tiểu luận kinh tế lượng HOW EXPORT AND FDI AFFECTS IMPORT IN VIET NAM (1986 2018)

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS ---***---ECONOMETRICS REPORT HOW EXPORT AND FDI AFFECTS IMPORT IN VIET NAM 1986-2018 Group 7: Nguyễn Trọng Khoa Trần Thị Ng

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS

-*** -ECONOMETRICS REPORT

HOW EXPORT AND FDI AFFECTS IMPORT IN

VIET NAM (1986-2018)

Group 7:

Nguyễn Trọng Khoa Trần Thị Ngọc Diệu Trương Công Toàn

Lecturer: PhD Từ Thuý Anh

Hanoi, 10/2019

TABLE OF CONTENTS

I INTRODUCTION

As much as Economy is a meaningful science that determines the social development in general and national growth in particular, Econometrics is the use of statistical techniques to understand those issues and test theories Without evidence, economic theories are abstract and might have no bearing on reality (even if they are completely rigorous) Econometrics is a set of tools we can use to confront theory with real-world data

Since its inception, econometrics has provided economists with a sharp instrument for measuring economic relations As economics students, we recognize the need to study and learn about Econometrics in logical and problem analysis To better understand how to put the Econometrics into reality and to apply the Econometrics effectively and correctly, our team would like to develop the econometrics report under the guidance of PhD Tu Thuy Anh In this report, we used the econometric analysis tool Gretl to analyze the topic "How Export and FDI affect Import in Vietnam in the period of 1986-2018"

To the extent of purpose and resources, there are still deficiencies in this report, but we look forward to providing readers with a decent view of the overall of the data set given and the knowledge that we have gained through PhD Tu Thuy Anh’s Econometrics course

II LITERATURE REVIEW

Globalization and international economic integration are indispensable trends

of all countries in the world today With the accession to world economic organizations and especially becoming an official membership of the World Trade Organization (WTO), Vietnam has been actively participating in this trend The World Trade Organization is the biggest trade organization in the world, accounting for nearly 90% of world trade Joining the WTO is participating in the common playing field of the world market, promoting trade and investment promotion

Firmly integrated into international economic relations, Vietnam has an opportunity to expand exports of goods in which the country has strengths Thanks to the high export of mourning, it helped to increase the amount of foreign currency for

import, boosting the increase of imports Along with the increase of international trade, FDI has also increased, creating new industries and products to diversify the domestic market, reducing imported goods from foreign, contributing to improving the balance of international payments

Thus, it is essential to understand and evaluate the actual import, export and foreign direct investment of FDI in Vietnam in the current context of the economy

III THEORETICAL BACKGROUND

From 1986 up to now, Vietnam has adapted various innovative economic strategies and the Sixth Party Congress (December,1986) was considered as the basic turning-point of the Socialism in Vietnam with the introduction of guidelines for the comprehensive renovation of our country in terms of thoughts, organizational and personnel structure, administrative system, economic system, political system and other fields in the society The initiative launched in 1986 should be considered as a milestone for the transition from centrally-planned economy to socialist-oriented market economy together with a range of social, political and economical changes in Vietnam The private production and business innovative idea was the general break-through since 1986 up to now With the application of “Khoan 100”, “khoan 10” in agricultural sector; “market price structure” economic sector or Vietnam would like to

be friend of all nations and territories in the world in foreign policy, Vietnam has gradually established and expanded import-export markets and trade partners in the direction of multilateral relationships The successes of Vietnam’s foreign trade are showed by statiscal figures in the five year periods of development during 1986-2018 The average of total merchandise trade from 1986-2018 is 103.626 billion USD In each period, the growth rate is quite high For example, the growth rate in the period

of 1996-2000 tripled compared to that of the previous period, reaching approximate

100 billion USD (the average growth rate is 17,2 percent) regards to the period of 2001-2005, the growth rate almost doubled compared to that of the previous period, at

241 billion USD (The average growth rate is 18.2 percent) Of which, domestic economic sector in the 1986-1990 period played the most important role, making up 96.6 percent f total trade From 1986 to 2018, export value increases from 789 million

USD to 243,483 million USD and import value increases from 2.155 Billion USD in

1986 up to 236.687 billion USD in 2018, which is approximately 115th-fold increase only.the average growth rate of import value in 1991-1995 is the highest, at 127.3 percent

Foreign direct investment (FDI) is an important source of capital to supplement the total investment capital for economic growth of each country, including Vietnam Since the Law on Foreign Investment was adopted in 1987, Vietnam has attracted a large amount of foreign capital, and this capital inflow has made important contributions to economic development Since 1986, Vietnam has undertaken a comprehensive renovation of the country Compared to reform and transition from a planned economy to a market economy in other countries, innovation in Vietnam has its own characteristics Innovation in Vietnam takes place

in two dimensions: "from the bottom up" in cooperatives, enterprises and "top down" means the decisions of the Party and the State Relationship bidirectional for the renovation in Vietnam took place without conflict between "top" and "bottom", nor the "shock" is too strong to be created by the tough policies and measures and the willpower of the "top" leadership This is a remarkable feature of the process of innovation in Vietnam, both the top-down leadership and the creativity of the people from below Therefore, innovation has led to success This study aims to find relationships between exports, import, and FDI in the period 1986 - 2018 The results show that there is a long and significant relationship between investment and exports with total domestic output at a 95% confidence level

IV ECONOMETRIC MODEL

To demonstrate the relationship between Import and other factors, the regression function can be constructed as follows:

The Population regression function is set up:

(PRF ): ln (Import )=β1+β2Export+ β3FDI +u i

The Sample regression function is set up:

( SRF) : ln(^Import)=^β1+ ^β2Export+^ β3FDI

β 0 is the intercept of the regression model

β i is the slope coefficient of the independent variable x i

u is the disturbance of the regression model

^

β0 is the estimator of β0

^

β i is the estimator of βi

From this model, this report is interested in explaining Import in terms of Export and FDI

V DATA COLLECTION

1 Data overview

This set of data is a secondary one, as they are collected from a given source Data source: The World Bank, General Statistics Office of Viet Nam

The structure of Economic data: Time series data

2 Data description

Function we have in this report will include these following variables:

- Dependent variable: Import – The import of Vietnam from 1986 to 2018

(USD Million)

- Independent variables:

Export – The export of Vietnam from 1986 to 2018 (USD Million).

FDI – Foreign Direct Investment net inflows from 1986 to 2018 (USD

Million)

Exhibit 1: Statistic indicators of variables in the model

where:

S.D is the standard deviation of the variable

Min is the minimum value of the variable

Max is the maximum value of the variable

VI ESTIMATION OF ECONOMETRIC MODEL

1 Checking the correlation among variables

First of all, the correlation of l_Import, Export and FDI is checked by calculating the correlation coefficient among these variables The correlation coefficient measures the strength and direction of a linear relationship between two variables

Exhibit 2: Correlation Matrix for Log – linear Model:

As we can see:

+ Import is directly proportional to Export The set standard between these two variables is quite high

+ Import is directly proportional to FDI The set standard between these two variables is quite high

2 Regression run

Exhibit 3: Regression model

Equation of regression:

ln ( Import )=8.66273+0.000018195 Export+0.0000372087 FDI

Data explanation:

β1=8.66273: When all the independent variables are zero, the expected value of Import is 8.74558 (USD Million)

β2=0.000018195: When Export increases by 1 (USD Million), keeping the value of FDI constant, the Expected value of Import increases by 0.0018195%

β3=0.0000372087: When FDI increases by 1 (USD Million), keeping the value

of Export constant, the Expected value of Import increases by 0.00372087%.

The coefficient of determination R 2 :

- In our results, we can see R2 which indicates that the model explains all the variability of the response data around its mean

- That R2 = 0.723195 is quite high, which suggests that the model is good fit, which means 70.5324 % of the sample variation in the percentage vote for dependent variable (Import) is explained by the changes in the independent variables (Export, FDI)

3 Testing

3.1 Testing hypothesis

3.1.1 Testing an individual regression coefficient

Purpose: Test for the statistical significance or the effect of independent

variables on dependent one We have: α = 0.05

Testing the Export:

Given that the hypothesis is:

{H0: β2=0

H1: β2≠ 0

We see:

P-value of Export is 0.000000226 < 0.05 → Reject H0 → The coefficient β2 is statistically significant at the 5% significance level

Testing the FDI:

Given that the hypothesis is:

{H0: β3=0

H1: β3≠ 0

We see:

P-value of FDI is 0.0048 < 0.05 → Reject H0 → The coefficient β3 is statistically significant at the 5% significance level

3.1.2 Testing the overall significance

Purpose: Test the null hypothesis stating that none of the explanatory variables

has an effect on the dependent variable We have: α = 0.05

Given that the hypothesis is:

{H0: β i=0

H1:∃ β i ≠ 0 (i=1,2,3,4 )

We have:

P-value(F) = 4.29153e-09 < α= 0.05 → Reject H0→ All parameters are not simultaneously equal to zero → At least one variable has an effect on dependent one

→ At the 5% significance level, the model is statistically fitted

3.2 Testing the model’s problem.

3.2.1 Testing Omit variable.

Given that the hypothesis is:

{H0:The model does not omit variable

H1:The model omits variable

Ramsey’s RESET:

Exhibit 4: Ramsey’s RESET

We see:

p-value = P(F(2,28) > 13.4917) = 7.89e-05 < α = 0,05 → Reject H0 → The model omits variable

3.2.2 Testing multicollinearity.

Multicollinearity is the high degree of correlation amongst the explanatory variables, which may make it difficult to separate out the effects of the individual regressors, standard errors may be overestimated and t-value depressed The problem

of Multicollinearity can be detected by examining the correlation matrix of regressors and carry out auxiliary regressions amongst them

Using the following command vif regression to examine multicollinearity VIF commands specific to the variance inflation factor, if VIF is greater than 10, you have high multicollinearity and the variation will seem larger and the factor will appear to

be more influential than it is If VIF is closer to 1, then the model is much stronger, as the factors are not impacted by correlation with other factors

Exhibit 5: Multicollinearity test

We see:

VIF (Export) = 1.149 < 10

VIF (FDI) = 1.149 < 10

→At the 5% significance level, the model does not contain perfect multicollinearity

3.2.3 Testing Heteroskedasticity.

Heteroskedasticity indicates that the variance of the error term is not constant, which makes the least squares results no longer efficient and t tests and F tests results may be misleading The problem of Heteroskedasticity can be detected by plotting the residuals against each of the regressors, most popularly the White’s test It can be remedied by prespecifying the model look for other missing variables

Given that the hypothesis is:

{H0:The model does not have heteroskedasticity problem

H1:The model has heteroskedasticity

White’s test:

Exhibit 6: Heteroskedasticity test

We see:

p-value = P(Chi-square(5) > 14.792439) = 0.011287 < α = 0.05

→At the 5% significance level, there is enough evidence to reject H0 and conclude that this set of data meets the problem of Heteroskedasticity

3.2.4 Testing normality of residual

In statistics, normality tests are used to determine if a data set is well-modeled

by a normal distribution and to compute how likely it is for a random variable underlying the data set to be normally distributed

Given that the hypothesis is:

{ H0:The residuals have normality

H1:The residuals do not have normality

Using normality of residual in Gretl:

Graph 1: Normality of residual test

We see:

Chi-square(2) = 4.110 with p-value 0.1281 > α = 0.05

→ At the 5% significance level, the model has normality Even the u does not come from a normal distribution parameters estimates will be asymptotically normal according to Central Limit Theorem

3.2.5 Testing autocorrelation

Autocorrelation can be defined as correlation between the variables of some observations at different points of time

Given that the hypothesis is:{H0:The residuals do not have autocorrelation

H1:The residuals have autocorrelation

Using autocorrelation (Breasch-Godfrey test) in Gretl:

Exhibit 7: Breasch-Godfrey test

We see: p-value = 0.0003 < α = 0.05

→ At the 5% significance level, the model do not have autocorrelation

4 Solution

Correcting Omit variable

After reviewing a number of papers of other authors, we found that the model may lack some of the following variables: Population growth rate, Government spending, Money supply…

However, due to the limited time and capacity to collect information, the data

of the missing variables could not be collected This is a weakness of the model, so it will need to be overcome in the future to bring better efficiency

Correcting Heteroskedasticity

In a well-fitted model, there should be no pattern to the residuals plotted against the fitted values - something not true of our model Ignoring the outliers at the top center of the graph, we see curvature in the pattern of the residuals, suggesting a