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

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 Class: Anh - KDQT – K57 Đỗ Thu Trang Nguyễn Thị Thúy Quỳnh 1815520229 1815520217 Lê Thị Thu Hà 1815520163 Hanoi, October 2019 TABLE OF CONTENTS I INTRODUCTION II LECTURE REVIEW Error! Bookmark not defined III METHODOLOGY Our model Data Error! Bookmark not defined Describe Variables 3.1 Summary Statistic 3.2 Correlation matrix 10 3.2.1 Correlation between independent variables and dependent variable 10 3.2.2 Correlation between independent variables 10 Regression run 11 IV.TESTING 12 Testing hypothesis: 12 1.1 Testing an individual regression coefficient 12 1.2 Testing the overall significance 13 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 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 decisionmakers for planning and policy formulation GDP helps the investors to manage their portfolios by providing them with guidance about the state of the economy Calculation of GDP provides with the general health of the economy 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 stability and jobs for the population; GDP is one of the concrete signals for government efforts Therefore, studying the relationship between GDP and the important factors that affect GDP such as Family Expenditure, Exports, and Government Debt will help government look for trends in GDP growth and enable to change its policies to achieve set goals to promote economic growth United Kingdom has the fifth largest economy in the world at the exchange rate on the market and the 6th in the world by purchasing power parity We can see the positive results today, the way each household's spending and export of the country plays a very important role for the economy of this union Besides the impact of the spending from households and exports of goods on the increase, the government 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 can find no one studied about factors affecting GDP of United Kingdom which includes Household Expenditure, Export and Government Debt The results of those seem to be different to kind of analysis and factors undertaken For instance, some researchers studied on literacy rate, natural resources, human capital, physical capital, standard of living while some others determined by government expenditure, consumption, … and revealed 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 y Variable/Factor Objectives Alex Reuben CrossKira (2013) tabulation Consumption and Export To analyze factors affecting Gross Domestic Product (GDP) in Developing Countries: The Case of Tanzania Dhiraj Jain , CrossK Sanal Nair tabulation and Vaishali Jain (2015) FDI, Net FII equity, Net FII To investigate the impact of debt, Import and Export various macro economic factors on GDP components Sherilyn CrossNarker (2015) tabulation Natural Resources ,Human -define key terms such as Capital, Physical Capital, entrepreneurship, GDP per capita, gross domestic product, Entrepreneurship human capital, literacy rate, natural resources, physical capital, standard of living -explain how changes in a particular factor will influence the GDP of a country -analyze economic data and identify to which type of resource the data refers Mertha Endah CrossErvina (2018) tabulation populations, original local Analyzing Factors Affecting government revenue, GRDP in Indonesia government expenditure, domestic investment, and foreign investment Besides factors mentioned in Table 1, there are main other factors which caused 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 force participation Based on empirical studies it showed that female labor force participation rate has proved a significant impact on GDP growth Through the female labor force participation rate, the average household income has improved thus it did increase the GDP 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 the labor force participation of women, it increases the rate of GDP This is primary due 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 III METHODOLOGY Our model: Our model is based on the simple model raised before about the Gross Domestic Product using expenditure approach: GDP is the sum of the final uses of goods and services (all uses except intermediate consumption) measured in purchasers' prices: GDP=Y=C+I+G+(X–M) And after consulting other researches about effects of household consuming, export and government debt on GDP, in the narrow range of our model, we propose the following model with these variables: GDPi = ➢ ➢ coni + exi + debti + Ui Dependent Variable: • Gross Domestic Product of the United Kingdom through 1965 to 2010: GDP (billion GBP) Independent Variables: • • • ➢ 0+ Household consumption: (billion GBP) Export: ex (billion GBP) 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 missing variable or factors that are not mentioned in the model Data: Our model uses data for each variable (GDP, Household Consumption, Export and Government 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 Year GDP (billion GBP) (billion GBP) ex (billion GBP) debt (% GDP) 1965 394292 16535 1333 117.9 1966 403406 17383 1399 113.8 1967 407616 18396 1435 110.5 1968 425077 19511 1457 108.6 1969 448359 20812 1551 101.1 1970 458368 22080 1613 94.6 1971 467207 23315 1800 91.9 1972 478737 24499 2041 89.1 1973 498789 26364 2425 88.5 1974 509158 27955 2691 82.8 1975 520568 30442 3225 73.2 1976 531049 34127 3727 65.6 1977 550002 38639 4057 62 1978 589158 44204 5083 54.6 1979 581111 50958 6512 51.6 1980 577489 62656 7668 46.7 1981 592659 72804 10041 48.9 1982 606780 83212 11681 50.5 1983 626382 96023 12615 51.6 1984 643043 114030 14613 48.7 1985 629559 132128 15795 46.2 1986 620332 146508 17346 50 1987 632052 160266 18260 48.2 1988 654267 175908 20409 47.3 1989 670995 188586 22897 49.3 1990 694661 205737 25727 49.5 1991 721977 227812 26709 50.3 1992 754678 250274 29122 49.6 1993 792176 282777 29093 47.2 1994 809214 310168 31542 42.8 1995 814956 336265 34270 38.4 1996 803892 358107 34723 38 1997 805699 377780 37617 39.5 1998 824085 399875 43605 43.5 1999 859566 419825 48072 50.7 2000 884748 441085 53570 54.6 2001 909102 472711 61851 57.2 2002 936717 501290 65555 57.9 2003 968040 534153 69228 54.6 2004 997295 567994 76525 52 2005 1035295 600826 81883 50.3 2006 1059648 632496 87773 48.5 2007 1081469 664562 94012 47.4 2008 1110296 697160 102357 46.9 2009 1146523 732531 112518 47.2 2010 1167792 760777 119420 41.8 Describe variables: 3.1 Summary Statistic: Table 2: Summary Statistic of variables Variable Maximum Minimum Average GDP 1167792 394292 710745.3 Con 60777 16535 248294.5 Ex 119420 1333 31670.6 Debt 117.9 38 60.893 (Source: Gretl, Self-aggregation) 3.2 Correlation matrix: Table 3: Correlation coefficients, using the observations - 46 (5% critical value (two-tailed) = 0,2907 for n = 46) GDP Con Ex debt 1.0000 0.9833 0.9676 -0.6794 GDP 1.0000 0.9870 -0.5556 1.0000 -0.5048 ex 1.0000 debt (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 is 96.76% 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 are positively correlated r ( debt, con) = -0.5556 Thus, variable debt and variable are inverse correlated r ( debt, ex) = -0.5048 Thus, variable ex and variable ex are inverse correlated → The correlation between ex and is 0,9870 > 0,8 therefore we predict there happens multicollinearity 10 = 0.5966 When the number of household consumption increases by one, the expected value of UK GDP increases by 0.5966 (billions of GBP) • • • =1.5363 When t h e n u m b e r o f e x p o r t increases by one, the expected value of UK GDP increases by 1.5363 (billions of GBP) = - 2039.7 : When the percentage of government debt decreases by one, the expected value of UK GDP increases by 2039.7 (billions of GBP) • 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: 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: H1: 1=0 We see: P-value of is < 0.0001 < 0.05 → Reject H0 → The ➢ coefficient is statistically significant Testing the variable of Export (ex): Given that the hypothesis is: H0: H1: 2 =0 We see: P-value of ex is < 0.0001 < 0.05 → Reject H0 → The ➢ coefficient is statistically significant Testing the variable of Government Debt: Given that the hypothesis is: 12 : H 1: H =0 We see: P-value of debt is < 0.0001 < 0.05 → Reject H0 → The coefficient 1.2 ➢ is statistically significant 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 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 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 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 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 13 ➢ 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 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 and debt: Model 2: OLS, using observations 1965-2010 (T = 46) Dependent variable: ex Const Con Debt Coefficient Std Error −10429.3 3300.88 0.146319 0.00399025 94.7503 41.9048 Mean dependent var Sum squared resid R-squared F(2, 43) Log-likelihood Schwarz criterion Rho ➢ 31670.57 1.17e+09 0.976908 909.5530 −457.5443 926.5745 0.947026 t-ratio −3.160 36.67 2.261 p-value 0.0029 0,9 and P-value(F) is quiet small thus we can conclude that multicollinearity exists 2.1.1 Correcting multicollinearity: Removing or ex from the model: 14 ➢ The model after removing variable con: Model 4: OLS, using observations 1965-2010 (T = 46) Dependent variable: GDP Coefficient Std Error 690532 16176.6 −2521.91 212.206 5.48714 0.141139 Const Debt Ex Mean dependent var Sum squared resid R-squared F(2, 43) Log-likelihood Schwarz criterion rho - 710745.3 3.25e+10 0.985104 1421.880 −533.8889 1079.264 0.915210 t-ratio 42.69 −11.88 38.88 p-value 27.5749) = 1.51e-007 We can see p-value = P(F(1,42) > 55.1527) = 3.59e-009 < 0,05 Thus at the 5% significance level, there is enough evidence to reject H → We can conclude that this set of data also meets the problem of Autocorrelation 2.3.1 Correcting Autocorrelation: To fix the problem, robust standard errors are used to relax the assumption that errors are both independent and identically distributed: Model 4: OLS, using observations 1965-2010 (T = 46) Dependent variable: GDP HAC standard errors, bandwidth (Bartlett kernel) Coefficient Std Error t-ratio p-value const 622147 17308.3 35.95