tiểu luận kinh tế lượng THE INFLUENCE OF FACTORS ON UNITED KINGDOMS GDP FROM 1965 TO 2010

Correlation between independent variables and dependent variable 10 3.2.2.. Besides theimpact of the spending from households and exports of goods on the increase, thegovernment debt is

FOREIGN TRADE UNIVERSITY

FACULTY OF INTERNATIONAL ECONOMY

REPORT ECONOMETRICS

THE INFLUENCE OF FACTORS

ON UNITED KINGDOM'S GDP FROM 1965 TO 2010

Instructor: Dr Chu Thi Mai Phuong

TABLE OF CONTENTS

I INTRODUCTION 3

II LECTURE REVIEW Error! Bookmark not defined III METHODOLOGY 7

1 Our model 7

2 Data Error! Bookmark not defined 3 Describe Variables 9

3.1 Summary Statistic 9

3.2 Correlation matrix 10

3.2.1 Correlation between independent variables and dependent variable 10 3.2.2 Correlation between independent variables 10

4 Regression run 11

IV.TESTING 12

1 Testing hypothesis: 12

1.1 Testing an individual regression coefficient 12

1.2 Testing the overall significance 13

2 Testing the model’s problems: 13

2.1 Multicollinearity 13

2.2 Heteroskedasticity 16

2.3 Autocorrelation 18

2.4 Normality of residual Test 19

3 Summary table: 23

V CONCLUSION 24

VI REFERENCES Error! Bookmark not defined.5

I INTRODUCTION:

One of the basic indicators reflecting economic growth in economic scale, level of economic development per capita, economic structure and changes in price level of a country is GDP Gross Domestic Product (GDP) is one of the determinants of country’s economic growth It represents the economic health of a country, presents a sum of a country's production which consists of all purchases of goods and services produced by a country and services used by individuals, firms, foreigners and the governing bodies.

GDP is used as an indicator for most governments and economic makers for planning and policy formulation GDP helps the investors to managetheir portfolios by providing them with guidance about the state of the economy.Calculation of GDP provides with the general health of the economy

decision-With all its importance to economic growth, studying on GDP is vital for all nations.Any nation wants to maintain a growing economy along with monetary stabilityand jobs for the population; GDP is one of the concrete signals for governmentefforts Therefore, studying the relationship between GDP and the importantfactors that affect GDP such as Family Expenditure, Exports, and GovernmentDebt will help government look for trends in GDP growth and enable to changeits policies to achieve set goals to promote economic growth

United Kingdom has the fifth largest economy in the world at the exchange rate onthe market and the 6th in the world by purchasing power parity We can see thepositive results today, the way each household's spending and export of thecountry plays a very important role for the economy of this union Besides theimpact of the spending from households and exports of goods on the increase, thegovernment debt is also a critical factor impacting GDP of the United Kingdom.Studying the theories and indicators of the relationship between household spending, exports, public debt and economic growth helps us understand the impacts of these factors on GDP In addition, we can imagine the characteristics and development trends to control and propose orientations and solutions to attract investment capital, use them most effectively, reduce public debt and integrate extensively and develop sustainably not only in United Kingdom but

also our country For that reason, we choose the topic “Regression

model of the influence of factors on United Kingdom's GDP from 1965 to 2010”.

II LITERATURE OVERVIEW

Many practical studies are carried out to investigate factors affecting GDP but you canfind no one studied about factors affecting GDP of United Kingdom which includesHousehold Expenditure, Export and Government Debt The results of those seem to bedifferent to kind of analysis and factors undertaken For instance, some researchersstudied on literacy rate, natural resources, human capital, physical capital, standard ofliving while some others determined by government expenditure, consumption, … andrevealed that there was a significant difference in how much that factors affect GDP

Table 1: A summary of previous study on factors impacting GDP in general

Author/Year Methodolog Variable/Factor Objectives

y

Alex Reuben Cross- Consumption and Export To analyze factors affecting

in Developing Countries: TheCase of Tanzania

Dhiraj Jain , Cross- FDI, Net FII equity, Net FII To investigate the impact of

K Sanal Nair tabulation debt, Import and Export various macro economic factors

on GDP componentsand Vaishali

-explain how changes in aparticular factor will influencethe GDP of a country

-analyze economic data andidentify to which type ofresource the data refers

Mertha Endah Cross- populations, original local Analyzing Factors AffectingErvina (2018) tabulation government revenue, GRDP in Indonesia

government expenditure,

4

domestic investment,andforeign investment.

Besides factors mentioned in Table 1, there are 3 main other factors whichcaused controversy a lot They are Inflation, Foreign Direct Investment (FDI)and Female Labor Forces

GDP growth indeed has many controversial issues regarding the explanatory variables such as inflation According to Barro (1995), inflation is the determinant of economic growth, which has been further explained that if there is a high inflation, then the level of investment will be reduced Thus, the reduction in investment adversely affects economic growth Besides that, Mundell (1963) and Tobin (1965), have found the empirical evidence that support the findings that the inflation has huge impact on economic growth However, other researchers for instance Gultekin (1983), mentioned that depending on the rate of return will affect the relationship between the inflation and GDP If the rate of return is decreased, then economic growth is definitely having a negative relationship with inflation Furthermore, the research was further investigated by Fischer (1993) Moreover, according

to Sidrauski (1967), the inflation has insignificant impact on economic growth This study was then supported by Sarel (1996).

Secondly, the explanatory variable that affects GDP is FDI FDI has always been the major source to finance the economic activities of a country There are some studies on the relationship between FDI and economic growth Based on the previous research, Herzer et

al (2008), have mentioned that there is a positive relationship between FDI and economic growth Furthermore, economic instability will probably have a negative effect on the FDI such as inflation and unstable exchange rate Wai-Mun et al (2008) Besides that, the study about the relationship was further explained by Yol and Teng-Teng (2009) Their investigation shows that it is a negative relationship between Foreign Direct Investment and economic growth However, Lim (2001); Duasa (2007); Karim and Yusop (2009); Kogid (2010), found that there is no causal relation between FDI and GDP growth.

Finally, the explanatory variable that affects GDP growth is female labor forceparticipation Based on empirical studies it showed that female labor force participationrate has proved a significant impact on GDP growth Through the female labor forceparticipation rate, the average household income has improved thus it did increase theGDP growth Past studies conducted by Nor (1998) have shown that highly educated

women tend to get better jobs, earn more and are less prone to be unemployed.from research done by Bryant et al (2004), concluded that by increasing thelabor force participation of women, it increases the rate of GDP This is primarydue to more equal human capital investment.

From this section, it can be inferred that there is no research on Factors affecting GDP

of United Kingdom Therefore, we will take responsibility to make clear this topic

• Government debt: debt (% GDP)

➢ , , , β4 are the coefficient of the independent variables to be estimated and Ui is

the random error term or disturbance error term that represent the missingvariable or factors that are not mentioned in the model

2 Data:

Our model uses data for each variable (GDP, Household Consumption, Export andGovernment debt of the UK from 1965 to 2010) on the website

https://data.worldbank.org/ and then we summarized them as in the following table:

Table 1: Economical numbers of the UK from 1965 to 2010

(billion GBP) (billion GBP) (billion GBP) (% GDP)

Table 2: Summary Statistic of variables

3.2 Correlation matrix:

Table 3: Correlation coefficients, using the observations 1

- 46 (5% critical value (two-tailed) = 0,2907 for n = 46)

(Source: Gretl, Self-aggregation)

3.2.1 Correlation between independent variables and dependent variable:

- According to theory, household consumption and GDP have positive relation.Based on the table, r (GDP, Con) = 0.9833, which means they are positively correlated.Hence it is suitable with the theory and the correlation is 98.33% which is very high

- Based on theory, when export increases, GDP increases r (GDP, Ex) =0.9676 therefore they are positively correlated and the correlation is high which is96.76% So it is suitable with the theory

- When the government has more debt, it causes GDP to decrease From the

table, r (GDP, debt) = -0.6794, which means they are inverse correlated in 67.94%.

Therefore it is suitable with the theory

→ In general, correlations between independent variables and dependent variable are quite high

3.2.2 Correlation among independent variables:

• r (ex, con) = 0.9870 Thus, variable ex and variable con are positively correlated

• r ( debt, con) = -0.5556 Thus, variable debt and variable con are inverse correlated

• r ( debt, ex) = -0.5048 Thus, variable ex and variable ex are inverse correlated

→ The correlation between ex and con is 0,9870 > 0,8 therefore we predict there

happens multicollinearity

Con, ex, debt all have statistically significant effects on GDP at the 5%

significant level (as all p-values are smaller than 0.05) In particular, thoseeffects can be specified by the regression coefficients as follows:

When all the independent variables are zero, the expected valueof UK GDP is 638169 (billions of GBP)

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• The coefficient of determination R squared = 0.993785: all independent

variables (con, ex, debt) jointly explain 99.37% of the variation in the

dependent variable (GDP); other factors that are not mentioned explain the remaining 0.63% of the variation in the GDP

IV Testing:

1 Testing hypothesis:

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 variable of Household Consumption (con):

Given that the hypothesis is:

H0: 1=0

We see: P-value of con is < 0.0001 < 0.05 → Reject H0 → The

coefficient 1 is statistically significant

➢ Testing the variable of Export (ex):

Given that the hypothesis is:

H0: 2 =0

We see: P-value of ex is < 0.0001 < 0.05 → Reject H0 → The

coefficient 2 is statistically significant

➢ Testing the variable of Government Debt:

Given that the hypothesis is:

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H 0: 3= 0

H :1 3 0

We see: P-value of debt is < 0.0001 < 0.05 → Reject H0 → The

coefficient3 is statistically significant

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:

We have: P-value (F) = 2.41e - 46 < α = 0.05 → Reject H0 → All parameters are not simultaneously equal to zero→ At least one variable has an effect on dependent one.

→ The model is statistically fitted

2 Testing the model’s problems:

2.1 Multicollinearity:

Multicollinearity is the high degree of correlation amongst the explanatory

variables, which may make it difficult to separate out the effects of the individualregressors, standard errors may be overestimated and t-value depressed Theproblem of Multicollinearity can be detected by examining the correlation matrix ofregressors and carry out auxiliary regressions amongst them In Gretl, the

VIFcommand is used, which stand for variance inflation factor.

• Given that the hypothesis is:

Ho: no multicollinearity

H1 : Multicollinearity exists

Variance Inflation Factors

Minimum possible value = 1.0

Values > 10.0 may indicate a collinearity problem

con 46.678

debt 1.618

ex 43.305

VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlation

coefficient between variable j and the other independent variables

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➢ The value of VIF here is higher than 10, indicating that Multicollinearity can be

a problem for this set of data

→ We’ll make another regression model with dependent variable ex and independent variable con and debt to determine whether the multicollinearity exists or not.

• The second regression model:

Rule: If R – squared of the second regression model > 0.9 or > R – squared of the first regression model then multicollinearity may be present.

The regression with dependent variable ex and independent variable con and debt:

Model 2: OLS, using observations 1965-2010 (T = 46)

Mean dependent var 31670.57 S.D dependent var 33617.35

Sum squared resid 1.17e+09 S.E of regression 5225.977

➢ R-squared = 0,9769 > 0,9 and P-value(F) is quiet small thus we can conclude

that multicollinearity exists.

2.1.1 Correcting multicollinearity:

Removing con or ex from the model:

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➢ The model after removing variable con:

Model 4: OLS, using observations 1965-2010 (T = 46)

- Equation of regression:

GDP = 638169 + 0.5966con – 2039.7debt

R2without con = 0,985104

➢ The model after removing variable ex:

Model 3: OLS, using observations 1965-2010 (T = 46)

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Equation of regression:

GDP = 638169 + 0.5966con – 2039.7debt

➢ Comparing 2 model we have: R2without con < R2without ex

→ Therefore, after removing variable ex, we will have better result.

It can be remedied by specifying the model – look for other missing variables

• Given that the hypothesis is: Ho: no heteroskedasticity

H1 : Heteroskedasticity exists

• White’s test for the first model:

➢

The first regression model:

White's test for heteroskedasticityOLS, using observations 1965-2010 (T = 46)Dependent variable: uhat^2

coefficient std error t-ratio p-value -

const 2.48863e+09 1.50721e+09 1.651 0.1074

We can see P (Chi-square (9) > 19.187334) = 0.023646 < 0,05

Thus at the 5% significance level, there is enough evidence to reject H 0

→ We can conclude that this set of data meets the problem ofHeteroskedasticity

• White’s test for the second regression model (without variable ex):

White's test for heteroskedasticity

OLS, using observations 1965-2010 (T = 46)

Dependent variable: uhat^2

coefficient std error t-ratio p-value -

with p-value = P(Chi-square(5) > 17.073786) = 0.004362

We can see P – value = 0,004362 < 0,05 then we conclude that this set of data meets the problem of Heteroskedasticity