Economics Assignment Analyzing GDP Growth Rate in South Korea from 2014 to 2018
Question
Task
To prepare this economics assignment, you are to conduct an analysis of the GDP Growth Rate in South Korea. Choose a period of between 1 and 5 years that you find interesting during the last 30 years. Use the Aggregate Demand and Supply Model and your own research to explain the movements in the GDP Growth Rate over this period. Note that data is available from the Trading Economics website (https://tradingeconomics.com/southkorea/indicators), the statistics section of the Central Bank of South Korea's website (http://kostat.go.kr/portal/eng/index.action) and the website of the International Monetary Fund (https://www.imf.org/en/data). Your answer should primarily use the Aggregate Demand and Supply Model but you may also use the Loanable Funds and the FX Loanable Funds Model in your analysis if you deem it appropriate.
Answer
Abstract
As evident in the present context of economics assignment, South Korea has emerged as the successful economy in the previous decades. The economists and policy advisers are specifically interested in the pattern of economic development. Therefore the currently the economic growth pattern of South Korea is termed as the ‘miracle’ because it is surprising in its rapidity. Present study is an analysis of the growth rate of GDP of Korean Economy for the five year period of 2014 to 2018. The data for this study is taken from the Trading Economics website, the statistics section of the Central Bank of South Korea's website and the website of the International Monetary Fund. The analysis supports the demand side macroeconomic components have significant impacts on growth rate of the Korean GDP.
Introduction
The economic success of Korea is praised as the paradigm of economic growth and development and it is the focus of public curiosity and academic investigations. Currently, a significant level of interest has been showed in the dynamic, driving and leading forces behind Korean economic miracle. More explicitly, the concentration of the focus remains on the components which account for the higher growth of the economy of Korea in previous decades: productivity or technology (Kwon, 2010).
The economy of Korea is regarded as the successful example of NICs at Asia, analyzed with three kinds of empirical analysis that were applied on the basis of the dominalrole of demand, modern theory of cointegration, and presence of important economies of scale. The empiricalgrowth path of Korea of current times looks to justify the extensive doctrines of newgrowth theory (Sengupta, 2006)
The present report of empirical analysis uses the VECM/VAR procedures, and the results suggested a bidirectional causallink among the GDP growth rate and Aggregate Demand for South Korea. Also, with the addition of additional variable appeared, that capital formation/investment, net trade and government expenditures have statistically significant relation in the model. The report article further organized on the following pattern. Section II describes the data its sources and empirical methodology whereas in Section III the empirical findings are presented. Section IV is comprises of the conclusion.
Research Question
The current report provides the analysis of the GDP Growth Rate in South Korea of the last five years from 2014 to 2018 by using the Aggregate Demand and Supply Model and empirical research to explain the movements in the GDP Growth Rate over this period.
The Objectives of the study
This proposed study tends to deal with the objectives given below:
 To analyse the GDP Growth Rate in South Korea.
Hypothesis
After reviewing the literature, the following question has been constructed for the study
H0: GDP growth of the country is not influenced by the Macroeconomic Components of Aggregate Demand and Aggregate Supply.
Economic Model
Keeping in view the discussion in previous chapters this study adopted the following methodology for the estimation of a direct pattern of the GDP growth rate of South Korea.
The functional form of the model can be expressed as:
Y= f(C, I, G, T,)
Y = C + I+ G +NX+ ?
Or
?t =f (?1t, ?2t,X3t,X4t)
And the econometric model can be specified as;
?t =?0+?1?1t+ ?2?2t+?3?3t + ?4?4t +?1t …………. Where
?t ?Y=GDP
?1t ? C = Consumption Expenditure
?2t ? I= Investment
?3t ? G = Government Expenditure
?4t ? NX = Net Exports
?1t = Error term
t = 1,2,3……
Econometric Methodology
For this report I adopted the methodology which is given below for the GDP growth rate of South Korea, following econometric methodology was adopted,
?t =?0+?1?1t+ ?2?2t+?3?3t + ?4?4t +?1t
To analyze the pattern of the GDP growth rate of South Korea with relation to macroeconomic components of aggregate demand and aggregate supply including aggregate consumption expenditures, investment, government expenditures and net exports balance in Korea and there overall contribution to the growth rate of GDP of the country, this study employs timeseries data from 2014 to the 2017. The use of simple regression techniques can give spurious and misleading results because of the chances of the presence of nonstationarity in the data. Thus before to apply any test or data analysis method it is must be the initial crucial condition for testing the stationarity. Designed for this purpose, within present study the AugmentedDickeyFuller test is used, this empirical test was projected by the Dickey and Fuller for the confirmation of the presence of stationarity or nonstationarity in the data.
Empirical Analysis
The Augmented DickeyFuller (ADF)
Let a series ?t that is AR (1) is of the form,
?t = ??t1 + ?t
H0:?=1 Series is nonstationary.
H1:?<1 Series is stationary.
Three types of ttests related to the existence of stationarity or nonstationarity in the data of time series by checking the tstatistics on constant and/or the time trend.
?xt= ? xt1+ ? t
? xt = ? + ? x t1+ ? t
??t = ? + ? t + ? x t1 + ? t
In all above stated cases the null hypothesis of nonstationarity
H0: ? = 0
with the alternate hypothesis
H: ? < 0
?t = ?1xt1+?2xt2 …………………. + ?p2xt1 + ?p1xtp+2+?pxtp+?t
And ?t is White noise.
A more general form of the ADF is then
? ? t = ? + ?t + ? ? t1 + ?i ?nx=1 ? ? ti + ?t
Cointegration and ECT
Hence the variables having unit roots at level but remained stationary at the same first level of difference, I use the cointegration tests for checking the long run cointegrating relationship in variables by employing the Johansen cointegration tests which applied MLT in the VAR/VECM setting:
xt =?+?t x t 1+....+? k xt k+? t
Here xt an (n×1) vector of variables with the order of integration 1 (I(1)), and ? here being an (n×1) intercept vector, ?s are parameters whereas ?t is a residual term with the normal distribution.
VAR can also take the form of a common Vector Error Correction Mechanism (VECM). So the alternative form of the VAR concerning VECM could be
??t =?+ i=1p1t + ??ti +??t1 +?t
? represents the change or difference whereas ? and ? represent the coefficient matrices.
For the determination of the number of cointegrating vectors, the Eigenvalue scale can be used. The eigenvalues further provide with the two kinds of test figures to find the vectors which possess cointegration characteristics. Firstly, the maximum eigenvalues test (Max) can be established and results can be obtained with the following equation
max=?nlog(1??q+1)
The 2nd statistic is known as the trace with the null hypothesis can be established as R ? Q with a general alternate. To calculate the trace statistic following equation can be set
Trace = ?N log(1??i)
Now the suggested VECM for our study will have the following form of variables for the proposed model.
?? =?0+?1?1?+ ?2?2?+?3?3? + ?4?4? +?1?
If ECT possesses positive value this reflects divergence from the equilibrium point of long run. Here negative value of coefficient of ECT provides evidence on the convergence in long run equilibrium.
Discussion of Results
Testing of Stationarity
In the direction of testing the stationarity, the unit root is carried out. Augmented unit root assessment specifies the nature of the statistics and the order of integration, which is helpful to avoid spurious results and false forecasting.
The test is performed to estimate the following equation with trend variables or without trend variable.
??t= ?+? t+? ? ?t1+?i? ?ti+?t
The tests and their empirical results are given below for the analysis of the main model. Following is the table of the empirical result of ADF test.
Variables 
At Level 
At Ist Difference 

t  statistics 
p – value 
t  statistics 
p  value 

Y 
0.636737 
0.9597 
1.369259 
0.0045 
C 
0.545422 
0.9537 
0.119512 
0.0087 
I 
1.559159 
0.3944 
0.119512 
0.0087 
G 
1.282162 
0.1985 
1.421363 
0.0143 
NX 
1.227324 
0.5327 
2.913413 
0.0146 
Source: Calculated by author
Now, for the further proceeding, the same ADF analysis is applied on every variable in the proposed model to find the stationary level of the variables and to further check if these variables are stationary on the same level. Test statistics confirm that at the first difference level these variables are stationary at Constant, Linear and Trend, None.
As these results of Augmented DickeyFuller (ADF) tests confirm the nonstationarity at the level and facilitates to move forward.
Long Run Function: A Cointegration Analysis
The key purpose of this report is to find the growth rate of GDP of Korea. So, for this purpose, the Johansen Cointegration test appropriately is employed to discover the long run association amongst variables in addition to determine the extent of dependency of dependent variable over the independent variables.
The purpose of Johansen Cointegration is to discover the numbers of vectors that are cointegrating with each other. If numbers of cointegrating vectors is zero, it suggests the absence of longrun correlation amongst the variables. Johansen Cointegration method yields two likelihood ration test statistics known as Unrestricted Cointegration rank tests; one is trace test (?trace) and second is maximum Eigenvalue (?max). Results of either of the test can be employed for the identification of cointegrating vectors as per the choice of the researcher.
The table is showing the results of these tests. Trace test statistics are used for the identification and confirmation of the number of cointegrating vectors.
Ho=There lies no cointegration
H1=There lies cointegration
It is tested against the alternative hypothesis in this study by using the trace statistics.
The table given below constitutes the Trace test statistics:
Ho 
H1 
Trace Statistics 
0.05 Critical Value 
Prob. 
R = 0 
R ≥ 1 
24.15608 
17.61385 
0.0000 
R ≤ 1 
R ≥ 2 
25.40239 
19.61937 
0.1211 
R ≤ 2 
R ≥ 3 
5.883931 
35.30391 
0.1892 
R ≤ 3 
R ≥ 4 
1.231758 
2.491421 
0.3810 
R ≤ 4 
R ≥ 5 
3.8456138 
37.11218 
0.2304 
Based on the calculated Trace statistics null hypothesis of no cointegration R = 0 has been rejected. Hence, timeseries data on the base of Johansen Cointegration model indicates the existence of cointegrating vectors and thus concluding that there is a relationship for the longrun period amid time series variables of our model, i.e. Saving, Real Disposable Income and Interest Payments.
The long run coefficients of the analysis model can be expressed as,
?t =?0+?1?1t+ ?2?2t+?3?3t + ?4?4t +?1t
?t =2.31 + 0.867134?1t+ 1.487457?2t+ 1.936723?3t + 0.948174?4t +?1t
The OLS regression results showed significant effect of independent variables on the dependent variable of GDP growth rate.
The Short Run Statistics

Dependent Variable=DSRt 

Variable 
Coefficient 
tstatistic 
pvalue 
DY (2) 
0.000259 
0.75848 
0.00034 
DC(1) 
1.108672 
14.7359 
0.07524 
DC(2) 
0.085146 
1.07019 
0.07956 
DI (1) 
0.852428 
11.2651 
0.07567 
DI (2) 
0.130187 
1.70198 
0.07649 
DG(1) 
0.185093 
2.38222 
0.07770 
DG(2) 
0.041583 
0.78847 
0.05274 
DT(1) 
2.017241 
24.4395 
0.05547 
DT(2) 
0.055217 
5.0019 
0.05539 
ECT (1) 
0.020982 
2.72284 
0.00771 
C 
0.001303 
0.00686 
0.19008 
R  squared = 0.997851 F  statistic = 103.6784 Prob (Fstatistic) = 0.00000 Durbin  Watson stat= 2.154229 
Now explanation of the test statistics given in the table can be deducted as: the shortrun test statistics of the model confirm the presence of the association of dependent variable over independent variable in the shortrun. Fstatistics and probability indicate the overall significance of the model. Moreover, significance of ECT also reflects the presence of longrun relationship amongst independent variables as well as the depending variables of the estimated model. The negative sign of the coefficient of ECT confirms convergence of the model in the longrun equilibrium point. Hence Johansen cointegration estimates and ECT methodology proves the significant short run and stronger long run correlation between dependent as well as independent variables of the estimated model.
Conclusion
The results of this analytical study establish the supportive evidence on the aggregate demand side of the economy. The aggregate consumption and government expenditures played the significant role in the growth rate of GDP of South Korea. While the role of government spending in the success of economies of the newly industrialized Korea, as reflected, is well documented in previous studies and literature, we consider it essential for further addressing the issues in concurrence with these empirical results. The results of this study support the Keynesian conventional framework that causality is present from the government expenditures to the gross domestic product.
References
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