: Evidance From Malaysia, Global Economy Journal, Vol.9, No. 4, IIUM, Berkerley Press.

26

The Central Bank of Malaysia, www.bnm.gov.my Department of Statistic, Malaysia. www.statistics.gov.my International Financial Statistic, World Economic Database, www.imf.org Prof. Mansor, 2010., Class Notes for DBA,IIUM, Gombak, Selangor.