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

TABLE OF CONTENT

1 Introduction2 Methodology

3 Econometric model4 Data description5 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 Multicollinearity3 Heteroscedasticity4 Autocorrelation

C Detect and cure default new model

1 Normality

2 Multicollinearity3 Heteroscedasticity4 Autocorrelation

6.Conclusion and policy implication

a Conclusionb Recommendationc Policy implicationAPPENDIX

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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 Uon 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 meansto 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 policiesof 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 production

 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- Description:

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 441975.8Sum squared resid 3.06e+11 S.E of regression 159783.1R-squared 0.887974 Adjusted R-squared 0.869303F(2, 12) 47.55913 P-value(F) 1.98e-06Log-likelihood -199.3341 Akaike criterion 404.6682

Schwarz criterion 406.7923 Hannan-Quinn 404.6455rho 0.525136 Durbin-Watson 0.7669082 Analyze the basic content of results.

 FDI i+ ˆ3Ui +ei ( ei is estimator of Vi) (SRM) GDPi = 1.68744e+06 + 85.6018.FDIi – 236250.Ui + ei

 = 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)

 = 85.6018 means that when FDI increases 1 million USD then GDP increases 85.6018billion dong (holding other factors constant)

ˆ 3 = – 236250 means that when U increases 1% then GDP decreases 236250 billiondong (holding other factors constant)

b Measure of fit

+ Intercept: 1

Test the hypothesis: 

HH

6247400668744.1)( 1

= 2.701

With  = 5% : 0(12.025))

t   = 2.179

Reject Ho if: t > t0(12.025) t 2.701

 Reject H0 -> 1

 0 -> intercept is statistical significance

+ Slope:

* 2

Test the hypothesis: 

6018.85)( 2

With  = 5% : 0(12.025))

t   = 2.179

Reject Ho if: t > t0(12.025)3.620 > 2.179

=> Reject H0  2≠ 0 2 is statistical significance

Test the hypothesis: 

HH

( 33

 

With  = 5% : (12)025.0)315(

t   = 2.179

Reject Ho if: t > t0(12.025)2.495 > 2.179

=> Reject H0  3≠ 0 3 is statistical significance

(H0: the model is significantH1: the model is not significant)

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

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

0 1e-006 2e-006 3e-006 4e-006 5e-006 6e-006

Test collinearity

Variance Inflation Factors

Minimum possible value = 1.0

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 coefficientbetween 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

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

α (k-1) = χ2

0.05(5) = 11.07reject 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)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 = 0.271087Test statistic: TR^2 = 4.066311,

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

n.R2 = 15x0.271087 = 4.066311χ2

α (k-1) = χ2

0.05(4)= 9.49reject 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) = 0H1: cov (ui;uj) ≠ 0

d =

= 0.766908

with n=15, α 5%

k=3  k'=3-1=2Use Durbin-Watson statistics

d = 0.946 4dL 40,9463.054

d u 1,543 4 du 2.457

0 dL du 4-du 4-dL

Ta có d= 0.766908

 Autocorrelation of order 1

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

Breusch-Godfrey test for first-order autocorrelationOLS, 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

Test statistic: LMF = 4.659936,

with p-value = P(F(1,11) > 4.65994) = 0.0538Alternative statistic: TR^2 = 4.463558,

with p-value = P(Chi-square(1) > 4.46356) = 0.0346

Ljung-Box Q' = 3.7777,

with p-value = P(Chi-square(1) > 3.7777) = 0.0519

p-value=0.0346 <0.05

 autocorrelation of order 1

(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* + vUse 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

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 202377.8Sum squared resid 1.42e+11 S.E of regression 113681.5R-squared 0.733005 Adjusted R-squared 0.684460F(2, 11) 15.09963 P-value(F) 0.000701Log-likelihood -181.1532 Akaike criterion 368.3064Schwarz criterion 370.2235 Hannan-Quinn 368.1289rho 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 distributionH1: error is non-normal distribution

Frequency distribution for uhat5, obs 2-15

number of bins = 5, mean = 4.15769e-012, sd = 113681