Abstract

Each of the Gravity Model of trade variables has played an important and

significant role to determine the economic growth in every country and

international economic arena. Therefore, this term paper is based on the

econometrics’ theory to make analysis of multiple regression models on the

relationship between Gravity Model of trade variables and volume of trade in

between Malaysia and Singapore by using the annual data from year 1979 to

2008. Also, we have used the volume of trade as the explained variable to

measure the economic growth, since majority of the countries in nowadays are

using the Gravity Model to estimate their countries’ economic growth. By using

the econometrics’ estimation methods, we can conclude that trade value, GDP of

Malaysia, GDP of Singapore, and population between two countries played the

significant role and also have the positive effect on the volume of trade. Port

distance between Malaysia and Singapore also has played an important role in

measuring volume of trade, but it has a negative effect on value of trade.

1.0 Statement of the Problem

The model that we have choose to use is Trade = Malaysia GDP +

Singapore GDP + Population + Distance. In this model, there are several factors

that can affect the trade of a country. Those factors are Malaysia GDP, Singapore

GDP, population, and distance.

Even that the total amount of distance are consider to be the main factors

in affect the trade, however the other factors also can affect the value of trade.

First of all, the consumption has played a significant role in the economic growth

because the Keynesian theory of consumption showed that the consumption can

promote the production of a country. Besides that, Adam Smith also shows that

the investment can motivate of the economic growth when in the early period of

classical economics. This resulted that the investment also should be considered

into the model. Keynesian multiplier theory also said that the government’s

spending can increase the national income, so this variable also should be taken

into the factors in this model. Therefore Malaysia GDP, Singapore GDP,

population, and distance should be added into this model.

Here we need to find out how those variables can affect the trade in a

country. With having this research, we also can find out that which variables will

having the most effect in the trade or else all of the variables have the same

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effect on the trade. When we find out which variables will have more effect on

the trade, government can improve in the variable that have more effect on

trade. Trade is important because it can show out the economic growth of a

country. With using the trade, we also can compare each country’s economic

growth. Besides that, we also can compare a country’s economic growth by

compared its each year GDP.

All of the economists should be interest in the results. This is because it

can help they study of a country when they are doing their researches. Besides

that, all of our group members would interest in the results too, because we can

do this as a practical before we do other else researches that maybe will include

more variables and also more complicated.

2.0 The Review of Literature

There are many studies that explore the gravity model of bilateral relation

between developing countries such as Celine Carrere (2004), Min Zhou (2010),

James E. Anderson (2010), Nuno Carlos Leiton (2010), Joakim Westerlund and

Wilhelmsson Fredrik (2006), Carlos Carrillo and Carmen A Li (2002), Hubert P.

Janicki, Warin Thierry, and Phanindra V. Wunnava (2005),

Suleyman Tulug Ok (2010), Kim-Lan Siah (2009), Giuseppina MariaChiara

Talamonand and many other authors.

Celine Carrere (2004), using the gravity model for trade agreements and

regional ex-post. The introduction of the correct number of dummy variables that

allows to identify of Vinerian the incidence and impact of commercial fraud, while

calculating the budget rules observed by the characteristics of pairs of trading

partner countries. In previous estimates, the results indicate that regional

agreements have resulted in a significant increase in trade between members

and are often sacrificed in the world.1

Besides that, Min Zhou (2010) have extended the principle of homophily,

the similarity breeds connection is found to have a lot of social networks to learn

about global trade. This research also identified geographic and cultural

homophily increase in global trade to shows that countries are more profitable

they are geographically provide and cultural partners with in global trade.

1 Celine Carrere. (2006). Revisiting the effect of regional trade agreements on trade flows with proper specification of the gravity model, European Economic Review, 50, pp 223-247.

2

Analysis of other data for bilateral trade in the sector level is to produce an

explanation for the observed intensification of geo-cultural homophily.

Development of cross-sector trade differences shift the composition of global

trade as a whole and make it more susceptible to the influence of geo-cultural.

Thus, the two taken

together for global trade becomes more geo-culturally embedded.2

James E. Anderson (2010) has explained the gravity that gravity has long

been one of the most successful empirical models in economics. In addition, the

basic theory of gravity in the recent practice has resulted in estimated to be

richer and more accurate. The interpretation of the spatial relationships is also

reflected by gravity. Author also explains about recent developments are

reviewed here and recommendations are made to promising research in the

future.3

For Nuno Carlos Leiton (2010), the review of factors that determine

bilateral trade between the U.S. and NAFTA, EU, and ASEAN countries in the

period 1995-2008. Findings indicate that current U.S. trade by the Linder

hypothesis, while the bilateral trade relations in connection with the theorem of

Heckscher-Ohlin-Samuelson. Results showed that geographic distance is

negative and significant, that increased trade if transport costs fall. The authors

have introduced the dimension of the economy, productivity, and foreign

investors. These results also confirm the hypothesis that foreign direct

investment positively correlated in the trade. 4

This paper examines the effects of zero gravity model of trade in the

estimation using the two data, simulated and real. The author also shows that

the usual methods of estimating the log-linear may lead to the inference of fraud

when some observations are zero. As an alternative approach, the author

suggests using a fixed effects Poisson estimator. This approach can eliminates

the problem of zero, as open trade and perform well in small samples. 5

2 Min Zhou. (2011). Intensification of geo-cultural homophily in global trade: Evidence from the gravity model, Social Science Research, 40, pp 193-209.3 James E.Anderson. (2010). The Gravity Model, NBER Working Paper Series.4 Nuno Carlos Leitao. (2010). The Gravity Model and United States trade, Europe Journal of Economic, Finance and Administrative Sciences, 21, pp 93-100.5 Joakim Westerlund and Fredick Wilhelmsson. (2006). Estimating the gravity model without gravity using panel data, pp 1-14.

3

Carlos Carrillo and Carmen A Li (2002) have applies the gravity model to

check the effects of the Andean Community and Mercosur in both trades is two

intra-regional and intra-industry in the period 1980-1997. After accounting the

effects of size and distance, preferential trade agreements of the Andean

Community has a significant influence on the two different products and as a

reference for certain capital-intensive goods. Meanwhile, the

Mercosur preferential trade agreements will only have a positive effect on capital

intensive sub-reference product categories.6

Then, Hubert P. Janicki, Warin Thierry, and Phanindra V. Wunnava (2005)

are review the empirical evaluation of the theory of endogenous currency area is

optimal. Gravity model used to rate the effectiveness of empirically the

convergence criteria by reviewing the specific benefits that guides the location of

multinational investment in the European Union. A fixed effects model based on

panel data of foreign direct investment (FDI) flows in the 15 European Union

shows that horizontal investment encourages the diffusion of production

processes across national borders. In particular, the Maastricht criteria for

convergence of interest rates show the government’s fiscal policy and debt

played an important role in attracting multinational investment.7

Since the pioneering work of Tinbergen (1962) and Poyhonen (1963),

gravity models have become standard tools for studying bilateral trade. The

author also suggests some continuation of the standard gravity model. This

equation was modified and tested using panel data from 140 observations over

the period 2000-2008. This result in a specification which you can be seen (i)

income that is more flexible response, (ii) competitive effects of a general and

special part, and (iii) an alternative and consistent size of remoteness. These

connections are found to be significant factors in explaining intra-EU. 8

Kim-Lan Siah (2009) debate about the economic integration of ASEAN and

its ability to promote intra-ASEAN, namely Indonesia, Malaysia, Philippines,

Singapore and Thailand. To achieve this, the gravity model has been modified to

estimate the autoregressive distributed lag (ARDL) framework, or approach to

6 Carlos Carrillo and Carmen A Li. (2002). Trade blocks and the gravity models evidence from Latin American countries, pp 1-30.7 Hubert P. Janicki, Thierry Warin and Phanindra. (2005). Endogenous OCA theory: using the gravity model to test mundeil’s intuition, Center For European Studies, 125, pp 1-15.8 Suleyman Tulug Ok. (2010). What determines intra-EU trade? The gravity model revisited, International Research Journal of Finance and Economic, 39, pp 245-250.

4

testing the limits for each of the five ASEAN countries. Empirical results showed

that the effects of the economic size of bilateral trade flows in both trade-ASEAN

trade blocks is increased, depend on the particular country. However, the ASEAN

countries may have no overall benefit from the establishment of AFTA as a

deflection of trade that may occur in regional market. 9

The author has estimated the factors that affect foreign direct investment

flows using gravity equations and the importance of controlling both the

traditional gravity variables (size, level of development, distance, common

language) and other institutional variables such as shareholder protection (La

Porta et al., 1998 and Pagano and Volpin, 2004) and openness to FDI (Shatz,

2000). The purpose of this study was to identify factors that determine the

outcome of multinational companies to establish new foreign affiliates abroad. 10

Will Martin and Pham Cong (2007) has systematically explains that allows

you to see the zero trade flows are very common in international trade. It aims to

determine the best approach to estimate the zero-gravity model of trade flows

and heteroskedasticity problems highlighted in the paper the influence of Silva

and Tenreyro. Based on Monte Carlo simulations with the data constructed using

the Tobit-type, we find that the Eaton-Tamura (E-T) Tobit estimator generally has

the smallest bias, although not necessarily better than the truncated OLS

regression. ET estimator with a strong emphasis on ensuring that the correct

error is determined and the lowest bias will be investigated. The Heckman

Maximum Likelihood estimators appear to perform better if properly identify the

existing restrictions. 11

The authors have examines how much the gravity model that explains the

various cross-border flows that can lead knowledge spillovers. It turns out that

the model works well for trade and telephone traffic, but less satisfactory for the

flow of mergers and acquisitions. 12

Chan-Hyun Sohn (2005) has applied the gravity model to explain bilateral

trade flows of South Korea. A trade structure and trade network in the Asia-

9 Kim-Lan Siah. (2009). AFTA and the intra-trade patterns among ASEAN-5 economies: trade-enchancing or trade-inhibiting?, International Research Journal of Finance and Economic, 1, 1, pp 117-126.

10 Giuseppina Maria Chiara Talama, Institution FDI and the gravity model, pp 1-41.11 Will Martin and Cong Pham. (2007). Estimating the gravity model when zero trade flows are important, pp 1-1912 Wei-Kang Wong. (2007). Comparing the fit of the gravity model for different cross-border flows, pp 1-11.

5

Pacific region, including the gravity equation to describe the peculiarities of trade

patterns in South Korea. Korea has significant commercial potential that have

not been realized with Japan and China, indicating that they are required

partners for FTAs. These authors also have applies the extract practical trade

policy applications. Empirical results showed that South Korea’s trade follow the

Heckscher-Ohlin model is more of a decision to increased or model of product

differentiation. North-South Korean trade will expand significantly if the normal

bilateral relations and North Korea will participate in APEC. 13

Jacques Melitz (2006) generally assumed that distance in the gravity

model strictly reflects frictions that hinder bilateral trade. However, distance

North-South could also reflect differences in factor endowment, which provides

opportunities for a profitable trading. In addition, significance of North-South

differences that survive the stress test, the period, the difference of latitude

North-North, North-South and South-South, and the other control s the size

difference in the timeless factors, such as differences in output per capita and

the average temperature difference average, rainfall, and seasonal temperature

range. Finally, the study of the impact of internal distance and remoteness made

the trade because the two variables specific to a particular country. This is done

by studying its effect on the country fixed effect themselves have previously

estimated. Internal distance it possessed a much greater effect than isolation.14

The authors have implemented the gravity model on panel consisting of

India with annual bilateral trade with all trading partner in the second half of the

twentieth century. The main conclusion that emerges from this analysis are: (1)

the core gravity model can explain about 43 percent of volatile trading in India

for centuries in the second half of the twentieth century, (2) trade between India

and less than proportionate response to the size and more than proportionately

the distance, (3) colonial herigate is still an important factor in determining the

direction of trade between India and at least in the second half of the twentieth

century, (4) India trade and more advanced than backward countries, but (5)

13 Chan-Hyun Sohn. (2005). Does the gravity model explain South Korea’s trade flows?, The Japanese Economic Review, 56, 4, pp 417-430.14 Jacques Melitz. (2007). North, South and distance in the gravity model, European Economic Review, 51, pp 971-991.

6

determine the size of a influence on the development of trade between India and

trade partners. 15

I-Hui Cheng and Howard J. Wall (2005) was compare the various

specifications of the gravity model of trade as nested versions of a general

specification of bilateral country-pairs with fixed effects to control heterogeneity.

For each specification, the authors also show that the theoretical restrictions

used to obtain them from the general model is not supported by the statistics

because of the gravity model has become the “workhorse” baseline model to

estimate the effects of international integration. It is important for the empirical

implications. In particular, the author shows that, unless heterogeneity is

properly recorded the gravity model may overstate the impact of integration on

trade volumes. 16

Amita Batra (2004) has used the gravity model has been enhanced in

advance to analyze the flow of world trade and the coefficients obtained are then

used to predict trade potential for India. The dependent variable in all tests

performed were total merchandise trade (exports plus imports in U.S. dollars), in

log form, between pairs of countries. Results show that the authors estimate the

gravity equation fits the data and provide timely and accurate revenue and

elasticity sense of distance and estimates for others, such as geographic, cultural

characteristics and history. Alternative size of GNP in current dollar value and

purchasing power parity is not changes either the sign or significance of different

explanatory variables. The highest amount of potential trade between India and

the Asia-Pacific region, follow by Western Europe and North America. Countries

like China, UK, Italy, and France only disclose the maximum potential for

expanding trade with India.17

Konstantinos Kepaptsoglou, Matthew G. Karlaftis and Tsamboulas Dimitrio

s (2010) mentions gravity model has been widely used in the study of

international trade over the last 40 years as strength and endurance quite

empirically clear. Gravity model used to assess the implications for policy and

trade, especially recently, to analyze the impact of Free Trade Agreements on

international trade. The aim is to review new empirical literature on gravity

15 Ranajoy Bhattacharyya and Tathagata Banerjee. (2006). Does the gravity model explain India’s direction of trade? A panel data approach, Research and Publications, pp 1-18.16 I-Hui Cheng and Howard J. Wall. (2005). Controlling for Heterogeneity in gravity models of trade and integration, pp 49-64.17 Amita Batra. (2004). India’s global trade potential: the gravity model approach, pp 1-43.

7

models, highlight best practices and provide an overview of the impact Free

Trade Agreements on international trade. 18

According to the authors, Dixit (1989), Eichengreen & Irwin (1996),

Anderson & Marcouiller (1999) and Das et. Al. (2001) showed that determine

sunk costs in the model of bilateral trade. By using this literature, some

economists have introduced the lagged trade variable in the model. However, in

determining the commercial model, it is expected that the economic agents’

expectations are more important than the lagged trade variable. In addition,

expectations are likely to be included in a model of bilateral trade by considering

the cost of risk. In this paper, the authors develop a model of Anderson and

Wincoop (2003) for the theoretical gravity model that takes into account the

costs of trade are expected to determine the volume of bilateral trade. Enable

the development of new theories in the hope of determining the commercial

model. Authors also suggest methods for estimating the future growth of

bilateral trade costs. In addition, theoretical models allow authors to justify the

existence of several variables that are commonly used in the gravity models.

Lack of hope can lead to wrong interpretation of the coefficients of these

variables. 19

Shaohui Mao and Dr Michael J. Demetsky (2002) examine the application

of gravity models for the delivery of the process flow distribution throughout the

country according to their commodity. Transport stream output and attraction

equations are then developed for the Virginia area. Gravity model has also been

implemented in the allocation for primary commodities in this area. Four

scenarios commodity flows in the state and national levels that are considered to

determine the flow within and between Virginia and outside the region. Friction

factors is calculated by regression analysis using logform the gamma function

and calibrated with the distribution of the long journey and the root mean

squared error method. K-factor is introduced to determine trends and aid in

model predictive capability. Transport stream output and attraction equations

were applied to the factors of production to estimate the socio-economic

attractiveness in the future. Research done by the authors showed that gravity

model for predicting the flow of goods in the distribution of goods. This study

18 Konstantinos Kepaptsoglou, Matthew G. Karlaftis and Dimitrios Tsamboulas. (2010). The gravity model specification for modeling international trade flows and free trade agreement effects: a 10-year review of empirical studies, The Open Economics Journal, 3, pp 1-13.19 Javad Abedini. (2005). The gravity model and sunk costs: a theoretical analysis, pp 1-29.

8

shows that the gravity model for predict the flow of good in the cargo flow of

commodities.20

The author of this article do research aimed at studying the intra-ASEAN

trade is created (higher trade with competent members) or transfer dealers

(higher trade with members of the incompetent) for both commercial of inter-

industry and intra-industry. Since the ASEAN integration efforts should be

directed to "open regionalism", the factors that affect trade, both inter-industry

trade and intra-industry trade at the sector level have been identified. This

research adopts an extended gravity model of total and phase-separated by

using a number of the Standard International Trade Classification (SITC) Revision

2. Based on the findings, in general, policies that encourage growth and

development in the area must be maintained. In addition, steps should be taken

to ensure lower transportation costs include both physical infrastructure and

increase the efficiency of the transport system. Emphasis should also be focused

on other factors that may affect the demand for exports such as product

development to improve the quality of exports to meet the priorities of importing

countries.21

Mark N. Harris and Laszlo Matyas (1998) states that the types of gravity

model are often used to analyze trade flows between countries and trade

blocs. Recently, Gravity model has been adapted to public and panel data

settings, where time-series cross section data set was collected. This approach

not only increases the degree of freedom, but also enables precise specification

of the effects of source and target countries and the effect of time (or the

business cycle). In this paper, the authors have reviewed the framework of the

union, the latest developments in econometric methodology, Gravity model, and

improve the estimation technique to calculate the possible simultaneity

bias. Although the equipment is completed determined the impact of the Gravity

model has been expected previously, this paper contains the result of the first of

his random effects. The author also suggests a connection to the base model,

which explains the fact that contemporary trade flows that may be closely

related to the previous. Finally, all the various models and methods are

illustrated with applications to export flows in the APEC area. The result clearly

20 Shaohui Mao and Dr. Michael J. Demetsky. (2002). Calibration of the gravity model for truck freight flow distribution, pp 1-61.21 Ruzita Mohd. Amin, Zarina Hamid and Norma MD Saad. (2009). Economic integration among ASEAN countries: evidence from gravity model, 40, pp1-86.

9

shows that it is important to determine the correct model, in terms of resources,

targets and effects of business cycles. Explanatory variables of interest are found

in domestic GDP and the target, and depending on the specifications of the

resident, local and domestic exchange rate, foreign currency reserves. 22

3.0 The Economic Model

3.1 Theoretical Framework

TRADE = f (GDPM, GDPS, POP, DIST)

The value of trade between Malaysia and Singapore is determined by GDP

of the Malaysia and Singapore, populations of the Malaysia and Singapore and

the distance between Malaysia and Singapore is based on a gravity model. The

economic model is:

TRADE= 1 + 2GDPM + 3GDPS + 4POP+ 5DIST

22 Mark N. Harris and Laszlo Matyas. (1998). The econometries of gravity model, Melbourne Institute Working Paper, 5, 98, pp 1-18.

10

TRADE

GDPM

GDP of Malaysia

GDPS

GDP of Singapore

POP

Population between the two countries

DIST

Distance between the two countries

Where TRADE represents the value of trade between Malaysia and

Singapore, billion $US, GDPM and GDPS represents the GDP of Malaysia and

Singapore, billion $US, POP represents the populations of the country Malaysia

and Singapore, million and DIST represents the distance between country

Malaysia and Singapore.

The symbols are the unknown parameters βkthat describe the dependence

of the value of trade between Malaysia and Singapore on different country of

GDP (GDPM and GDPS), populations of the country Malaysia and Singapore and

distance between country Malaysia and Singapore. Parameter β1is the intercept

and it is also the value of dependent variable when each of the independent

variables takes the value of zero. However, this parameter don’t have clear

economic interpretation in many case including this case because it is not

realistic to have situation where A = E = 0.

The signs of parameter can be positive or negative. If an increase in the

parameter leads to an increase in the dependent variable, then parameter > 0.

Conversely, if an increase in the parameter leads to a decrease in the dependent

variable, then parameter < 0.

The hypothesis that we wish to test is the significance of the GDP of the

Malaysia and Singapore, the populations of the Malaysia and Singapore and the

distance between Malaysia and Singapore on the value of trade between

Malaysia and Singapore, which means to examine the relationship between the

GDP of the Malaysia and Singapore, the populations of the Malaysia and

Singapore, the distance between Malaysia and Singapore and the value of trade

between Malaysia and Singapore.

4.0 The Econometric Model

4.1 Econometric Model

LNTRADEi = E(LNTRADEi )= 1 + 2LNGDPMi + 3LNGDPSi + 4LNPOPi+ 5LNDISTi

+ ei

This model is a log-log model where both dependent and independent

variables are transformed by the “natural” logarithm, the purpose to solve the

measurement problem. LNTRADEi represents the value of trade between

Malaysia and Singapore in year i, LNGDPMi and LNGDPSi represents the GDP of

Malaysia and Singapore in year i, LNPOPi is represents the populations of

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Malaysia and Singapore in year i, LNDISTi represents the distance between

Malaysia and Singapore in year i, and e i is error term in year i.

Error assumption (e): cost of transportation between Malaysia and

Singapore, cost of communications between Malaysia and Singapore, exchange

rate between Malaysia and Singapore, total production at Malaysia and

Singapore and others.

4.2 The Assumption of This Model, (Hill. R.C et al, 2008: 111)

To make the econometric model, assumptions about the probability

distribution of the random error ei need to be made. The assumptions that we

introduce for ei are similar to those introduced for the simple regression model.

1. LNTRADEi = 1 + 2LNGDPMi + 3LNGDPSi + 4LNPOPi + 5LNDISTi + ei, i=1,

……,30

2. LNTRADEi = 1 + 2LNGDPMi + 3LNGDPSi + 4LNPOPi + 5LNDISTi + ei, the

expected of LNTRADEi depends on the explanatory variables and the

unknown parameter.

E(e i) = 0

3. Var(LNTRADEi)= Var (e i) =ơ 2. The variance for total output is same with variance of error term.

4. Cov (LNTRADEi, LNTRADEj) = Cov (ei, ej) = 0. The random error (e) and dependent variable (T ij) statistically independent, uncorrelated.

5. The variables of each are parameter are not random and are not exact linear functions of the other independent variables.

In addition to the above assumptions about the error term (and hence

about the dependent variable), make two assumptions about the explanatory

variables. The first is that the explanatory variables are not random variables.

Thus we are assuming that the values of the explanatory variables are known to

us prior to observing the values of the dependent variable. This assumption is

realistic for trade variables and trade for these variables are set accordingly. For

cases in which this assumption is untenable, our analysis will be conditional upon

the values of the explanatory variables in our sample, or further assumption

must be made.

The second assumption is that any one of explanatory variables is not an

exact linear function of the others. This assumption is equivalent to assuming

that no variable is redundant. As we will see, if this assumption is violated, a

condition called exact collinearity, the least squares procedure fails.

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5.0 The Data

5.1 Data

In this term paper, we used the annual data from year 1979 until 2008 of

the bilateral trade between Malaysia and Singapore. The annual variables that

we used in this term paper are the GDP of the Malaysia and Singapore (Billion

USD), the populations of the Malaysia and Singapore (Million), the distance

between Malaysia and Singapore and the value of trade between Malaysia and

Singapore.

5.2 Data Source

The source of the data is from the website. This is from:-

Trading Economics http://www.tradingeconomics.com/

Department of statistics Singapore http://www.singstat.gov.sg/

Malaysia external trade Development Corporation

http://www.matrade.gov.my/cms/content.jsp?

id=com.tms.cms.section.Section_727381fb-7f000010-562d562d-dcfd3067

.

6.0 The Estimate and Inference Procedures

6.1 Estimation Method

In this term paper, we used the Least Squares Estimation method to

examine the data to know relationship the variable in this model. Panel data

allow estimating a time-varying elasticity of trade with respect to distance as

dummy variable.

6.2 Reason for Choosing Least Squares Method

There are several reasons for choosing this method. The first reason is

because this method can help us to find out the parameter estimates are in the

“coefficient” column, the symbol of “C” represents constant term (the estimate

b1), the estimate b2 is Malaysia GDP, the estimate b3 is Singapore GDP, the

estimate b4 is population Malaysia and Singapore, and the estimate b5 is distance

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between Malaysia and Singapore. The second reason is because we can find out

the standard error, t-statistic, and probability for each parameter. Standard

errors are used in hypothesis testing and confidence intervals. Besides, we use

the value of t-statistic to decide either to reject the null hypothesis or not to

reject it. In addition, the p-value, or known as probability value of the outcome

can help us to determine the outcome of the test by comparing the p-value to

the chosen level of significance (), without looking up or calculating the critical

values. Through this Least Square method, it provides the coefficient of

determination, or R2, which is the fraction of the sample variance of Y i explained

by the regressors.23 Another reason to choose this method is to obtain the

adjusted R2, which is an alternative for measure the goodness-of-fit. In addition,

the adjusted R2 is a modified version of the R2 that does not necessarily increase

when a new regressor is added.24 Besides, the reason for using Least Square

method is to estimate the sum of squared least squares residuals, reported as,

sum square resids, which is used to explain the part of total variation in y about

its mean that is not explained by the regression. The last reason to choose the

Least Squares method is to find out the F-statistic to test the overall significance

of a model.

6.3 Hypothesis Testing Procedures

Hypothesis testing, which is the foundation for all inference variables in

classical econometrics.25 Overall, we will use the t-test, p-value, interval

estimation, and F-test to test the significance of the model. First, we use t-test to

estimate the value for test statistic which will be used to decide whether to reject

the null hypothesis or not to reject it. It has a special feature where the

probability distribution is completely know when the null hypothesis is true, and

it has some other distribution if the null hypothesis is not true. Besides, we can

also use the p-value in the hypothesis testing. P-value, or also known as

23 James H. Stock, Mark W. Watson. (2007). New York: PearsonEducation, Introduction To Econometrics, Second Edition, Inc, pg 200.24 Ibid., pg 201.25 Russell Davidson, James G. MacKinnon (2004). New York: Oxford University, Econometric Theory and Methods, Press, Inc, pg 177.

14

probability value, is used to determine the outcome by comparing it to the

chosen level of significance, , whether to reject or not reject the null hypothesis.

It is the quickest way to find out the hypothesis test by comparing the p-value

without looking up or calculating the critical value. The interval estimation is

used to estimate the range of the outcome. F-test has the same function with t-

test, but it has being distinguished by test the joint null hypothesis.

LNTRADE= 1 + 2LNGDPM + 3LNGDPS + 4LNPOP+ 5LNDIST + e

6.3.1 T-test Procedures

Testing of Malaysia GDP.

1. The null and alternative hypothesis is H0: 2 = 0 and H1: 2 ≠ 0.

2. The test statistic, if the null hypothesis is true, is t = b2/se(b2) ~ t(N-K)

3. Using a 5% significance level ( = 0.05), and noting that they are 25

degrees of freedom, the critical values that lead to a probability of 0.025

in each tails of the distribution are t(0.975, 25) = 2.059 and t(0.025, 25) = -2.059.

Thus, we reject the null hypothesis if the calculated value of t from step 2

is such that t 2.059 or t -2.059. If -2.064 t < 2.064, we do not reject

H0.

It is the same procedures to test the LNGDPS, LNPOP, and LNDIST by substituted

the null hypothesis and alternatives hypothesis as following:

LNGDPS H0: 3 = 0 and H1: 3 ≠ 0

LNPOP H0: 4 = 0 and H1: 4 ≠ 0

LNDIST H0: 5 = 0 and H1: 5 ≠ 0

6.3.2 P-Value Procedures

For the p-value for LNGDPM, we reject H0: 2 = 0 if P 0.05.

For the p-value for LNGDPS, we reject H0: 3 = 0 if P 0.05.

For the p-value for LNPOP, we reject H0: 4 = 0 if P 0.05.

For the p-value for LNDIST, we reject H0: 5 = 0 if P 0.05.

The p- value is given by:

P(t(25)> the value of t-statistic in positive) + P(t(25)< the value of t-statistic in

negative)

6.3.3 Interval Estimation Procedures

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We are finding a 95% interval estimate for 2, 3, 4, and 5, the response

LNTRADE to a change in GDP of Malaysia, GDP of Singapore, population, and

distance.

Step 1: Find a value from the t(25)-distribution, called tc.

P(-tc < t(25) < tc) = 0.95

Step 2: Find the value of tc and rewrite the equation.

P(−tc bk−kse (bk)

tc)=0.95

Step 3: Rearrange the equation.

P[b2 – tc x se(bk) k b2 + tc x se(bk)] = 0.95

Step four: The interval endpoints.

[bk – tc x se(bk), bk + tc x se(bk)]

6.3.4 F-test Procedures

1. We want to test H0: 2 = 0, 3 = 0, 4 = 0, 5 = 0

Against the alternative

H1: At least one of k is nonzero for k = 2, 3, 4, 5

2. If H0 is true F=(SST−SSE)/(5−1)SSE/ (30−5)

F (4,25)

3. Using a 5% of significance level, we find the critical value for the F-

statistic with (4,25) degree of freedom is Fc = 2.76. Thus, we reject H0 if F

2.76.

7.0 The Empirical Results and Conclusions

7.1 Parameter Estimates

Dependent Variable: LNTRADEMethod: Panel Least SquaresDate: 03/06/11 Time: 15:15Sample: 1979 2008Periods included: 30Cross-sections included: 60Total panel (balanced) observations: 1800

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Variable Coefficient Std. Error t-Statistic Prob.  

C -108.0519 1.714297 -63.02985 0.0000LNDPM 0.050911 0.004747 10.72600 0.0000

LNDGPS 0.231105 0.018317 12.61719 0.0000LNPOP 7.713531 0.112518 68.55378 0.0000LNDIST -0.108752 0.463132 -0.234819 0.8144

Effects Specification

Cross-section fixed (dummy variables)

R-squared 0.988945    Mean dependent var 85.50633Adjusted R-squared 0.988544    S.D. dependent var 45.06967S.E. of regression 4.824032    Akaike info criterion 6.020005Sum squared resid 40398.94    Schwarz criterion 6.215402Log likelihood -5354.005    Hannan-Quinn criter. 6.092134F-statistic 2464.967    Durbin-Watson stat 2.711914Prob(F-statistic) 0.000000

7.2 EViews regression output.

Using the annual data from year 1979 until 2008 between Malaysia and

Singapore, the output from the software package EViews is shown in Figure 7.1.

Based on this EViews output, it has shown the parameter estimates or also

known as coefficient estimates for each least squares estimators such as b1, b2,

b3, b4 ,and b5.

b1 = - 108.0519; b2 = 0.050911; b3 = 0.231105; b4 = 7.713531; b5 = -0.108752

LNTRADE = -108.0519 + 0.050911LNGDPM +0.231105 LNDGPS + 7.713531 LNPOP + (-

0.108752 LNDIST)

We measure the fit of the data base on adjusted R2 = 0.9885 where, 98.85

percent changing in trade explain by Malaysia GDP, Singapore GDP, Population

and Distance. However 1.15 percent changing in trade explain by other factor.

7.3 Interpretation of Coefficient Estimates

The coefficient on Malaysia GDP is positive. We estimate that, with

Singapore GDP, population, and distance held constant, an increase in Malaysia

GDP by 10 % will lead to an increase in trade of 5%. Or, expressed differently, a

decrease in Malaysia GDP of 10% will lead to a decrease in trade of 5%.

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Besides, the coefficient on Singapore GDP is positive. We estimate that,

with Malaysia GDP, population, and distance held constant, an increase in

Singapore GDP of 10% will lead to an increase in trade of 2%. Expressed in

differently, a reduction in Singapore GDP of 10% will lead to a decrease in trade

of 2%.

In addition, the coefficient on population is positive. We estimate that,

with Malaysia GDP, Singapore GDP, and distance held constant, an increase in

population of 1% will lead to an increase in trade of 7.%. Or, expressed in

differently, a decrease in population of 1% will lead to a decrease in trade of 7%.

However, the coefficient on distance is negative. We estimate that, with

Malaysia GDP, Singapore GDP, and population held constant, an increase in

distance of 10% will lead to a decrease in trade of 1%. Or, expressed in

differently, a decrease in distance of 10% will lead to an increase in trade of 1%.

7.4 The Values of Test Statistic and Statistical Significance

Based on the EViews output Figure 7.1. Using the t-test to show the

significant of this model and how each variable can influence trade. The value of

test statistic for Malaysia GDP is. 10.72600 Refer to the t-test procedures at part

(6.3.1). Since t = 10.72600 > 2.059, we reject the null hypothesis that 2 = 0.

That is, there has relationship between Malaysia GDP and trade at 5% significant

level.

On the other hand, the value of test statistic for Singapore GDP is

12.61719. Based on 5% significant level, we reject the null hypothesis that 3 = 0

because t > 2.059. That is, Singapore GDP has relationship to explain changing

on trade.

Besides, the value of test statistic for population is 68.55378. Since t =

68.55378 > 2.059, we reject the null hypothesis that 4 = 0. In 5% significant

level, population can explain why trade change if population also change.

The value of test statistic for distance is -0.234819. We do not reject the

null hypothesis that 5 = 0. That is, we are not able to conclude distance is full

factor why trade is reduce on 5% significant level test where, -0.234819 < -

2.059. However, we can conclude that there is a statistically significant negative

relationship between distance and trade.

7.5 The P-Value

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Based on the EViews output, the p-value for Malaysia GDP is 0.0000. Using

the p-value procedures at part (6.3.2), since 0.0000 < 0.05. That is statistically

significant and factor on trade changing. Next, the p-value for Singapore GDP is

0.0000, where 0.0000 < 0.05. That means Singapore GDP factor of trade

changing. Population is factor on explain why trade is change because the p-

value is 0.0000 < 0.05. The p- value for distance is 0.8144 and since p-value >

0.05. Distance is not significant to explain why trade is change but is still

influence on changing because today globalizations transport encourage trade in

big scale.

7.6 Interval Estimation

Based on the procedures for interval estimation in (6.3.3), the interval

estimate for Malaysia GDP is (0.041136, 0.060685). That is, we estimate “with

95% confidence” that an additional 1% of consumption will increase between

0.041136 and 0.060685 on trade.

The interval estimate for Singapore GDP is (0.193390, 0.268819). That is,

we estimate that the additional 1% of Singapore GDP will increase the trade in

between 0.193390 and 0.268819.

The interval estimate for population is (6.981856, 7.445205). That is, we

estimate that an additional 1% of population will increase between 6.981856 and

7.445205 on trade.

The interval estimate for direct is (-1.062340, 0.844836). We estimate that

an additional 1% of distance will either decrease the trade until -1.062340 or

increase until 0.844836.

7.7 The F-test

F-test is used to make sure the significant on this model based on the

procedures in (6.3.4), the value of F-test for the model is 2464.967. Since

2464.967 ≥ 2.76, we reject the H0: 2 = 0, 3 = 0, 4 = 0, 5 = 0, 6 = 0. All

variable is factor that influence of trade changing because the model is fit and

significant to explain why trade raise and how trade will growing up.

7.8 The Relation with Previous Estimate

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In this term paper, we have examined the relationship of economic

growth, or known as trade, with the trade variables and found out that each of

trade variables will affect the economic growth. According to previous estimate,

Wong, (2008) examined the importance of exports and domestic demand to

economic growth in ASEAN-5, namely Indonesia, Malaysia, the Philippines,

Singapore and Thailand before Asia financial crisis, 1997- 1998. It has shown that

there is a relationship between export economic growth.26 It is same with our

estimate that the trade variables, which are Malaysia GDP, Singapore GDP,

population, and distance, are influencing economic growth, or trade.

7.9 Economic Implication

The impacts of trade variables are important to the economic growth. The

component of trade variables, which are Malaysia GDP, Singapore GDP,

population, and distance are vital to stimulate economic growth. In the estimated

trade model, Malaysia GDP, Singapore GDP, and population have a positive

relationship with the trade. This means that one of the component or the entire

component is increase, the trade will increase, or in other word, the economic

are growing. While for the distance, it has a negative relationship with the trade.

If the distance is increase, it will lead the trade decreased.

8.0 Possible Extensions and Limitations of the Study

From the empirical result above, the relationship between gravity model

variables has shown strong in determine volume of trade, this is because the R-

square value is 0.988945. Even though, the equation still can be expanded for

the future research.

First of all, Malaysia and Singapore have the highest values of GDP, which

measures the total value of all goods and services produced in an economy.

There is a strong empirical relationship between the size of country’s economy

and the volume of both its import and its export.

Besides that, we should not ignore the role of population plays in the

gravity model. This is because from the population theory of neo-classical

economics involved a series of equations which showed the relationship between

labour-time, living standard, poverty rate, and investment which found that the

26 Omoke Philip Chimobi, Ugwuanyi Charles Uche. (2010). Export, Domestic Demand and EconomicGrowth in Nigeria: Granger Causality Analysis. European Journal of Social Sciences. Vol. 13.

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population and the economic growth in the real economic system have causal

effect.

Lastly, the distance between two countries important plays to fit the data

on value of trade. There are important if two countries’ port distance is nearer,

so that trade of two countries can reduce their transport fees.

9.0 Conclusion

Through using the t-test, p-value, interval estimation, and F-test on the

Malaysia’s economic annual data, we can conclude that all the gravity model

variables have played an important role in measure and also affect on volume

trade in Malaysia. In addition, the EViews output has shown the 0.988945 for the

R-squared, it means all of those gravity model variables or explained variables

are fitted regression equation LNTRADE= 1 + 2LNGDPM + 3LNGDPS +

4LNPOP+ 5LNDIST + e. Besides, we also conclude that, trade value, GDP of

Malaysia, GDP of Singapore, population and port distance between Malaysia and

Singapore played the significant role and also have the positive effect on the

volume of trade. Port distance between Malaysia and Singapore also has played

an important role in measuring volume of trade, but it has a constant effect on

GDP.

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