Tiểu luận tiếng anh thương mại đại học ngoại thương môn Kinh tế vĩ mô.doc

Tiểu luận tiếng anh thương mại đại học ngoại thương môn Kinh tế vĩ mô

5 Results and test

A Results and analysis

1 Results

2 Analyze some basic content of results

B Detect and cure default model

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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 fromthis researching will be helpful for other economics subjects such asMacroeconomics, International Economics….and our future jobs as well

 Secondly, our country started to innovate in 1986; foreign investment law in VietNam was promulgated on 29th December, 1987 to make a legal basis for theinvestment in Viet Nam from foreign investors The fact is that since Viet Namopened to integrate, foreign investment has become a very important source ofcapital for Viet Nam economy in industrialization and modernization Being amember of World Trade Organization (WTO), Viet Nam has many chances togain more FDI However, now the issue is that how to use FDI effectively, makeFDI be an important factor to develop the economy

The study of the effects of foreign direct investment and unemployment on economicgrowth 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 andtrends to develop, we can make the directions and solutions to attract FDI and use FDI inthe most effective way; besides, try to bring back unemployment ratio to natureunemployment standard in order to help GDP grow up

That is all the reasons why we choose to research this topic!

Y = C + I + G + NX

Because in this equation Y captures every segment of the national economy, Y representsboth GDP and the national income This because when money changes hands, it isexpenditure for one party and income for the other, and Y, capturing all these values, thusrepresents the net of the entire economy

Four components of GDP:

- Consumer spending, C, is the sum of expenditures by households on durablegoods, nondurable goods, and services Examples include clothing, food, andhealth care

- Investment, I, is the sum of expenditures on capital equipment, inventories, andstructures Examples include machinery, unsold products, and housing

- Government spending, G, is the sum of expenditures by all government bodies ongoods and services Examples include naval ships and salaries to governmentemployees

- Net export, NX, equals the difference between spending on domestic goods byforeigners 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 andbusiness 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 theeconomy It reflects the prosperity of the country in each period of time The somefollowing simple analysis shows us that unemployment occupies an importantposition, is one of the objectives of government activities:

 High unemployment rate means that GDP is lower – human resource is not useeffectively, we are wasting opportunities to produce more products and services

 Unemployment also means less production, reducing the efficiency of productionscale

 Unemployment leads to social demand reduction Moreover, goods and servicesare less consumed, business opportunities are smaller, quality and quantity ofproduct reduces Besides, high unemployment ratio can lead to the lessconsumers’ demand compared with when they are employed, as the result, theinvestment opportunities reduces

d Relationship between gross domestic product GDP and foreign direct investmentFDI:

The relationship between the GDP and the level of FDI has always been a matter of discussionbetween economists There is a widespread belief among policymakers that foreign directinvestment (FDI) generates positive productivity effects for the host countries.The neoclassical growth model states that FDI cause an increase in investments and theirefficiency leading to increases in growth In the long-run, according to the endogenousgrowth model, FDI promote growth, which is considered a function of technologicalprogress, originating from diffusion and spillover effects The main mechanism for theseexternalities is the adoption of foreign technology, which can happen via licensingagreements, imitation, competition for resources, employee training, knowledge andexport spillovers These benefits, together with the direct capital financing it provides,suggest that FDI can play an important role in modernizing a national economy andpromoting 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 fromproducing and consuming There is much economic activity (for example, replacing alow quality product, or repairing damage from war or natural disaster) that does notimprove quality of life (compared to having a high quality product to begin with, or nowar) 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

Model includes three variables: dependent variable: GDP (billion dong), independentvariables: 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 andunemployment (Phillip curve) Generally, high GDP leads to high inflation because ofgrowth objectives of government As the result, relation between GDP andunemployment is negative

- Table of data: see table in the appendix

- Relation between variables: see graph in the appendix

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

Log-likelihood -199.3341 Akaike criterion 404.6682

Schwarz criterion 406.7923 Hannan-Quinn 404.6455

0 : 1 1

1 0





H H

06 68744 1 ) ( 1

0 : 2 1

2 0





H H

6463 23

6018 85 ) ( 2

2 2

0 : 3 1

3 0





H H

9 94698

236250 )

0:

2 1

2 0

R H

R H

(H0: the model is significant

H1: the model is not significant)

3 15

0.887974

0.887974 1

1 2 2

R k

B Detect and cure default of model

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

Chi-squared(2) = 5.815 pvalue = 0.05461

p-value = 0.05461 > 0.05  accept H0

← => Error is normal distribution

2 Multicollinearity

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

Reciprocal condition number = 1.4456799e-010

VIF (FDI) = VIF (U) = 2.812 < 10

Use gretl software:

+ Tests  heterokesdasticity  white test

White's test for heteroskedasticity

OLS, using observations 1995-2009 (T = 15)

Dependent variable: uhat^2

coefficient std error t-ratio p-value - 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

=> accept H0

+ Test  heteroskedasticity  white test ( squares only)

White's test for heteroskedasticity (squares only)

OLS, using observations 1995-2009 (T = 15)

Dependent variable: uhat^2

coefficient std error t-ratio p-value - 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 * Warning: data matrix close to singularity!

Unadjusted R-squared = 0.271087

Test statistic: TR^2 = 4.066311,

with p-value = P(Chi-square(4) > 4.066311) = 0.397106

=> accept H0

 Var(ui) = σ2 for all i

 No hereroscedasticity in the model

11

1

t

2)1te

Ta có d= 0.766908

) d (0;



 Autocorrelation of order 1

- Use gretl: Test autocorrelation Lag of oder: 1

Breusch-Godfrey test for first-order autocorrelation OLS, using observations 1995-2009 (T = 15)

Dependent variable: uhat

coefficient std error t-ratio p-value -

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 * Unadjusted R-squared = 0.297571

+ 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

coefficient std error t-ratio p-value

const 494695 211153 2.343 0.0390 **

newFDI 72.9059 21.6312 3.370 0.0062 ***

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 > tt|t|= 2.343 > t= 2.343 > t0.0511=2.201  intercept is statistical significance

+ β2: |t|= 2.343 > tt|t|= 2.343 > t= 3.370 > t0.0511=2.201  slope β2 is statistical significance

+ β3: |t|= 2.343 > tt|t|= 2.343 > t = 1.680 < t0.0511=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

interval midpt frequency rel cum.

Test for null hypothesis of normal distribution:

Chi-square(2) = 4.788 with p-value 0.09128

0 5e-007

Chi-squared(2) = 4.788 pvalue = 0.09128

p-value = 0.09128

Accept H

 Error is normal distribution

2 Multicollinearity

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

Use gretl software:

+ Tests  heterokesdasticity  white test

White's test for heteroskedasticity

OLS, using observations 1996-2009 (T = 14)

Dependent variable: uhat^2

coefficient std error t-ratio p-value - 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

Warning: data matrix close to singularity!

Unadjusted R-squared = 0.569928

- Use gretl: Test autocorrelation Lag of oder: 1

Breusch-Godfrey test for first-order autocorrelation OLS, using observations 1996-2009 (T = 14)

Dependent variable: uhat

coefficient std error t-ratio p-value - 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

Unadjusted R-squared = 0.019038

From the above detection, we can conclude that:

- Foreign direct investment FDI and unemployment rate have influence on grossdomestic 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