Tiểu luận tiếng anh thương mại đại học Ngoại Thương Kinh Tế Vi Mô

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TABLE OF CONTENT
Content Page
1. Introduction
2. Methodology
3. Econometric model
4. Data description
5. Results and test
A. Results and analysis
1. Results
2. Analyze some basic content of results
B. Detect and cure default model
1. Normality
2. Multicollinearity
3. Heteroscedasticity
4. Autocorrelation
C. Detect and cure default new model
1. Normality
4. Autocorrelation
6.Conclusion and policy implication
a. Conclusion
b. Recommendation
c. Policy implication
APPENDIX
REFERENCES
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1. Introduction
a. Issue: Try to establish an econometrics model to analyse the impacts and
influences of Foreign Direct Investment (FDI) and urban unemployment ratio U
on Gross Domestic Products (GDP).
b. Reason for researching:
 Firstly, this is an issue relating to economics. All the knowledge we can gain from
this researching will be helpful for other economics subjects such as
Macroeconomics, International Economics….and our future jobs as well.
 Secondly, our country started to innovate in 1986; foreign investment law in Viet
Nam was promulgated on 29th
December, 1987 to make a legal basis for the
investment in Viet Nam from foreign investors. The fact is that since Viet Nam
opened to integrate, foreign investment has become a very important source of
capital for Viet Nam economy in industrialization and modernization. Being a
member of World Trade Organization (WTO), Viet Nam has many chances to
gain more FDI. However, now the issue is that how to use FDI effectively, make
FDI be an important factor to develop the economy.
The study of the effects of foreign direct investment and unemployment on economic
growth helps us to know the extent of the impact of FDI to GDP as well as U to GDP.
According to learning the theories and features, understanding characteristics of this and
trends to develop, we can make the directions and solutions to attract FDI and use FDI in
the most effective way; besides, try to bring back unemployment ratio to nature
unemployment standard in order to help GDP grow up.
That is all the reasons why we choose to research this topic!

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2. Methodology
*Economic theories:
a. Gross domestic product (GDP) is the market value of all final goods and services
produced within a country in a given period of time.
In the real world, the market values of many goods and services must be calculated to
determine GDP. While the total output of GDP is important, the breakdown of this output
into the large structures of the economy can often be just as important. In general,
macroeconomists use a standard set of categories to breakdown an economy into its
major constituent parts; in these instances, GDP is the sum of consumer spending,
investment, government purchases, and net exports, as represented by the equation:
Y = C + I + G + NX
Because in this equation Y captures every segment of the national economy, Y represents
both GDP and the national income. This because when money changes hands, it is
expenditure for one party and income for the other, and Y, capturing all these values, thus
represents the net of the entire economy.
Four components of GDP:
- Consumer spending, C, is the sum of expenditures by households on durable
goods, nondurable goods, and services. Examples include clothing, food, and
health care.
- Investment, I, is the sum of expenditures on capital equipment, inventories, and
structures. Examples include machinery, unsold products, and housing.
- Government spending, G, is the sum of expenditures by all government bodies on
goods and services. Examples include naval ships and salaries to government
employees.

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- Net export, NX, equals the difference between spending on domestic goods by
foreigners and spending on foreign goods by domestic residents. In other words,
net export describes the difference between exports and imports.
b. FDI is a form of international investment, in which the investors bring the means
to invest abroad to directly organize the production process management and
business profits. FDI plays a huge role in economic development:
 Add to domestic capital.
 Acquisition of technology and management know-how.
 Join the global production network.
 Increase the number of jobs and trained workers.
 Bring a large budget inflow.
c. Unemployment is always a concern of society; long-term macroeconomic policies
of the government are aiming to achieve the natural rate of unemployment in the
economy. It reflects the prosperity of the country in each period of time. The some
following simple analysis shows us that unemployment occupies an important
position, is one of the objectives of government activities:
 High unemployment rate means that GDP is lower – human resource is not use
effectively, we are wasting opportunities to produce more products and services.
 Unemployment also means less production, reducing the efficiency of production
scale.
 Unemployment leads to social demand reduction. Moreover, goods and services
are less consumed, business opportunities are smaller, quality and quantity of
product reduces. Besides, high unemployment ratio can lead to the less
consumers’ demand compared with when they are employed, as the result, the
investment opportunities reduces.

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d. Relationship between gross domestic product GDP and foreign direct investment
FDI:
The relationship between the GDP and the level of FDI has always been a matter of discussion
between economists. There is a widespread belief among policymakers that foreign direct
investment (FDI) generates positive productivity effects for the host countries.
The neoclassical growth model states that FDI cause an increase in investments and their
efficiency leading to increases in growth. In the long-run, according to the endogenous
growth model, FDI promote growth, which is considered a function of technological
progress, originating from diffusion and spillover effects. The main mechanism for these
externalities is the adoption of foreign technology, which can happen via licensing
agreements, imitation, competition for resources, employee training, knowledge and
export spillovers. These benefits, together with the direct capital financing it provides,
suggest that FDI can play an important role in modernizing a national economy and
promoting economic development.
e. Relationship between gross domestic product GDP and utility U:
GDP only measures production and consumption, not the level of utility people gain from
producing and consuming. There is much economic activity (for example, replacing a
low quality product, or repairing damage from war or natural disaster) that does not
improve quality of life (compared to having a high quality product to begin with, or no
war). The result can be a very high GDP combined with low customer satisfaction.
*We collect the data and statistics of GDP, FDI and U to prove relations between GDP,
FDI and U and by using regression model in econometrics.
3. Econometric model

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Model includes three variables: dependent variable: GDP (billion dong), independent
variables: FDI( million USD) and U (%)
GDPi= β1 + β2 FDIi +β3Ui + Vi
This is multi regression model.
Many economic models express the negative relation between inflation and
unemployment (Phillip curve). Generally, high GDP leads to high inflation because of
growth objectives of government. As the result, relation between GDP and
unemployment is negative.
4. Data description
- Data collected from website: www.gso.gov.vn, GDP, FDI and U in Vietnam from 1995-
2009.
- Correlated analysis between variables: During one year, if the total capital of foreign
direct investment in Vietnam increases, there will be more capital for other projects. This
will encourage produce more; therefore GDP increases accordingly. Unemployment rate
increasing means GDP decreasing.
- Table of data: see table in the appendix
- Relation between variables: see graph in the appendix
- Description:
Mean Standard
deviation
Minimum Maximum Median
GDP(billion dong) 697572.1 441975.8 228292 1658389 535760
FDI(million USD) 4198.913 3028.292 2334.9 11500 2714.0

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U(%) 5.71 0.76 4.60 6.85 5.8800
5. Results and test
A. Results and analysis
1. Results
Model’s result from the gretl software ( Model-> Ordinary Least Squares )
Model 1: OLS, using observations 1995-2009 (T = 15)
Dependent variable: GDP
coefficient std. error t-ratio p-value
---------------------------------------------------------------------------------------------
const 1.68744e+06 624740 2.701 0.0193 **
FDI 85.6018 23.6463 3.620 0.0035 ***
U -236250 94698.9 -2.495 0.0282 **
Mean dependent var 697572.1 S.D. dependent var 441975.8
Sum squared resid 3.06e+11 S.E. of regression 159783.1
R-squared 0.887974 Adjusted R-squared 0.869303
F(2, 12) 47.55913 P-value(F) 1.98e-06

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Log-likelihood -199.3341 Akaike criterion 404.6682
Schwarz criterion 406.7923 Hannan-Quinn 404.6455
rho 0.525136 Durbin-Watson 0.766908
2. Analyze the basic content of results.
a.
Population regression model:
(PRM) GDPi =  1+  2 FDIi+  3 Ui+ Vi
Sample regression model:
(SRM) i
GDP=

1
 +

2
 FDI i+ 
ˆ 3Ui +ei ( ei is estimator of Vi)
(SRM) GDPi = 1.68744e+06 + 85.6018.FDIi – 236250.Ui + ei

1
 = 1.68744e+06 means that if FDI=0 and U=0 then GDP = 1.68744e+06 billion dong
(holding inflation rate, CPI equal to 0, population is constant)

2
 = 85.6018 means that when FDI increases 1 million USD then GDP increases 85.6018
billion dong (holding other factors constant)

ˆ 3 = – 236250 means that when U increases 1% then GDP decreases 236250 billion
dong (holding other factors constant)
b. Measure of fit
+ Intercept:

1

Test the hypothesis:





0
:
0
:
1
1
1
0


H
H

9
624740
06
68744
.
1
)
( 1
1
1 


 

e
Se
t



= 2.701
With  = 5% :
)
12
(
025
.
0
)
3
15
(
2
/
t
t 

 = 2.179
Reject Ho if: t > )
12
(
025
.
0
t
701
.
2

t
 Reject H0 ->

1
  0 -> intercept is statistical significance
+ Slope:
*

2






0
:
0
:
2
1
2
0


H
H
6463
.
23
6018
.
85
)
( 2
2
2


 




Se
t = 3.620
With  = 5% :
)
12
(
025
.
0
)
3
15
(
2
/
t
t 

 = 2.179
Reject Ho if: t > )
12
(
025
.
0
t
3.620 > 2.179
=> Reject H0  2
 ≠ 0  2
 is statistical significance
* 
ˆ 3





0
:
0
:
3
1
3
0


H
H

10
9
.
94698
236250
)
( 3
3
3 


 




Se
t = -2.495
With  = 5% :
)
12
(
025
.
0
)
3
15
(
2
/
t
t 

 = 2.179
Reject Ho if: t > )
12
(
025
.
0
t
2.495 > 2.179
=> Reject H0  3
 ≠ 0  3
 is statistical significance
+ Model
R2
= 0.887974 indicates that FDI and U explain about 88.7974 % for the variation of
dependent variable GDP.





0
:
0
:
2
1
2
0
R
H
R
H
(H0: the model is significant
H1: the model is not significant)
3
15
0.887974
1
2
0.887974
1
1
2
2







k
n
R
k
R
F = 47.5590
F0.05(2;12)= 3.89
Reject H0 if F > F0.05(2;12)
47.5590 > 3.89
=> reject H0  R2
> 0  model is significant
B. Detect and cure default of model

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1. Normality
H0: error is normal distribution
H1: error is non-normal distribution
Use gretl software: Test  Normality of residual
Frequency distribution for uhat1, obs 1-15
number of bins = 5, mean = -3.88051e-010, sd = 159783
interval midpt frequency rel. cum.
< -2.010e+005 -2.586e+005 3 20.00% 20.00% *******
-2.010e+005 - -8.598e+004 -1.435e+005 2 13.33% 33.33% ****
-8.598e+004 - 2.908e+004 -2.845e+004 0 0.00% 33.33%
2.908e+004 - 1.441e+005 8.662e+004 9 60.00% 93.33% *********************
>= 1.441e+005 2.017e+005 1 6.67% 100.00% **
Test for null hypothesis of normal distribution:
Chi-square(2) = 5.815 with p-value 0.05461

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0
1e-006
2e-006
3e-006
4e-006
5e-006
6e-006
-400000 -200000 0 200000 400000
Density
uhat1
uhat1
N(-3.8805e-010,1.5978e+005)
Test statistic for normality:
Chi-squared(2) = 5.815 pvalue = 0.05461
p-value = 0.05461 > 0.05  accept H0
=> Error is normal distribution.
H0: No multicollinearity in the model
H1: Multicollinearity in the model
Use gretl software
Test collinearity
Variance Inflation Factors
Minimum possible value = 1.0

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Values > 10.0 may indicate a collinearity problem
FDI 2.812
U 2.812
VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlation coefficient
between variable j and the other independent variables
Properties of matrix X'X:
1-norm = 3.9324782e+008
Determinant = 5.4825961e+009
Reciprocal condition number = 1.4456799e-010
VIF (FDI) = VIF (U) = 2.812 < 10
 Accept H0
 No multicollinearity in the model.
H0: Var(ui)= σ2
for all i
H1: Var(ui) = σ2
i
Use gretl software:
+ Tests  heterokesdasticity  white test

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White's test for heteroskedasticity
OLS, using observations 1995-2009 (T = 15)
Dependent variable: uhat^2
----------------------------------------------------------------------------
const -1.31163e+012 1.19540e+012 -1.097 0.3010
FDI 5.75038e+06 1.47086e+08 0.03910 0.9697
U 4.02859e+011 3.58620e+011 1.123 0.2904
sq_FDI -2697.74 2068.51 -1.304 0.2245
X2_X3 8.86852e+06 2.97815e+07 0.2978 0.7726
sq_U -3.37756e+010 2.61055e+010 -1.294 0.2279
Warning: data matrix close to singularity!
Unadjusted R-squared = 0.278199
Test statistic: TR^2 = 4.172989,
with p-value = P(Chi-square(5) > 4.172989) = 0.524788
n.R2
= 15x0.278199 = 4.172989

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χ2
α (k-1) = χ2
0.05(5) = 11.07
reject H0 if n.R2
> χ2
0.05(5)
4.172989 < 11.07
=> accept H0
+ Test  heteroskedasticity  white test ( squares only)
White's test for heteroskedasticity (squares only)
-----------------------------------------------------------------------------
const -1.55410e+012 8.34385e+011 -1.863 0.0921 *
FDI 4.84548e+07 3.11676e+07 1.555 0.1511
U 4.74663e+011 2.53071e+011 1.876 0.0902 *
sq_FDI -2807.88 1940.23 -1.447 0.1785
sq_U -3.81973e+010 2.04696e+010 -1.866 0.0916 *

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n.R2
= 15x0.271087 = 4.066311
χ2
α (k-1) = χ2
0.05(4)= 9.49
reject H0 if n.R2
> χ2
0.05(4)
4.066311 < 9.49
=> accept H0
 Var(ui) = σ2
for all i
 No hereroscedasticity in the model.
4. Autocorrelation
*Hypothesis:
H0: cov (ui;uj) = 0
H1: cov (ui;uj) ≠ 0
d =


 

11
1
2
t
e
11
1
t
2
)
1
t
e
t
(e
= 0.766908
with n=15, 5%
α 
k=3k'=3-1=2
Use Durbin-Watson statistics
L
d = 0.946 3.054
0,946
4
d
4 L 



1,543
u
d  2.457
u
d
4 

0 L
d d u 4-d u 4-dL

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Ta có d= 0.766908
)
d
(0;
d L


 Autocorrelation of order 1
- Use gretl: Test autocorrelation Lag of oder: 1
Breusch-Godfrey test for first-order autocorrelation
Dependent variable: uhat
-------------------------------------------------------------
const -529735 599418 -0.8837 0.3957
FDI 20.9536 22.8623 0.9165 0.3791
U 78884.8 90594.5 0.8707 0.4025
uhat_1 0.653404 0.302686 2.159 0.0538 *
Test statistic: LMF = 4.659936,
with p-value = P(F(1,11) > 4.65994) = 0.0538
Alternative statistic: TR^2 = 4.463558,
Ljung-Box Q' = 3.7777,
p-value=0.0346 <0.05

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 autocorrelation of order 1
*Cure:
(AR1): Ut = .Ut-1 + v
= 0.653404
Set: GDP* = GDP – 0.653404*GDP(-1)
FDI* = FDI – 0.653404*FDI(-1)
U* = U – 0.653404*U(-1)
Then run the regression model:
GDP* = 1 + 2GDP* + 3U* + v
Use gretl:
+ Add  lags of selected variables: 1
+ Add  Define new variables
 newGDP = GDP - 0.653404*GDP_1 ( newGDP = GDP* )
newFDI = FDI - 0.653404*FDI_1 (newFDI = FDI*)
newU = U – 0.653404*U_1 (newU=U*)
+ Model Ordinary Least Squares
Model 5: OLS, using observations 1996-2009 (T = 14)
Dependent variable: newGDP
----------------------------------------------------------------------------
const 494695 211153 2.343 0.0390 **
newFDI 72.9059 21.6312 3.370 0.0062 ***

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newU -162241 96558.4 -1.680 0.1211
Mean dependent var 320349.9 S.D. dependent var 202377.8
Sum squared resid 1.42e+11 S.E. of regression 113681.5
R-squared 0.733005 Adjusted R-squared 0.684460
F(2, 11) 15.09963 P-value(F) 0.000701
Log-likelihood -181.1532 Akaike criterion 368.3064
Schwarz criterion 370.2235 Hannan-Quinn 368.1289
rho 0.153244 Durbin-Watson 1.213297
New regression model:
(SRM): GDP* = 494695 + 72.9059 FDI*i - 162241U*i + ui
+ F = 15.09963 > F(2,11) = 3.98  model is statistical significance
+ β1: |t|= 2.343 > t0.05
11
=2.201  intercept is statistical significance
+ β2: |t|= 3.370 > t0.05
11
=2.201  slope β2 is statistical significance
+ β3: |t| = 1.680 < t0.05
11
=2.201  slope β3 is not statistical significance
C. Detect and cure default of new model
(SRM): GDP* = 494695 + 72.9059 FDI*i - 162241U*i + ui
1. Normality
H0: error is normal distribution
H1: error is non-normal distribution
Frequency distribution for uhat5, obs 2-15
number of bins = 5, mean = 4.15769e-012, sd = 113681

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interval midpt frequency rel. cum.
< -8.790e+004 -1.385e+005 3 21.43% 21.43% *******
-8.790e+004 - 1.323e+004 -3.733e+004 6 42.86% 64.29% ***************
1.323e+004 - 1.144e+005 6.380e+004 4 28.57% 92.86% **********
1.144e+005 - 2.155e+005 1.649e+005 0 0.00% 92.86%
>= 2.155e+005 2.661e+005 1 7.14% 100.00% **
Test for null hypothesis of normal distribution:
Chi-square(2) = 4.788 with p-value 0.09128
0
5e-007
1e-006
1.5e-006
2e-006
2.5e-006
3e-006
3.5e-006
4e-006
4.5e-006
-300000 -200000 -100000 0 100000 200000 300000
Density
uhat5
uhat5
N(4.1577e-012,1.1368e+005)
Test statistic for normality:
Chi-squared(2) = 4.788 pvalue = 0.09128
p-value = 0.09128
 Accept H0
 Error is normal distribution

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H0: No multicollinearity in the model
H1: Multicollinearity in the model
Use gretl software
Test collinearity
Variance Inflation Factors
Minimum possible value = 1.0
Values > 10.0 may indicate a collinearity problem
newFDI 1.468
newU 1.468
VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlation coefficient
between variable j and the other independent variables
Properties of matrix X'X:
1-norm = 88260386
Determinant = 7.8663739e+008
Reciprocal condition number = 2.2696973e-009
VIF(FDI*) = VIF(U*) = 1.468 <10
 Accept H0

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 No multicollinearity in the model
H0: Var(ui)= σ2
for all i
H1: Var(ui) = σ2
i
Use gretl software:
+ Tests  heterokesdasticity  white test
White's test for heteroskedasticity
--------------------------------------------------------------------------------
const -1.93698e+011 2.30951e+011 -0.8387 0.4260
newFDI 8.09341e+07 6.29831e+07 1.285 0.2347
newU 8.12109e+010 1.97847e+011 0.4105 0.6922
sq_newFDI -10103.1 3338.49 -3.026 0.0164 **
X2_X3 -3.34721e+06 3.51410e+07 -0.09525 0.9265
sq_newU -6.86930e+09 4.04854e+010 -0.1697 0.8695

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p-value = 0.157397 > 0.05
 Accept H0
 No heteroscedasticity in the new model
4. Autocorrelation
*Hypothesis:
H0: cov (ui;uj) = 0
H1: cov (ui;uj) ≠ 0
- Use gretl: Test autocorrelation Lag of oder: 1
Breusch-Godfrey test for first-order autocorrelation
Dependent variable: uhat
----------------------------------------------------------------------
const -61938.2 260534 -0.2377 0.8169
newFDI 5.61478 25.8330 0.2173 0.8323
newU 29565.2 120684 0.2450 0.8114
uhat_1 0.247459 0.561722 0.4405 0.6689

24
Test statistic: LMF = 0.194073,
with p-value = P(F(1,10) > 0.194073) = 0.669
Alternative statistic: TR^2 = 0.266529,
Ljung-Box Q' = 0.101984,
p-value = 0.606 > 0.05
 Accept H0
 No autocorrelation of oder 1
6. Conclusion and policy implication
a. Conclusion
From the above detection, we can conclude that:
- Foreign direct investment FDI and unemployment rate have influence on gross
domestic products GDP.
- Model GDP* = 494695 + 72.9059 FDI*i - 162241U*i + ui
+ FDI and U explain 73.3005% for GDP
+ We can eliminate variable U from model and cannot eliminate FDI
+ No multicollinearity in the model
+ No heteroscedasticity in the model
+ No autocorrelation of order 1
+ Error is normal distribution

25
b. Recommendation
To make model more significant, we can insert some variables. However, model is likely
to be complicated and there may appear some defaults which cause difficulty for
detection.
c. Policy implication
- Policies attracting FDI in Vietnam should:
+ Focus on development goals: Policies to attract FDI need to focus on objectives
of development in Vietnam at new economic fields, be modern, suitable to
international market demand, competitive and able to connect with business global
network. They also highly focus on selected areas and strictly reject projects which
cause permanent damage to the economy of exploiting human resources, nature
resources, protecting environment and market position. It is vital to prepare internal
resources, especially human resources and infrastructure, construction and develop
necessary facilities to support the attribution and development of areas that we want
to attract FDI.
+ Fit and support to the development of new economic zones: Policies to attract
FDI need to be appropriate and support planning to develop new economic regions in
Vietnam. This planning is also a basis for investors to choose the location to conduct
their projects and will be adjusted only when there exists new benefits that are bigger
for the whole economy.
+ Instead of making impression by the number of registered capital, we need to
measure the efficiency of attracting FDI by the real FDI investors invest in Vietnam.
If not, policies to attract FDI will be affected by unreal achievement. And we also

26
should establish foreign investment offices. These offices will consider all suggestion
of FDI enterprises to provide solutions and make policies better.
- Policies to decrease unemployment rate: In the long term, effective policies to reduce
the total level of unemployment need to encourage
+ An improvement in the employability of the labour supply - so that the
unemployed have the right skills to take up the available job opportunities. Policies
should focus on improving the occupational mobility of labour.
+ An improvement in the incentives for people to search and then accept paid
work - this may require some reforms of the tax and benefits system
+ A sustained period of economic growth so that new jobs are being created -
this requires that aggregate demand is sufficiently high for businesses to be looking to
expand their workforces
+ Improving skills and reducing occupational immobility Policies should
provide the unemployed with the skills they need to find re-employment and improve the
incentives to find work. Structural unemployment is the result of workers being
occupationally immobile - improvements in education and training will increase the
human capital of these workers, and therefore give them a better chance of taking the new
jobs that become available in the economy.
+ Reflating Aggregate Demand
The government can also use macro-economic policies to increase the level of aggregate
demand. These policies might involve lower interest rates or lower direct taxes. It might
also encourage foreign investment into the economy from foreign multinational
companies. In the diagram below we see an increase in aggregate demand leading to an
expansion of aggregate supply. Because of the increase in demand for output, the demand
for labour at each wage rate will grow - leading to an increase in total employment.

27
Not every increase in demand and production has to be met by using more labour. Each
year we expect to see a rise in labour productivity (more output per worker employed).
And, businesses may decide to increase production by making greater use of capital
inputs (machinery and technology).
+ Benefit and Tax reform
Reducing the real value of unemployment benefits might increase the incentive to take a
job - particularly if the real worth of unemployment benefits is well below the national
minimum wage rate.
Targeted measures are designed to help the long-term unemployed find re-employment
(including the Government's "Welfare to Work Schemes" - see New Deal

28
APPENDIX
1. Table of data
Year GDP FDI U GDP1
1995 228892 2556 6.08
1996 272036 2714 5.88 228892
1997 313623 3115 6.01 272036
1998 361017 2367.4 6.85 313623
1999 399942 2334.9 6.74 361017
2000 441646 2413.5 6.42 399942
2001 481295 2450.5 6.28 441646
2002 535762 2591 6.01 481295
2003 613443 2650 5.78 535762
2004 715307 2852.5 5.60 613443
2005 839211 3308.8 5.31 715307
2006 974266 4100.1 4.82 839211
2007 1143715 8030 4.64 974266
2008 1485038 11500 4.65 1143715
2009 1658389 10000 4.60 1485038

29
2. Graph 1: Relation between GDP and FDI
2000
3000
4000
5000
6000
7000
8000
9000
10000
11000
12000
1996 1998 2000 2002 2004 2006 2008
200000
400000
600000
800000
1e+006
1.2e+006
1.4e+006
1.6e+006
1.8e+006
GDP (right)
FDI (left)
3. Graph 2: Relation between GDP and U
4.5
5
5.5
6
6.5
7
1996 1998 2000 2002 2004 2006 2008
200000
400000
600000
800000
1e+006
1.2e+006
1.4e+006
1.6e+006
1.8e+006
GDP (right)
U (left)

30
4. Graph 3: Relation between FDI and U
4.5
5
5.5
6
6.5
7
1996 1998 2000 2002 2004 2006 2008
2000
3000
4000
5000
6000
7000
8000
9000
10000
11000
12000
FDI (right)
U (left)

31
REFERENCES
1. Website: www.gso.gov.vn
2. Econometrics course book – James H.Stock and Mark W.Watson

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