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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
1
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
11
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.
12
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
13
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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