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CAUSAL INTERACTIONS BETWEEN FOREIGN DIRECT INVESTMENT AND ECONOMIC GROWTH IN INDONESIA (USING VAR MODEL)
KHOIRUL IFA, SE, M.SEStie Widya Gama Lumajang
This study aims to determine two-way relationship between Foreign Direct Investment (FDI) and Economic Growth. Empirically this research uses time series data from 1981-2015 period, this research focuses on two variables, namely Foreign Direct Investment (FDI) which is limited to FDI inward data and economic growth interpreted on Gross Domestic Product (GDP) data, this research using Vector Auto Regressive (VAR) model to see the relationship between FDI and GDP. Based on unit root test, all variables have stationary at first difference level, optimum lag lies lag 1, Granger Causality test results show there is relationship between GDP to FDI and there is no relation between FDI and GDP, It can be concluded there is a relationship but not unidirectional, while the Johansen Cointegration Test shows data is no cointegrated, but the test of stability model shows unstable data, while The Impulse Response Function (IRF) test also shows GDP response to FDI is greater than FDI response to GDP, while the Variance Decomposition test (VD) indicates that the contribution of FDI to GDP is greater than the contribution of GDP to FDI.
Keywords: Foreign Direct Investment, Economic growth, Vector Auto Regressive (VAR)
I. THE BACKGROUND OF STUDYEconomic growth is one indicator to see the economic development of a country.
Economic growth is defined as a process of increasing the output of an economy embodied in the increase in national income. The increase in output in various economic sectors is believed to accelerate the economic growth of a country.
In the last few years Indonesia's GDP has recorded positive growth, since 2015-2016 the rate of economic growth is in the range of 4-5%, but the average economic growth is still far below other Asian countries. In essence, the average economic growth varies by country. In some Asian countries, such as China, Hong Kong, Singapore, South Korea and Taiwan the average real income of the population increases by about 6% per year over the last fifty years, at this level the average income almost doubled every twelve years . These countries have changed in one generation from the poorest to the richest, while some African countries such as Chad, Ethiopia and Nigeria have on average unchanged over the last few months (Mankiw, 2014: 39)
The drivers of economic growth according to Todaro, 2003 are embodied in three main components, firstly, the accumulation of capital covering all forms and types of new investments invested in land, physical equipment, and human capital. Second, population growth will further increase the labor force. These three technological advances are in the simplest sense due to the discovery of new ways or improvements over the old ways.
In the harrod and domar growth model there is a relationship between the rate of economic growth to the increase of investment and savings. The rate of growth of long-term income, investment and planned savings must be the same, this may mean the desire to save part of the national income, the additional capital for a certain period equal to the existing investment, and all the savings are channeled into net investment. In conclusion there is a positive relationship
between investment and economic growth, the higher the investment of a country, both foreign investment and domestic investment, the higher the economic growth of a country.Economic growth underpinned by investment is considered to increase the productivity of a country or region. Investment is the purchase of goods that will be used to produce more goods and services (Mankiw, 2006: 12)
Investment in Indonesia is not only supported by domestic investors but also by foreign investors, foreign direct investment inflows (FDI) are expected to encourage sustainable investment growth within a country. Foreign capital is needed to accelerate economic development. According to Jhingan, 1990 foreign capital helps in industrialization, in building economic overhead capital and in creating wider employment opportunities. Data of investment growth in Indonesia experienced a positive growth, data from Investment Coordinating Board noted since 2011-2016 the realization of investment PMDN experienced a significant increase that reached 76 trillion rupiah in 2011 and increased by 208.4 trillion rupiah in 2016, while The realization of foreign direct investment in 2011 reached 175.3 trillion rupiahs increased by 386.4 trillion rupiah in 2016. This shows that the realization of PMA is more dominant compared to the realization of domestic investment.
Foreign direct investment (FDI) can be defined as capital inflows from other countries or outflows of capital or expansion of the company to other countries. FDI is believed to be the fastest moving investment driver of the movement, in addition to the high realization of investment from FDI, FDI is also a supporter of a country's economic growth.This is supported by several studies, such as a study conducted by Tekin, 2012 showing there is granger causality between FDI and GDP in Benin and Togo, and there is a granger relationship between GDP and FDI in Burkina faso, Gambia, Madagascar and Malawi, By Lamsiraroj and Ulubasoglu, 2015 shows there is a positive influence between FDI and economic growth in 140 countries in the 1970-2009 period, the strongest influence being found in developing countries. While Ibrahiem, 2015 mentions in the long run FDI positively affect the economic growth in Egypt in 1980-2011. While Khatun and Ahamad, 2015 explained in the short term there is a positive relationship between FDI and economic growth in Bangladesh in 1972-2010. While Yaqin su, 2015 also argues that there is a positive influence of FDI on GDP per capita.
II. LITERATURE REVIEWRelationship Between Foreign Direct Investment (FDI) and Economic Growth
According Jhingan (2004) Foreign Investment (PMA) has a role in the economic growth of a country. The first foreign capital can be utilized as a tool to accelerate investment and economic growth. Both economic growth increases should be followed by the structure of production and trade in the country. Last foreign capital as the mobilization of funds that have an important role. These three things must be supported also by the role of the government using the foreign capital for the purpose of building infrastructure.
Borenszteina, Gregorio and Lee (1998) argue that the impact of FDI on economic growth depends on the level of human capital available in the host economy, there is a strong positive interaction between FDI and the last level of education (the proxy for human capital). De Mello (1999) also mentions a positive impact between FDI and economic growth in developing countries and developed countries, he mentioned that in the long run economic growth in the old home countries is determined by the abundance of technology and knowledge of the countries Who invested. In some arguments FDI not only has an impact on economic growth but has other impacts on human capital and technology.
Previous ResearchResearch conducted by Diby, 2014 on "The impact of foreign direct investment on
economic growth in Africa: for three decades using panel data analysis" indicates there is a positive and significant influence between FDI and economic growth in the African country during the interest period. However there was a negative impact of FDI on economic growth during the period 1980-1994 and had a positive impact during the period 1995-2009.
Different research results by Belloumi, 2014 on "The relationship between trade, FDI and economic growth in Tunisia: Using the Autoregressive Distribusion Lag Model" indicates that there is no significant relationship from FDI to economic growth, from economic growth to FDI, from trade to growth Economy and from economic growth to trade in the short term in the State of Tunisia in 1970-2008.
While Nistor, 2014 in his research on "FDI and Economic Growth: Case study in the State of Romania" shows there is a positive influence of FDI inflows on economic growth and there is a positive relationship between FDI and economic growth period 1990-2012 in Romania.The different shown in the study by Dritsaki and Stiakakis, 2014 on "FDI, Export, and Economic Growth in Croatia: Time Series Analysis" indicate that foreign direct investment does not result in growth in Croatia, both in the short and long term Year 1994-2012.
Temiz and Gokmen, 2014 on a study entitled "FDI inflow as an international business operation by multinational corporations and economic growth: An empirical study of Turkey" also mentions no significant relationship between FDI inflow and economic growth in Turkey both in the short and long-term.
According Almfraji and Almsafir, 2014 that in the long run there is a relationship between FDI with Economic Growth in Qatar Country, in addition there is a positive influence of FDI on economic growth in the country of Qatar
While Abbes, et al, 2015 on "The interaction of relations between FDI and Economic Growth: dynamic panel data model and cointegration" indicates a direct relationship between FDI and GDP in 65 countries, the countries of Asia and the countries of maritime, the middle eastern state, the state of north america , Latin america country, european country, north africa country, and middle african country during the period of analysis 1980-2010.
III. METHODOLOGYTypes of Research
This research is a correlation research between variables, according to Suryana (2010) correlational research aims to examine the extent of variations in a factor related to variations of other factors based on the correlation coefficient.
The purpose of this study is explanatory (explanatory research) where exploratory research is a type of research that explains the causal relationship between one variable with other variables through hypothesis testing.
Data Collection TechniquesData collection techniques used documentary studies by collecting secondary data,
recording, and processing data related to this study. According Azwar (2001: 91) Secondary data usually tangible data documentation or data reports that have been available. The data used include: Foreign Direct Investment (FDI) and Economic Growth (GDP). While the type of data used is the time series data of the fiscal year 1980-2015. Data sources were obtained from World
Bank, International Financial Statistics (IFS), Indonesia Investment Coordinating Board (BKPM), and Statistics Data (BPS).
Data Analysis TechniquesTest Stationaryity
Stationary test is done by unit roots test, if the stationary data will not have the unit roots whereas if the data is not stationary it will have the unit roots. To test the unit roots (unit root test) using Augmented Dickey-Fuller test by looking at the series has the unit roots. If the hypothesis is zero then the series does have the unit roots.
In performing the DF test it is assumed that the error term is not correlated. But in the case of correlated error terms, Dickey and Fuller have developed tests, known as Augmented Dickey-Fuller (ADF) tests. Earlier this test did three equations by adding lag values of the dependent variable ∆ Y t . This ADF test has the following regression model:
∆ Y t = β1+ β2t +δY t−1+∑i=1
m
∝1 ∆ Y t−i+ε t
Where ε tis the term white noise error and where Y t−1 = (Y t−1−Y t−2), ∆Y t−2 = (Y t−2−Y t−3), and so on. A number of lagged difference terms are entered and determined empirically, the term error in the above equation is correlated. In the ADF we are still testing whether δ = 0 and the ADF test follow the asymptotic distribution equal to the statistical DF, so the same critical values can be used (Gujarati, 1995).
Test of Causality (Granger Causality)Test Causality is a test that measures the degree of relationship of two or more variables
independent variables with dependent variables that have a causal relationship. Furthermore, Gujarati (1995) explains if variable X (Granger) causes the variable Y, then the change in X must precede the change of Y. Therefore, in the regression of Y on another variable (including its own past values). If we enter the past values of the variable X and significantly increase the prediction of the variable Y, then we can say that X (Granger) causes Y. The same definition holds if Y (Granger) causes X.
The Granger Causality model is expressed in the following model:
FDI t = ∑i−1
n
∝i GDPt−i + ∑i−1
n
β i FDI t−i + u1 t
GDPt = ∑i−1
n
∝i GDPt−i + ∑i−1
n
β i FDI t−i + u2 t
Where: FDI: Foreign Direct InvestmentGDP: Economic Growthμ1t and μ2t are error terms that are assumed to be uncorrelatedThe model explains that whether FDI causes GDP or otherwise GDP causes FDI. The Granger Causality Test assumes that the information is relevant for the predictions of each variable, FDIAnd GDP is time series data.
Optimal Lag Determination
Optimal Lag Determination in the model is done by looking at some information using Akaike Information Criterion (AIC) and Schwarz Information Criterion (SC). When using any of these criteria then the optimal lag length is the smallest absolute value. In this study used Akaike Information Criterion (AIC) criteria to determine optimal lag length. This model estimates the VAR model with different lag levels and then compares to the value of Akaike Information Criterion (AIC). The smallest AIC value used in optimal lag determination.
Cointegration TestCoointegration though individually it is nonstationary, but there is a linear combination of
two or more stationary time series data. EG, AEG, and CRDW tests can be used to determine whether two or more cointegrated time series data (Gujarati, 1995) in addition to the cointegration of two (or more) time series data shows that in the long run there is a balance of relationships between two variables .
Several methods have been used for cointegration testing, the simplest method of which are: (1) DF or ADF unit root test on residuals of cointegration regression and (2) Durbin-Watson cointegration regression (CRDW) test Engle-Granger (EG) or Augmented Engle-Granger (AEG) Test. In addition to using the Engle Granger test, cointegration testing can also use the Johansen test used to obtain long-term relationships among variables in the model. Johansen test is a non-standard asymptotic distribution, and is similar to the Dickey-Fuller distribution (Hansen, 2017-363)
Stability Model TestStability test is used to see the ability of the model in doing forcasting or used for policy
tools, stability model is used so that the forecasting process is able to produce good predictions. According to Brown et. Al (1975) stability test is based on recursive residual.The stability test is a requirement for implants to approach the desired stability, but not always the stability shown by this test ensures that implus will lead to stability in the period the researcher wants (Ekananda 2016: 221). The model is said to be stable if the modulus value of the roots of characteristic polynomial is less than 1.
Vector Autoregression Analysis (VAR)According to Greene, 2002 The form of the VAR equation has changed greatly but not
only the form of the equations has changed, the variables in the equation have changed as well, the VAR is not only in the form of several structural models but also aims to analyze and forecast macroeconomic activities and trace the effects of policy changes and external stimuli On the economy. In addition to forecasting, VAR has been used for two main functions, Granger causality test and studying the effects of the policy through implus response factor and variance decomposition.
According to Gujarati (1995) there are several advantages of VAR method are: (1) Simple method; No need to worry about determining which variables are endogenous and which are exogenous. All variables in VAR are endogenous. (2) Simple estimates; the OLS method canApplied to each equation separately. (3) Forcasting with this method is in most cases better than some simultaneous equations models.In addition to having advantages, VAR model also has shortcomings, including:
1. Unlike simultaneous equations model, VAR model is theoritic because it uses less information before. Exceptions in the simultaneous equation model are the inclusion of certain variables that play an important role in model identification.
2. Because of the emphasis on forecasting, the VAR model is less suitable for policy analysis.
3. The biggest challenge in VAR modeling is to choose the right length of lag. Suppose you have a three variable VAR model and you decide to include eight slowness of each variable in each equation. You will have 24 lagg parameters in each equation plus constant term, so a total of 25 parameters. Except for large sample sizes, estimates with many parameters will use many degrees of freedom with all the problems associated with it.
4. Actually, in VAR model, all variables must be stationary. If that does not happen, we should change the exact data (eg, by first-differencing).
5. Since individual coefficients in VAR model estimation are often difficult to interpret, practitioners of this technique often call it the impulse response function or the impulse response factor (IRF).
IV. RESULTS AND DISCUSSION5.4 VAR Estimation Results5.4.1 Stationary Test
The stationarity test is used to see if the research variable has a unit root or does not have a unit root. The stationary data is not found in a unit root whereas the non stationary data has the a unit root. Unit root testing using Augmented dickey fuller test, stationary data must have a statistical t value on the ADF greater than the critical values.
Based on unit root test result (unit root) in table 5.1(see Appendix 1) below shows that FDI variable in research is not stationary seen t statistic value at output is equal to -1.546833, still smaller than McKinon criticism value at confidence level 1%, 5%, and 10 %. And the Probability value of 0.7927 is still greater than the critic value α = 0.05 (0.7927> 0.05). The output results indicate that the data is not stationary.
To generate stationary data it is necessary to re-test by changing the data level to the first difference data, because data that has not stationary will produce unbiased regression, that is the relationship of 2 variables or more that look statistically significant but the reality is not.
Based on the result of unit root test using first difference data it can be seen that the FDI variable in the research has been stationary because the t statistic at output is -5.489474 greater than the criticism value of McKinon at 1%, 5%, and 10% confidence level. And the probability value of 0.0004 is smaller than the critic value α = 0.05 (0.7927> 0.05). The output results indicate that the data is stationary.
While the result of unit root test of GDP variable seen that data is not stationary at data level. Table 5.3 (see Appendix 1) below shows that the statistical value of t -1.294056 is less than the critical value of McKinon at a confidence level of 1%, 5%, or 10%. And seen on the probability value greater than the critical value α 5% (0.8719> 0.05) so that the data has not been stationary.
The test results of variable GDP is not stationary at the data level then it is necessary to test unit root on the first difference data for the result of stationary variables. Estimation of stationary data will result in an unbiased regression of all variables.
The result of unit root test of GDP variable at first difference data shows that GDP variable has stationary, it can be seen that the statistical value of t (-4.124561) is greater than the critical value of MacKinnon at level 1%, 5% and 10%, besides probability value 0,0139 Greater than the critical value α 5% (0.0139> 0.05) so that the data has been stationary.
5.4.2 Optimal Lag DeterminationBefore estimating VAR we need optimal lag length to determine optimal lag length using
Akaike Information Criterion (AIC) criterion value. The smallest AIC value used in optimal lag determination. Based on Table 5.5 (see Appendix 2) below shows that the smallest Akaike Information Criterion (AIC) value lies in lag 1, then the optimal lag length is 1
5.4.3 Test of CausalityGranger causality test aims to determine the relationship between variables, relationship
patterns among variables referred to in this study is the variable Foreign Direct Investment (FDI) and Economic Growth (GDP). In the granger causality test using first diffence data known that there is a causal relationship between GDP to FDI but not vice versa.
Based on the results of Granger Causality testing using lag 1 it is known that the probability value <α 10% (0.0887> 0.1) shows there is a relationship between GDP to FDI while the other probability value is 0.4793> 0.05 (see Appendix 2) which means there is no relationship between FDI and GDP, It can be concluded that there is a relationship but not mutual / reciprocal between FDI to GDP and between GDP to FDI, in accordance with the research hypothesis:H0: Accepted: FDI does not affect (not cause) GDPH1: rejected: FDI affects (causes) GDPH0: rejected: GDP does not affect (does not cause) FDIH1: accepted: GDP affects (causes) FDI
5.4.4 Cointegration TestTo see the research data there is cointegration or not by using Johansen Cointegration Test.
The following is Johansen Cointegration Test Results using length lag 1 with fist difference data. If the value of Trance Statistic is greater than Critical Value then the equation is cointegrated and vice versa if the value of Trance Statistic is smaller than Critical Value then the equation is not cointegrated, in VAR model the equation used is unintegrated equation.
Johansen Cointegration Test results in Table 5.7 (See Appendix 3) above shows there are 2 cointegration equation with Trace Statistic value smaller than Critical Value then research data can be continued using Vector Autoregression model (VAR).
5.5 Stability Test Model ResultsThe stability test is a requirement for implants to approach the desired stability, but not
always the stability shown by this test ensures that implus will lead to stability in the period the researcher wants (Ekananda 2016: 221). The model is said to be stable if the modulus value of the roots of characteristic polynomial is less than 1.
Based on the model stability test it is seen that the modulus value in root 1 is greater than 1, it can be concluded that the model is unstable. So the model in the long run can not be stable in the period that the researcher wants
5.6 VAR Estimation ResultVAR estimates are used to look at past FDI movements and GDP in the past, in addition
the VAR model is not only structural in nature but aims to analyze and forecast macroeconomic activity and trace the effects of policy changes and external stimuli on the economy.The result of cointegration test above that each variable is not cointegrated then the model used is the VAR model, after the VAR estimation we get the equation model as follows:
FDI = C(1)*FDI(-1) + C(2)*FDI(-2) + C(3)*GDP(-1) + C(4)*GDP(-2) + C(5)GDP = C(6)*FDI(-1) + C(7)*FDI(-2) + C(8)*GDP(-1) + C(9)*GDP(-2) + C(10)
With two variables used to look at 2-way relationships will result in 10 parameters plus constants. C (1) to C (10) is the coefficient of each parameter. While FDI (-1) and FDI (-2) are FDI in lag 1 and lag 2, GDP (-1) and GDP (-2) are GDP in lag 1 and lag 2 so that the above equation can be written according to the VAR estimation result (Appendix 4)
FDI = 0.720921*FDI(-1) + 0.044858*FDI(-2) + 11205510*GDP(-1) 8980841.*GDP(-2) - 3.88E+09 …………Model VAR 1
GDP = 5.41E-09*FDI(-1) - 4.53E-09*FDI(-2) + 1.217320*GDP(-1) - 0.211149*GDP(-2) + 43.88528 …………Model VAR 2
Model 1 FDI = 0.720921*FDI(-1) + 0.044858*FDI(-2) + 11205510*GDP(-1) 8980841.*GDP(-
2) - 3.88E+09
Based on the estimation result of VAR (Appendix 4) model 1, FDI variable able to explain the diversity of 87% (R Square value), the result of equation of model 1 can know the relationship between FDI to FDI in lag 1 is significant with value P value 3.20726> α 5% . The relationship between FDI to FDI in lag 2 is significant with P value 0.19660> α 5%, whereas the FDI relationship to GDP in lag 1 is significant with P value 1.52193> α 5%, while the FDI relationship to GDP in lag 2 is not Significant with value P value -1.22312 <α 5%,
Model 2 GDP = 5.41E-09*FDI(-1) - 4.53E-09*FDI(-2) + 1.217320*GDP(-1) 0.211149*GDP(-
2) + 43.88528
Based on the estimation results of VAR (Appendix 2) model 2, the GDP variable is able to explain the diversity of 98% (R square value), the result of model equation 1 can be seen the relationship of GDP to FDI in lag 1 is significant with P value 0.85928> α 5% On the other hand the relationship between GDP to FDI in lag 2 is not significant with P value -0.70955 <α 5%, while the relationship between GDP to GDP in lag 1 is significant with the value of P value 5.90323> α 5%, while the relationship between GDP on GDP in lag 2 is not significant with P value -1.02675 <α 5%.
1. Implus Response Factor (IRF)Next to know the shock (shock) of each variable against other variables using Implus
Response Factor (IRF). Here is an IRF estimate based on the relationship of each variable (See Figure 5.4)
The IRF estimation results can be seen using the standard deviation values of each variable. In the FDI response shows that the initial standard deviation of the period of the FDI variable of 3.05E + 09 does not bring any effect to the GDP variable (standard deviation = 0), but in the next period the FDI response to GDP has a standard deviation value that increases each period which means response FDI on GDP in subsequent periods generates a positive response.However, the IRF estimation result on GDP response shows positive response with FDI standard deviation value of 40.33621 which gives effect to GDP variable equal to 75.39503 whereas in the next period the GDP response increases with increasing standard deviation value every period. It can be concluded that the GDP response to FDI is greater than the FDI response to GDP
While in view from IRF Chart shows FDI Response to FDI shows fluctuating development since the shock or shock to FDI at the beginning of period until period 10, this can be interpreted the amount of FDI entering in Indonesia progressively develop along with some easiness to invest in Indonesia, such as licensing, improved infrastructure, and more stable economic conditions.
While the FDI response to GDP shows a positive trend from the beginning of the period to the period of 10. The impact of FDI shock on GDP is felt, if FDI rises will actually increase GDP and vice versa if FDI decreases will reduce the portion of GDP in Indonesia. It can be interpreted that the change of foreign capital inflows of FDI in Indonesia is crucial to the increase of economic growth (GDP). While the GDP response to FDI shows a positive trend from the beginning of the period to the period of 10. At the beginning of the 5th period reached 76.04439 US Dollar to the end of the 10th period of shock GDP against FDI reached 130.2549 US Dollars. This indicates that the increase in economic growth in Indonesia is caused by an increase in the amount of foreign capital inflows (FDI). The GDP response to GDP shows a positive trend, from the beginning of the first to the end of the period to 10. As a result of the increase in economic growth in the previous year it will increase economic growth in the years to come.
Figure 5.4Result of Implus Response Factor (IRF)
-2,000,000,000
-1,000,000,000
0
1,000,000,000
2,000,000,000
3,000,000,000
4,000,000,000
5,000,000,000
1 2 3 4 5 6 7 8 9 10
Response of FDI to FDI
-2,000,000,000
-1,000,000,000
0
1,000,000,000
2,000,000,000
3,000,000,000
4,000,000,000
5,000,000,000
1 2 3 4 5 6 7 8 9 10
Response of FDI to GDP
-80
-40
0
40
80
120
160
200
240
1 2 3 4 5 6 7 8 9 10
Response of GDP to FDI
-80
-40
0
40
80
120
160
200
240
1 2 3 4 5 6 7 8 9 10
Response of GDP to GDP
Response to Cholesky One S.D. Innovations ± 2 S.E.
2. Variance Decomposition (VD)Analysis of Variance Decomposition (VD) is an analysis to predict the contribution of
variable variance due to change of certain variable in VAR model. With this analysis will be known how the contribution of FDI variable due to change of GDP variable in each period and how the contribution of GDP variable due to change of FDI variable in each - period. The VD test is intended to test the estimated variance error of a variable how much variance is before and after shock, either shock coming from self or from another variable. The output of VD shows that the contribution of FDI due to changes in GDP is greater than the contribution of GDP due to changes in FDI, as shown in Table 5.10 (See Appendix 5) shows the contribution of FDI from the beginning of the period to the end of the 10th period has a greater contribution value than the contribution of GDP.
From the other side, at the beginning of 2nd period FDI variable able to explain the variation of GDP of 95,8% but downward trend until the end of 10nd period FDI variabel able to explain variation of GDP only equal to 51,13%, whereas opposite variable of GDP able to explain variation of FDI By 4.1% at the beginning of the 2nd period and continue to climb up (positive trend) until the end of the 10th period reaching 48.86%. This suggests that past and present information of the GDP variables can provide forecasts for the FDI variable in Indonesia.
5.7 DiscussionBased on the results of Granger Causality testing it can be concluded that there is a
relationship between GDP and FDI but the relationship is not unidirectional / it can be interpreted that any increase in economic growth will cause an increase to the amount of Foreign Direct Investment (FDI) in Indonesia, Direct Investment (FDI) does not lead to an increase in economic growth in Indonesia, the statement is in accordance with Dritsakti and Stiakakis 2014 study which states that there is no positive relationship between economic growth and foreign
direct investment to economic growth but there are other variables such as Exports that have a positive relationship to economic growth. Foreign Direct Investment (FDI) does not lead to an increase in growth in Indonesia, there are other variables that have a positive relationship to economic growth such as exports, the government must reform policies related to Foreign Direct Investment (FDI), as the number of FDI realizations is increasing every year, Should be supported with the government's readiness to help ease investment. Indonesia has undertaken various investment policy packages such as investment climate improvement packages, infrastructure improvement packages and financial sector development packages.On the other hand, there is a relationship between GDP and FDI which indicates that the increase of economic growth in Indonesia will be responded by the increase of FDI, to increase economic growth in Indonesia is necessary to promote the various primary, secondary and tertiary sectors that contribute to the increase of output, opportunities from these various sectors Which can attract foreign investors in investing in Indonesia.
Similarly, the result of Implus Response Factor (IRF) is that the GDP response to FDI is greater than the FDI response to GDP, the statement is in accordance with the statement of Temiz and Gokmen, 2014 which states there is no significant relationship between FDI inflow and economic growth in Turkey either In the short and long term, but there is a positive response from economic growth to FDI. But the results of this study reject the growth theory of Harrord Domar which states capital formation is considered an expenditure that will increase the ability of an economy to produce goods and or services, as well as spending that will increase the effective demand of the whole society. Where if at any given time a certain amount of capital formation takes place, then in the future the economy has the ability to produce larger goods and / or services (Sadono, 2007: 256-257) While the Variance Decomposition (VD) estimates indicate that the contribution of FDI due to changes in GDP is greater than the contribution of GDP due to the change in FDI it can be concluded that if GDP changes up / down it will affect the size of FDI in Indonesia, in this case past and future infromasi Now the GDP variable can provide forecast of FDI variable in Indonesia.
V. CONCLUSION.Based on Granger Causality test results can be concluded there is a relationship between
GDP and FDI but the relationship is not unidirectional / reciprocal, it can be interpreted that any increase in economic growth will cause an increase to the amount of Foreign Direct Investment (FDI) in Indonesia, but not vice versa Against Foreign Direct Investment (FDI) does not lead to an increase in economic growth in Indonesia.
The result of Implus Response Factor (IRF) shows the GDP response to FDI is greater than the FDI response to GDP, The Variance Decomposition (VD) estimates show that the contribution of FDI due to the change of GDP is greater than the contribution of GDP because of the FDI changes it can be concluded that if GDP changes up / down it will affect the size of FDI in Indonesia.
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