20
��� = �
� �
� where X
ij
is the export value of natural rubber commodity from country j, X
it
equals the value of total exports for country j, W
j
represents the world export value of natural rubber commodity and W
t
is the total value of world exports. The competitiveness index value of a commodity in the RCA has two
possibilities, namely: 1. If RCA 1, it indicates that the share of natural rubber commodity in the
total exports of country j is greater than the average share of natural rubber export in all countries the world. This means that country j has a
comparative advantage, so it is relatively more specialized in natural rubber commodity.
2. If RCA 1, it shows that the share of natural rubber commodity in the total exports of country j is less than the average share of export natural rubber
commodity in all countries the world. This means that country j does not have a comparative advantage, so the country will not specialize in natural
rubber commodity.
4.2.3 The Least Square Dummy Variable LSDV
The Least Squares Dummy Variable LSDV method aims to represent differences in the intercept, i.e., through the use of dummy variables Firdaus,
2011. To illustrate this approach, for example in the initial equation as in the pooled least square PLS equation and the group of dummy variables d
git
= 1 g=i.
� = �
+ � � + �
By entering a number of d
git
= 1 g=i, the initial equation becomes: �
= � �
+ � �
+ ⋯ + � �
+ � � + �
This equation can be estimated with the OLS approach in order to obtain the parameter β
LSDV.
The advantage of the LSDV approach is that it can generate the estimated parameter, β, which is unbiased and efficient. The weakness of this
approach, however, is that if the research contains a large number of observation units, it looks unmanageable. To test whether the intercept is significant or not,
the F-test can be used with the following hypotheses:
� = �
= � = �
= ⋯ = � H
1
= one value of α is not the same. The hypothesis can be directly used to test whether it is better to use the
PLS or the LSDV approach. The basic rejection of H is to use the F-statistic that
is: � =
� − �
1 − � .
�� − � − � � − 1
21 where R
2 DV
equals the R
2
of LSDV, R
2 p
is the R
2
of PLS and K is the number of variables.
If the value of the F-stat test result is greater than the values in the F-table, then there is evidence for the rejection of the null hypothesis so that the
assumption that α is the same for all individuals can be rejected.
4.3 Model Formulation
The dependent variable used in the model is the natural rubber export volume to the destination countries. Meanwhile, the independent variables are,
among others, the natural rubber production from exporting countries, the real GDP of destination countries, remoteness, and the real exchange rate. The gravity
equation model of natural rubber in international trade can be formulated as follows:
���
= � +
� ln ����
+ �
ln ���� +
� �
+ �
ln �� + �
� +
� �
+ � �
+ � �
+ ⋯ + � �
+ � where Y
is the natural rubber export value from country i to country j US, with PROD
it
as the volume of the natural rubber production in country i kg, the variable RGDP
jt
represents the real GDP of country j US, R
jt
accounts for the remoteness of country j, ER
ijt
is the currency exchange rate from country i to country j, with α
� representing the dummy variable for the exporting country
effect and γ �
representing the dummy variable for the importing country effect. β
indicates the intercept, while β indicates the parameter n= 1, 2, …, N, t is
the year, i represents the exporting countries and j represents the importing countries.
4.4 Goodness of Fit Test 4.4.1 Economic Criteria
The economic criteria will be tested by looking at the sign and the magnitude of each constant and variable. Economic criteria require that the sign
and magnitude of the coefficient results are in accordance with economic theory.
4.4.2 Econometric Criteria a.
Autocorrelation Autocorrelation is the correlation between members of series of
observations, which are then sorted by time and space Gujarati, 2011. Autocorrelation is detected when there is a significant relationship between the
estimation errors of the primary observation with the estimation errors of other observations. Autocorrelation is a problem that generally occurs when dealing
with time series data. The presence of autocorrelation results in an inefficient estimation or forecast, even though the estimator is still unbiased and consistent.
Another effect is that the standard error is biased and inconsistent, so that the result of the hypothesis becomes invalid. Guidelines on number DW Durbin-
Watson, which is used to detect can be seen in Table 3.
