After more than 20 years of revolution, first of all, is "economic thinking", shifting from acentrally planned economy to a socialist-oriented market economy, promoting industrialization
NATURE AND BACKGROUND OF THE STUDY
Introduction
The global economy is experiencing major transformations as countries increasingly integrate, fostering economic growth through the exchange and trade of goods across regions and continents This economic development influences both the prosperity and political dynamics of nations, with Vietnam being no exception.
Over the past 20 years, Vietnam has undergone a significant transformation from a centrally planned economy to a socialist-oriented market economy, leading to remarkable achievements in industrialization and modernization Initially characterized by a small-scale agricultural economy with a GDP of only 14 billion USD and a per capita income of approximately 250 USD, Vietnam has successfully lifted itself out of poverty By 2019, the country established official relations with 189 out of 193 United Nations member states and developed economic, trade, and investment ties with over 224 countries and territories worldwide.
Vietnam has established 16 strategic partners and 11 comprehensive strategic partners, engaging in over 500 bilateral and multilateral agreements, including 16 Free Trade Agreements (FTAs) Recognized as a market economy by 71 countries, Vietnam has embraced globalization by joining ASEAN, APEC, the WTO, and various international organizations The country actively contributes to the development of multilateral institutions, positioning itself as a reliable and responsible member of the global community This deep and broad integration into the global economy spans various sectors, including politics, defense, security, and culture, marking a significant step towards a promising economic future.
Economic growth is evidenced by a consistent and rising GDP growth rate over time, leading to significant achievements within the economy As income stability and living standards improve, the country experiences greater development Consequently, economic growth remains a key focus in economic research, serving as a crucial indicator of the evolving national economy.
Statement of the problem
Economic development significantly impacts life, culture, and political activities globally, with a particular emphasis on Vietnam Analyzing a country's GDP reveals that its components are key factors driving the economy.
To evaluate a country's economy, economists evaluate the gross domestic product GDP.
Background of the statement
Gross Domestic Product (GDP) is a key economic indicator that measures the overall growth rate of a country's economy and evaluates its development level.
Gross Domestic Product (GDP) measures the total value of all goods and services produced within a territory over a specific time frame, typically ranging from three months to one year, depending on the sector It serves as a key indicator of the economic performance of a country, encompassing the contributions of both domestic and foreign companies operating in Vietnam.
Constructed in a three-step sequence, the GDP is calibrated to four sub-indices, reflecting economic factors that influence the development of the economy of Vietnam:
The population refers to a group of individuals residing in a specific geographical area, serving as a crucial resource for socio-economic development It is typically assessed through census data and represented visually in population charts.
Population serves as both a driving force for production and a key consumer force The size of the population influences the workforce, enabling a country to develop its economic sectors comprehensively Additionally, a well-skilled labor force enhances productivity, thereby fostering overall economic and social development.
Total personal investment encompasses business spending on equipment and facilities, as well as household investments in new homes Additionally, unsold inventories contribute to GDP when they are added to stock In macroeconomics, enhancing capital is essential for boosting future production capacity.
Private gross domestic investment plays a crucial role in boosting economic growth by fostering the establishment of new businesses, which in turn attract a larger workforce and address unemployment challenges Additionally, private investment contributes to increased government revenue through taxation, benefiting society as a whole.
• Exports (X): Domestically produced goods that are sold abroad (the proceeds from the sale of goods and services abroad - which increases GDP).
• Imports (M): goods that are produced abroad, but purchased for domestic demand (the amount paid abroad by the purchase of goods and services - reduces GDP).
When we export, it will reduce the net worth bringing to the economy and it will increase the net worth in the economy if we do an import.
Rationale for the study
Gross Domestic Product (GDP) serves as a precise indicator of a country's economic health, illustrating both changes and the equilibrium levels of its key components It also highlights the government's efforts to influence these factors over time.
Understanding the factors that influence GDP and their interrelationships is crucial for economic analysis By constructing a model based on these factors, we can identify our economic strengths and weaknesses, enabling the implementation of more effective economic adjustment strategies This approach has the potential to enhance GDP growth over time and foster overall economic development.
Research questions
The relative growth rate of GDP can be affected by a number of factors, some of which show an inverse relationship while other factors show a direct relationship.
This assignment focuses on examining the impact of various factors on GDP growth in Vietnam The analysis encompasses the entire Vietnamese economy, with the research period spanning from 1995 to 2019, due to limitations in available data sources.
In this study, we will present the procedures in collecting data and the process of making our conclusion about:
- What is the relationship between GDP and four sub-indices? How do these determinants affect the economy?
- Are there any connections among these determinants?
- Are there any errors shown while running the model?
REVIEW OF LITERATURE
Before taking the projects, we looked for other research to see how the GDP was studied through previous research:
No Author/Year Research Theory Methods Result Limit
1 Karen Dynan GDP as a Macroeconomics Practical Measurement No
Random years to of evaluate the factors variables well-being affecting GDP
2 Alex Reuben The Macroeconomics Cross - Analyzing No
Kira (2013) influence theory: GDP - tabulation factors specific of factors Consumption affecting GDP analysis on UK’s and Export in Developing of
GDP from Countries: The factors
3 Dhiraj Jain The Macroeconomics Cross - To investigate No
K Sanal Nair influence theory: GDP - tabulation the impact of specific and Vaishali of factors FDI, Net FII various analysis
Jain (2015) on UK’s equity, Net FII macroeconomic of
GDP from debt, Import and factors on GDP factors
GDP in Vietnam is a vital indicator for both local and international economists, who seek to identify the key factors influencing the Vietnamese economy.
Overall, the three reports have limitations in that they do not contain specific 4 indicators affecting the GDP and taking the examination within 25 years.
METHODOLOGY
Definition of population
The scope of the research is the GDP which includes population, investment, exports and imports.
Sampling method use
We conducted a thorough evaluation of Vietnam's GDP by analyzing 25 years of statistical data With a sample size of 25, we utilized Microsoft Excel to effectively organize and present the findings.
How the data was collected
The data table can be found in the Appendix of the report, featuring statistics sourced from the GDP Vietnam website This site offers relevant and reliable data on Vietnamese GDP spanning from 1995 to 2019, which supports our project effectively.
Research design used
To analyze the impact on GDP, we employ a linear regression econometric model that incorporates the four specified variables.
GDP (Y) = β1 + β2*P + β3*I + β4*X + β4*M + uP + β3*P + β3*I + β4*X + β4*M + uI + β4*P + β3*I + β4*X + β4*M + uX + β4*P + β3*I + β4*X + β4*M + uM + u
Statistical tests
To identify the most suitable model, we conducted Ordinary Least Squares (OLS) analysis using the Eviews program on four primary functional forms: Lin-Lin, Log-Log, Lin-Log, and Log-Lin We then compared the models based on their R-squared values and coefficient of variation (CV), prioritizing the model with the highest R-squared and the lowest CV In cases where R-squared and CV conflicted, we selected the model with the lowest CV To assess the overall significance of the coefficients, we employed an F-test to determine whether the estimators were statistically significant.
The t-test is used to test the significance of each coefficient, particularly 10 coefficients This is to show whether each independent variable has any effect on the dependent variable.
To evaluate the error terms in the model, we conducted three tests The first test assessed multicollinearity using the variance inflation factor (VIF) The second test examined heteroscedasticity through White’s general heteroscedasticity test, which includes cross terms.
Finally, to check for autocorrelation, the Durbin-Watson and Breusch-Godfrey tests are conducted.
DATA ANALYSIS AND RESULTS
Descriptive Statistics
The following graphs show the relationship between GDP and others factors which are
Population, Investment, Export and Import respectively:
Figure 4: Relationship between GDP and its factors
Interpretations
Based on the EViews’ result, we have the equation:
GDP= 1 + 2*Population + 3*Investment + 4*Export + β5*Import
GDP= 1 + 2*Population + 3*Investment + 4*Export + β5*Import + Ui
Figure 5 Estimation of the best model
Thus, we have the Sample regression function is:
GDP = -3053843 + 0.040863*Population + 1.473427*Investment + 0.763855*Export - 0.401301*Import
2.1 Interpret coefficient β̂1 = -3053843:Regardless of other variables, the GDP is expected to decrease 3,053,843 billion
The analysis reveals significant relationships between GDP and various economic factors A 1 million increase in population is associated with a GDP rise of 0.040863 billion VND, indicating a positive correlation Similarly, a 1 billion VND increase in investment leads to a GDP increase of 1.473427 billion VND, highlighting the importance of investment in economic growth Exports also positively impact GDP, with a 1 billion VND increase resulting in a 0.763855 billion VND rise Conversely, imports negatively affect GDP; a 1 billion VND increase in imports is expected to decrease GDP by 0.401301 billion VND.
R-squared = 0.996421 is measure of “Goodness of fit”, which means that approximately
99.64% of total variation of GDP can be explained by the variation of four factors: Population, Investment, Export, Import.
3.1 Testing the overall significance of all coefficient
According to the test of functional form, the OLS for the model gets the following representation:
GDP = β̂1 + β̂2 * Population + β̂3 * Investment + β̂4 * Export+β̂5 * Import
We use the F-test to test the overall significant test to check the effect of all independent variables.
In hypothesis testing, we use a significance level of 5% and a number of observations n = 24.
H o : All variables have no effect on GDP (β 2 = β 3 = β 4 = β5 = 0)
H 1 : At least one variable has effect on GDP (β2≠0, β3≠0, β4≠0 or/and β5≠0)
In the Eviews table above, we obtain:
Decision rule: If F-stat > F c => Reject Ho
3.2 Testing the individual partial coefficients
In the previous section, we explored the importance of various estimators Now, we will utilize a t-test to evaluate hypotheses regarding individual partial regression coefficients, focusing on determining the significance of each independent variable.
Intercept Hypothesis Critical value: There is not enough evidence to coefficient : t c α
,kn−k = t 0.025,6 c = 2.447 conclude that the intercept coefficient statistically significant with 95% of
Population Hypothesis Critical value: There is enough statistical evidence to
,kn−k = t 0.025,6 c = 2.447 conclude that Population has an effect on GDP with 95% of confidence level.
Investmen Hypothesis Critical value: There is not enough statistical t : t c α
,kn−k = t 0.025,6 c = 2.447 evidence to conclude that Investment has an effect on GDP with 95% of
Export Hypothesis Critical value: There is enough statistical evidence to
: conclude that Export variable has an effect on GDP with 95% of confidence
Import Hypothesis Critical value: There is not enough statistical
,kn−k = t 0.025,6 c = 2.447 evidence to conclude that Import variable has an effect on GDP with
2 β5=0 Test statistic: 95% of confidence level.
V CHECKING ERRORS IN THE MODEL
Multicollinearity
One of ten assumptions of the classical linear regression model (CLRM) is that there is no multicollinearity among regressors (Assumption 10) This might be because the existence of
17 multicollinearity leads to less accuracy results of the regression coefficients and reduces the reliance of the model.
Multicollinearity occurs in a regression model when independent variables exhibit perfect linear relationships This situation arises when the equation \$\lambda_1X_1 + \lambda_2X_2 + \ldots + \lambda_kX_k + v_i = 0\$ holds true, where \$v_i\$ represents a stochastic error term and \$\lambda_1, \lambda_2, \ldots, \lambda_k\$ are constants.
Test for multicollinearity must be done to identify whether there are some functional relationships among explanatory variables so that improve the precision and accuracy for the model.
There are several consequences when imperfect multicollinearity exists, namely:
Large variances and covariances which make the estimation less accurate.
The estimation confidence intervals tend to be much wider, increasing the chance to accept “zero null hypothesis”.
The t-statistics of coefficients tend to be statistically insignificant The R2 can be very high.
The OLS estimators and their standard errors can be sensitive to small changes in the data.
In order to find out how much the variance (the square of the estimate's standard deviation) of an estimated regression coefficient is increased because of collinearity, we use a method
1 which uses the variance inflation factor: VIF = 1−R 2 i
As it can be seen from the table, all the VIF are greater than 10, our variables are high collinear Therefore, there is a multicollinear relationship between regressors.
In conclusion, our model faces a notable challenge with multicollinearity, excluding BIAS As Blanchard (1697) states, "Multicollinearity is essentially a data deficiency problem, and sometimes we have no choice over the data available for empirical analysis." Thus, in certain situations, the most prudent approach may be to accept the limitations of the data at hand.
Heteroscedasticity
Heteroskedasticity, characterized by unequal conditional variance of error terms, is a prevalent issue when constructing models with cross-sectional data, opposing Assumption 3 regarding homoscedasticity In comparison to multicollinearity, heteroskedasticity poses a more significant challenge in statistical modeling.
Heteroscedasticity exists if variances of error terms in any model are not constant according to changes in explanatory and explained variables Symbolically,
Var( u i ) = E( u i 2 ) = σ i 2 is not constant (for i = 1, 2, , n)
This indicates that the disturbance for each of the n-units is drawn from a probability distribution that has a different variance.
There are in fact both formal and informal methods to test for the existence of heteroscedasticity.
OLS estimators are still linear and unbiased
The estimated variances and covariances are biased and inconsistent t and F statistics are unreliable
We employ White's heteroscedasticity test, excluding the cross term to preserve degrees of freedom, to determine the presence of heteroscedasticity in our OLD model.
Decision rule: If W ¿ X 2 α,k df => Reject H 0
Conclusion: There is not enough evidence to infer that heteroscedasticity exists from this model at 5% level of significance.
Our model adheres to Assumption 3 of the Classical Linear Regression Model (CLRM) as confirmed by hypothesis testing Additionally, we mitigated the impact of heteroscedasticity by transforming the model into a log-log format.
“White Heteroskedasticity-consistent standard errors & covariance” method which is a robust method This action helps us have a better model with the result from Eviews below:
Figure 8: White Heteroskedasticity-consistent standard errors & covariance test
In this new model, we have the W-statistic (nR 2 ) = 6.339463 < X 2 0.05,4= 9.49 which shows that the model still be a Homoskedasticity.
Autocorrelation
The autocorrelation test aims to determine the presence of a linear relationship between errors, which violates Assumption 5 of the Classical Linear Model (CLM), where cov(u_i, u_j | x_i, x_j) should equal zero The significance of autocorrelation is comparable to that of the heteroskedasticity test, as the occurrence of autocorrelation leads to an increase in the standard error of coefficients, rather than achieving the minimum variance.
When the assumption that cov(\(u_i, u_j\)) = 0 for \(i \neq j\) is violated, autocorrelation occurs, indicated by cov(\(u_i, u_j\)) ≠ 0 for \(i \neq j\) This signifies the presence of serial correlation among the disturbances in the population regression function.
The estimated coefficients remain unbiased
Var(^ β 1 ) is no longer the smallest Therefore, its standard error also becomes large The usual t and F tests of significance are no longer valid
The residual variance σ 2 = is likely to underestimate the true σ n−2
R-squared is more likely to be overestimated.
3.3 Detection a Durbin-Watson Test AR(1):
Step 1: Ho: There is no positive autocorrelation existing
Ha: There is positive autocorrelation existing
Durbin-Watson stat: d* = 0.561738 (from Eviews)
Reject Ho if 0 < DW stat < d L
Do not reject Ho if 4 - d L < DW stat < 4 ord U < DW stat < 4 - d U
Inconclusive if d L < DW stat < d U or 4 - d U < DW stat < 4 - d L
Step 5: Conclusion: Since 0 < DW stat (0.561738) X 2 0.05 ,kdf
Do not reject Ho if LM X (5.991), we reject Ho There is enough evidence to conclude that there is high-order autocorrelation existing in order.
Figure 9: Breusch-Godfrey serial correlation LM test for AR (2)
After conducting an autocorrelation test, we identified significant high-order autocorrelation in our model To address this issue, we adjusted the standard errors of the regression coefficients using the Newey-West method, which accounts for autocorrelation with lags up to 2, while assuming that larger lags can be disregarded.
Here we have the result from Eviews:
Figure 10: Newey-West HAC standard errors & covariance
The OLS standard errors, as illustrated in figure 10, show no significant difference from those in figure 5 This indicates that while some tests suggest a correlation, the level of autocorrelation appears to be mild The detected correlation, ranging between 0.32 and 0.35, is likely not substantial enough to raise concerns.
Summary of checking errors of the model
After examining three potential errors in our model, we identified issues of multicollinearity and autocorrelation To detect multicollinearity, we employed two methods: the first method involved comparing the R-squared values of auxiliary and original regressions, which did not reveal any issues, while the Variance Inflation Factor (VIF) method confirmed the presence of multicollinearity Despite this, we chose not to address the multicollinearity based on our previous discussions For autocorrelation detection, we utilized the Newey-West method to adjust the standard errors of the regression coefficients, and the results indicated that the issue was not severe, leading us to retain the original model.
SUMMARY, CONCLUSION AND RECOMMENDATIONS
Summary and conclusion
Gross Domestic Product (GDP) serves as a key indicator for assessing a nation's economy, enabling investors and corporations to identify opportunities and strategize their investments This research paper delves into the factors influencing GDP fluctuations, with a specific focus on Vietnam from 1995 to 2019, utilizing regression analysis and essential testing methods.
Our analysis identifies four key variables affecting Vietnam's GDP: Population, Investment, Imports, and Exports A t-test was conducted to evaluate the significance of each variable, revealing that Investment (I) and Imports (M) are statistically insignificant at the 5% significance level.
Our analysis of multicollinearity using the variance inflation factor (VIF) revealed that all VIF values exceeded 10 Subsequently, we conducted a heteroscedasticity test and found insufficient evidence to support the presence of heteroscedasticity Additionally, the Durbin-Watson and Breusch-Godfrey tests indicated the existence of autocorrelation in two orders To address this, we applied the Newey-West method, which showed that the autocorrelation issue was not severe.
Recommendation
Below are some recommendations to rise the GDP of Vietnam:
Understanding the significance of GDP is crucial, as its values play a vital role in shaping the economy of Vietnam and influencing global economic trends.
Moreover, we need to statistically and clearly analyze data of factors affecting the change in GDP over the years (can be monthly or quarterly if necessary).
To boost Vietnam's GDP, it is essential to focus on enhancing investment The country's economy must grow and develop policies that foster a conducive environment for foreign corporations and companies to invest effectively.
To boost GDP growth, it is essential to focus on import and export activities This includes enhancing the circulation of domestic goods, actively exporting products to international markets, and minimizing imports.
Appendix: Data source of GDP - Dependent variable (Y)
Year GDP (Y) Population (K) Investment (I) Export (X) Import (M)
Source: General Statistic Office Vietnam