Name: Pham Thi Lan Huong

Student code: 17110084

Dependent variable: Y is Exports of goods and services (constant 2000 US$) (Export)

Independent variables:

X1 is GDP per capita, PPP (constant 2005 international $) (GDP_PPP)

X2 is Foreign direct investment, net inflows (BoP, current US$) (FDI inflow)

X3 is Urban population (% of total) (Ur_pop)

Table 1. Regression result table

Model 1 use the original data which all countries data is included, and does not use the

logarithm function. From the regression results table, we can see that R 2 value is 0.469, which is

quite low, and the significance level is not very good (only one variable has significance level less

than 0.05 while there are two variables have significance level over 1) indicates that the results do

not have high statistical significance.

> # Multi-co-linearity diagnostics -------> vif = diag(solve(cor(x))) ; vif

GDP_PPP

FDI.inflow

Ur_pop

3.940302

1.422217

3.939001

The VIF results show that all three variables have VIF value less than 5. Hence, there is no multico linearity between these variables and no need to delete any variable.

Regarding the residuals plots, the Q_Q plot shows the normality of residual, however, we can see

that there are some problems with Hong Kong (Number 8) and Japan (Number 6) because these

two countries’ residual do not lie on the same regression line as other countries. Moreover, from

the “Residuals and Leverage” plot which used to detect the observation value that has large

influence on the results we can see that, both Hong Kong and Japan exceed the 1 value of the

Cook’s distance. Hence, it shows that Hong Kong and Japan have large influence on the regression

coefficient. Therefore, we might need to delete the data of these two countries.

In model 2 (delete data of Hong Kong and Japan), after considering the multiple co linearity and

stepwise method, we get a single regression model with only one variable is FDI inflow. Comparing

model 1 and model 2, we can see that, model 2 can explain 75.97% the relation between

dependent variable and independent variables while model 1 can only explain 46.9%. Beside, AIC

value and significance level of variables of model 2 are also better than model 1. We can see that,

deleting the data of Hong Kong and Japan can help to improve the quality of the model. To explain

for this, one reason might be, unlike other country, export of Japan and Hong Kong does not

depend much on the FDI investment pour into the country. These two countries already have

sufficient economies, which is the condition for the domestic enterprise to become the leading in

export. For the case of Japan, the leading export products are mostly electronic and machinery.

These industries depend strongly on imported materials and while show no dependence of FDI

since electronic market in Japan is not attractive for foreign investors due to the fact that they

cannot compete with the domestic enterprise so it seem that there no relation between export of

Japan with the FDI inflow to Japan.

Meanwhile, for other country, particularly the developing countries such as Vietnam and

Indonesia, due to the underdeveloped economy, the domestic companies do not have enough

resource to become the leading in export market, instead, most of the FDI enterprises are the one

contributes the most to export of the country. Thus, in the case of these countries, there are a

strong relation between export value and the inflow of FDI.

Model 3 use the logarithm function for GDP_PPP variable. From the regression result table, we

can see that model 3 has higher value of R 2 (0.7931), lower value of AIC and better significance

value compare to other two models. Therefore, model 3 appears to be the best model among all

three models. After the results of multiple co – linearity and stepwise method, we get a multiple

regression function with two independent variables (GDP_PPP and FDI inflow). The co

Student code: 17110084

Dependent variable: Y is Exports of goods and services (constant 2000 US$) (Export)

Independent variables:

X1 is GDP per capita, PPP (constant 2005 international $) (GDP_PPP)

X2 is Foreign direct investment, net inflows (BoP, current US$) (FDI inflow)

X3 is Urban population (% of total) (Ur_pop)

Table 1. Regression result table

Model 1 use the original data which all countries data is included, and does not use the

logarithm function. From the regression results table, we can see that R 2 value is 0.469, which is

quite low, and the significance level is not very good (only one variable has significance level less

than 0.05 while there are two variables have significance level over 1) indicates that the results do

not have high statistical significance.

> # Multi-co-linearity diagnostics -------> vif = diag(solve(cor(x))) ; vif

GDP_PPP

FDI.inflow

Ur_pop

3.940302

1.422217

3.939001

The VIF results show that all three variables have VIF value less than 5. Hence, there is no multico linearity between these variables and no need to delete any variable.

Regarding the residuals plots, the Q_Q plot shows the normality of residual, however, we can see

that there are some problems with Hong Kong (Number 8) and Japan (Number 6) because these

two countries’ residual do not lie on the same regression line as other countries. Moreover, from

the “Residuals and Leverage” plot which used to detect the observation value that has large

influence on the results we can see that, both Hong Kong and Japan exceed the 1 value of the

Cook’s distance. Hence, it shows that Hong Kong and Japan have large influence on the regression

coefficient. Therefore, we might need to delete the data of these two countries.

In model 2 (delete data of Hong Kong and Japan), after considering the multiple co linearity and

stepwise method, we get a single regression model with only one variable is FDI inflow. Comparing

model 1 and model 2, we can see that, model 2 can explain 75.97% the relation between

dependent variable and independent variables while model 1 can only explain 46.9%. Beside, AIC

value and significance level of variables of model 2 are also better than model 1. We can see that,

deleting the data of Hong Kong and Japan can help to improve the quality of the model. To explain

for this, one reason might be, unlike other country, export of Japan and Hong Kong does not

depend much on the FDI investment pour into the country. These two countries already have

sufficient economies, which is the condition for the domestic enterprise to become the leading in

export. For the case of Japan, the leading export products are mostly electronic and machinery.

These industries depend strongly on imported materials and while show no dependence of FDI

since electronic market in Japan is not attractive for foreign investors due to the fact that they

cannot compete with the domestic enterprise so it seem that there no relation between export of

Japan with the FDI inflow to Japan.

Meanwhile, for other country, particularly the developing countries such as Vietnam and

Indonesia, due to the underdeveloped economy, the domestic companies do not have enough

resource to become the leading in export market, instead, most of the FDI enterprises are the one

contributes the most to export of the country. Thus, in the case of these countries, there are a

strong relation between export value and the inflow of FDI.

Model 3 use the logarithm function for GDP_PPP variable. From the regression result table, we

can see that model 3 has higher value of R 2 (0.7931), lower value of AIC and better significance

value compare to other two models. Therefore, model 3 appears to be the best model among all

three models. After the results of multiple co – linearity and stepwise method, we get a multiple

regression function with two independent variables (GDP_PPP and FDI inflow). The co

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