Page 152 - Contributed Paper Session (CPS) - Volume 4

CPS2160 Aye Aye Khin et al.
For Equation (10), it explains India’s natural rubber import demand VECM
model results, the explanatory variables accounted for about 71.80 percent of
the variation in the India natural rubber import demand equation. Estimations
reveal that the explanatory variables, namely the world NR price (nrstr20t-1),
India’s exchange rate (exrmt-1), NR SMR20 price (nrsmr20t-1) and the lag
variable of India’s natural rubber import demand (nrimpt-1), were the most
important explanatory variable with statistically significance at the 0.01, 0.05
and 0.10 level, respectively.
Looking at Equation (11), it explains on USA’s natural rubber import
demand VECM model results, the explanatory variables accounted for about
90.17 percent of the variation in the USA natural rubber import demand
equation. Estimations reveal that the explanatory variables, namely the world
NR price (nrstr20t-1), NR SMR20 price (nrsmr20t-1) and the lag variable of USA’s
natural rubber import demand (nrimpt-1), were the most important explanatory
variable with statistically significance at the 0.01 and 0.05 level, respectively.
Last but not least, looking at Equation (12), it explains on Japan’s natural
rubber import demand VECM model results, the explanatory variables
accounted for about 83.73 percent of the variation in the Japan natural rubber
import demand equation. Estimations reveal that the explanatory variables,
namely the world NR price (nrstr20t-1), Japan’s exchange rate (exrmt-1), NR
SMR20 price (nrsmr20t-1) and the lag variable of Japan’s natural rubber import
demand (nrimpt-1), were the most important explanatory variable with
statistically significance at the 0.01, 0.05 and 0.10 level respectively.
Moreover, Table 4.1 above shows China’s granger causality analysis results.
In the Engle-Granger test, F-statistics of the two variables of world NR price
(nrstr20) and import demand (nrimp); exchange rate (exrm) and import demand
(nrimp) are only significant at α 0.01, 0.05 & 0.10 level. Therefore, there are world
NR price (nrstr20) “Granger causes” to import demand (nrimp) and exchange
rate (exrm) “Granger causes” to import demand (nrimp). Moreover, their
granger causality relationships are bidirectional. Then, they are cointegrated and
also a long-run equilibrium relationships between each two variables. Table 4.2
above shows India’s granger causality analysis results. In the Engle-Granger test,
F-statistics of the two variables of exchange rate (exrm) and import demand
(nrimp) is only significant at α 0.05 level. Therefore, there is a variable exchange
rate (exrm) “Granger causes” a variable import demand (nrimp) and the
direction of the granger causality relationship is unidirection. Then, there is
cointegrated and also a long-run equilibrium relationships between two
variables.
Table 4.3 above shows USA’s granger causality analysis results. In the Engle-
Granger test, import demand (nrimp) to world NR price (nrstr20); and import
demand (nrimp) to SMR20 price (nrsmr20) are only significant at α 0.10 level.
Therefore, their directions of granger causality relationship are unidirection. Then,

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