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Economics Assignment Analyzing GDP Growth Rate in South Korea from 2014 to 2018



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 (, the statistics section of the Central Bank of South Korea's website ( and the website of the International Monetary Fund ( 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.


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.

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 dominal-role of demand, modern theory of co-integration, and presence of important economies of scale. The empirical-growth path of Korea of current times looks to justify the extensive doctrines of new-growth theory (Sengupta, 2006)

The present report of empirical analysis uses the VECM/VAR procedures, and the results suggested a bi-directional causal-link 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:

  1. To analyse the GDP Growth Rate in South Korea.

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+ ?


?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 time-series 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 non-stationarity 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 Augmented-Dickey-Fuller test is used, this empirical test was projected by the Dickey and Fuller for the confirmation of the presence of stationarity or non-stationarity in the data.

Empirical Analysis

The Augmented Dickey-Fuller (ADF)

Let a series ?t that is AR (1) is of the form,

?t = ??t-1 + ?t

H0:?=1 Series is non-stationary.

H1:?<1 Series is stationary.

Three types of t-tests related to the existence of stationarity or non-stationarity in the data of time series by checking the t-statistics on constant and/or the time trend.

?xt= ? xt-1+ ? t

? xt = ? + ? x t-1+ ? t

??t = ? + ? t + ? x t-1 + ? t

In all above stated cases the null hypothesis of non-stationarity

H0: ? = 0

with the alternate hypothesis

H: ? < 0

?t = ?1xt-1+?2xt-2 …………………. + ?p-2xt-1 + ?p-1xt-p+2+?pxt-p+?t

And ?t is White noise.

A more general form of the ADF is then

? ? t = ? + ?t + ? ? t-1 + ?i ?nx=1 ? ? t-i + ?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 co-integrating 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=1p-1t + ??t-i +??t-1 +?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


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+? ? ?t-1+?i? ?t-i+?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.


        At Level

        At Ist Difference

t - statistics

p – value

t - statistics

p - value


























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 Dickey-Fuller (ADF) tests confirm the non-stationarity at the level and facilitates to move forward.

Long Run Function: A Co-integration 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 Co-integration 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 long-run correlation amongst the variables. Johansen Co-integration method yields two likelihood ration test statistics known as Unrestricted Co-integration 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:



Trace Statistics

0.05  Critical Value


R   = 0

R   ≥  1




R  ≤  1

R  ≥  2




R  ≤  2

R  ≥  3




R ≤  3

R  ≥  4




R ≤  4

R  ≥  5




Based on the calculated Trace statistics null hypothesis of no cointegration R = 0 has been rejected. Hence, time-series data on the base of Johansen Cointegration model indicates the existence of cointegrating vectors and thus concluding that there is a relationship for the long-run 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






DY (-2)












DI (-1)




DI (-2)




















ECT (-1)









R - squared =  0.997851

F - statistic = 103.6784

Prob (F-statistic) = 0.00000

Durbin - Watson stat= 2.154229


Now explanation of the test statistics given in the table can be deducted as: the short-run test statistics of the model confirm the presence of the association of dependent variable over independent variable in the short-run. F-statistics and probability indicate the overall significance of the model. Moreover, significance of ECT also reflects the presence of long-run 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 long-run 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.

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.

Kwon, K.-H. Y. (2010, October 4). Economic growth and productivity: A case study of South Korea. Applied Economics , 13-23.

Palley, T.I. (2010): Inside debt and economic growth: a neo-Kaleckian analysis, in M. Setterfield (ed.), Handbook of Alternative Theories of Economic Growth, Northampton, MA: Edward Elgar, pp. 293–308.

Puu, T., L. Gardini, and I. Sushko (2005): A Hicksian multiplier–accelerator model with a floor determined by capital stock, economics assignment Journal of Economic Behavior and Organization, 56, 331–348

Sengupta, J. K. (2006). Exports and economic growth in Asian NICs: an econometric analysis for Korea. Applied Economics , 41-51.

Skott, P. (1989): Conflict and Effective Demand in Economic Growth, Cambridge, UK: Cambridge University Press.

Skott, P. (2010): Growth, instability, and cycles: Harrodian and Kaleckian models of accumulation and income distribution, in M. Setterfield (ed.), Handbook of Alternative Theories of Economic Growth, Northampton, MA: Edward Elgar, pp. 108–131.

Solow, R.M. (1956): A contribution to the theory of economic growth, Quarterly Journal of Economics, 70, 65–94.

Tobin, J. (1975): Keynesian models of recession and depression, American Economic Review, 55, 195–202.


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