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01.INTRODUCTIONMany policy makers and academics contend that foreign direct investment (FDI) can have
important positive effects on a host countrys development effort.
In addition to the direct capital
financing it supplies, FDI can be a source of valuable technology and know-how while fostering
linkages with local firms, which can help jumpstart an economy. Based on these arguments,
industrialized and developing countries have offered incentives to encourage foreign direct
investments in their economies. Especially, FDI provides much needed resources to developing
countries such as capital, technology, managerial skills, entrepreneurial ability, brands, and
access to markets. These are essential for developing countries to industrialize, develop, and
create jobs attacking the poverty situation in their countries. As a result, most developing
countries recognize the potential value of FDI and have liberalized their investment regimes and
engaged in investment promotion activities to attract various countries. Globalization and
regional integration arrangements can change the level and pattern of FDI and also it reduces the
trade costs. However, FDI flows to developing countries started to pick up in the mid-1990s
largely as a result of progressive liberalization of FDI policies in most of these countries and the
adoption of generally more outward- oriented policies.
In the context of Sri Lanka, before 1977 since we had practiced closed economic situation there
were plenty of limitation for international trade and FDI. However, White Paper which is
presented in 1966 and foreign advisory committee was set up in 1968 have looked to possibility
of improving contribution of FDI on economic growth of the county. In fact after realizing the
significance of market economic policies, both economists and politicians had discovered the
possibility of capturing FDI more and more. As a result of that Foreign Investment Act was
established on 1978, in order to build a proper way for the above task. All these efforts have
leaded to attract enormous FDI up to now. It specially includes free trade zones such as
Katunayake (1978), Biyagama (1986) Koggala, (1991) Pallekelle (1996) Mirigama (1997) and
Malwatte (1997) which has created thousands of employment opportunities and contributed to
national economy by providing exports income.
Therefore this study mainly focused on analyzing the effect of FDI on Sri Lanka economic
growth. According to the structure of the study here after, one can go through problem statement,
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research objective, literature review, methodology, data analysis, results and discussion and
conclusions and recommendations.
02.PROBLEM STATEMENTHowever, we had attracted significance level of FDI opportunities; the economy of Sri Lanka is
still struggling to overcome from the developing status, since our economic growth is not
sufficient to pull our economy to developed category to from the current situation. Consequently
it is doubtable the degree of significance of FDI on Sri Lanka economic growth. Therefore it is
worthwhile to create a clear picture about the effect of FDI on Sri Lanka economic growth and
hence more specifically, the research question can be interpreted as following;
Is FDI an important factor in explaining Sri Lanka economic growth?
Throughout this research paper, I have employed the number econometric tools in order to
quantify the question based relationship.
03.OBJECTIVE OF THE STUDYIn accordance with the research problem, the key objective of this research is, identify the
relationship and degree of significance of FDI on economic growth in Sri Lanka. The several
steps have been included along with the different theoretical supports to achieve the unique
objective.
04.LITERATURE REVIEW
International trade has grown radically in the past fifty years. However, in the past twenty years,
FDI has increased enormously, with a faster growth than international trade. Kreinin, Plummer
and ABE (1998) found that, in recent decades, international trade has increased at a percentage
of GDP in most major economies, but FDI and other financial flows have been growing
exponentially. The total value of inward FDI in the world increased from about US$ 200 billion
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in 1993 to US$ 1.3 trillion in 2000 (UNCTAD,2000). FDI with a rapid growth has interested
researchers and government policy makers. Foreign Direct Investment (FDI) is one form of
capital flows which have a particular impact on economic growth in developing countries and
multinational enterprises (MNEs) are the main drivers of FDI (Fortanier, F. and Maher, M.,
2002). OECD (1978) defined the main forms of FDI as follows:
Outlays for the establishment of a new enterprise or for the expansion of an existing
enterprise whose operation is controlled by the foreign investor.
Financial outlays for the acquisition of an existing enterprise (or part of it) either
through direct purchase or through purchases of equity, with a controlling interest by
the foreign investor. The notion of control is not defined, but control is assumed when
the foreign investor owns at least between 10 and 51 percent of the enterprises value
according to different definitions used by different governments.
Intra-corporate long-term loans.
The linkage between FDI and economic growth has been studied in past twenty years. Most of
the studies focus on the impact of inward FDI on economic growth through either direct or
indirect effect. Generally speaking, inward foreign direct investment (FDI) can lead to job
creation, increase tax revenue, introduce advanced management skills and technologies, benefitthe insufficient domestic capital formation, and increase foreign exchange reserves. It provides a
unique combination of long-term finance, technology, training, know-how, managerial expertise
and marketing experience (Bende-Nabende, 1999). One of the most direct effects of inward FDI
on economic development is that inward FDI is an important financing source of domestic
capital. It can increase the production of the host country by adding to the countrys savings and
investment, and it is more stable than other forms of private capital inflows, e.g. portfolio equity
and debt flows (Fortanier, F. and Maher, M., 2002).
However, inward FDI is more than a form of capital flow. Todaro (1982), Dunning (1970) and
Krueger (1987) argued that through the capital accumulation in the host country, inward FDI was
expected to generate non-convex growth by encouraging the incorporation of new inputs and
foreign technologies in the production function of the host country. The more important effect of
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FDI is to increase the productivity of the host country through technology transfer. Although
technology can also be transferred through foreign trade, as argued earlier, inward FDI has a
unique impact on the transfer. Fortanier, F. and Maher, M. (2002) summarized four channels
through which inward FDI may lead to technology transfer, namely, vertical linkages, horizontal
linkages, labour migration and the internationalization of R&D activities. Vertical linkage
indicates backward linkages with suppliers and forward linkages with buyers (either individual
consumers or other firms). These business partners of the host country may be able to partly or
entirely absorb some explicit and implicit technology. Horizontal linkages refer to relations with
the competitors of the MNEs subsidiaries. The diffusion of technology takes place through the
competitors in two ways: demonstration and competition. The MNEs expose the superior
technology to the local firms and lead them to update their technology. The entrance of foreign
firms also strengthens the competition in the host countries and forces the local firms to improve
the production technology. These two effects are difficult to disentangle and may reinforce each
other. Labour migration is another way through which technology may be transferred and
disseminated. Employers by the MNEs acquire superior technology and management skills.
When they switch to work for local firms or start their own business, their acquired advanced
technology and management skills spread. The MNEs will also bring some R&D activities to the
host country, which may also lead to the improvement of technology.
However, economic growth can also benefit inward FDI. Economic growth induces the increase
in domestic market size which is a determinant of inward FDI. Meyer (1999) argued that output
growth was an important reflection of market size in one host country, and penetration of
foreign market is a major motive for FDI. Rapid economic growth, accompanied by an
increasing per capita income, will create huge opportunities by expanding the domestic
consumption demand (for both industrial and consumer goods) in the host country. Output
growth is considered as one important determinant for FDI inflows to a host country and this
argument is often called a market size hypothesis (OECD, 1983; Moore, 1993; Shan, 2002).
More importantly, rapid economic growth in the host country will build the confidence of
overseas investors for investing in the host country (Shan, 2002). According to the static
investment theory, a risk is always associated with an investment and investors always try to
reduce the risk in pursuing a high return. A high-speed growth which indicates a low risk in the
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investment is undoubtedly attractive for the investors. Thirdly, economic growth is associated
with an increase in capital demand. The increase in capital demand pushes the governments to
embark on incentive policies towards attracting FDI inflow in the case of shortage of domestic
capital. The increasing capital demand also raises the price of capital, indicating an increase in
the return of capital, and consequently induces inward FDI.
Finally, economic growth is also accompanied by an improvement in investment environment,
such as the infrastructure, energy supply, legal system, human capital, education, and R&D level.
A good investment environment can induce foreign investment. Hence, in empirical studies, it is
shown that the causality between inward FDI and economic growth can run in either direction,
that is, not only can inward FDI Grangercause economic growth but also economic growth can
cause FDI. Toda and Yamamoto (1995) found that there was indeed a two-way causality
between FDI and output in China. Shan (2002) also found the evidence of bi-directional
causalities between inward FDI and output growth in the case of China. However, the studies on
the causality between inward FDI and economic growth are rare as compared to the studies on
exports and economic growth.
05.METHODOLOGY
05.1 DataSince this study mainly based on time series data during the period of 1990- 2009, the data set
was collected by the various issues of Central Bank annual reports. In addition to that, several
issues of Socio Economic Statistics published by the Central Bank of Sri Lanka also were
considered.
05.2 Theoretical ModelIn economic literature, Cobb-Douglas production function which has been established by Charles
Cobb and Paul Douglas in 19001928 provides extensive applications for growth accounting. Since
it is the more realistic production function, current study engaged with this production function
in order to launch solid theoretical background. Cobb- Douglas production function can be
interpreted as follows.
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1LAKY
In above function;
Y- Output level
A- Total Factor Productivity
K- Capital
LLabour
and (1- ) Labour and Capital elasticity of output
Based on the Cobb Douglas production function, I formulated another model using the variables
which are appropriate for this study as follows. Especially, I established the following model by
incorporating FDI in to initial Cobb-Douglas production function. In addition to FDI I have
included several explanatory variables such as total trade and domestic investment and labour
which can be used to explain the growth rate of real GDP. Further, domestic investment has been
considered as a proxy for capital stock.
4321
0
GRTOTGRFDIGRLGRDINVGRRGDP
Where;
GRRGDP - Growth Rate of Real GDP
GRDINV - Growth Rate of Domestic Investment
GRL - Growth Rate of Labour
GRFDI - Growth Rate of Foreign direct Investment
GRTOT - Growth Rate of Total Trade
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05.3 Estimation techniques
05.4 Unit Root TestBefore moving down to empirically estimate the above model, it is wise to check data for
stationarity in order to avoid the spurious regression in time series data. Therefore unit root test
was done since; the unit root test that captures the order of integration of the time series can be
utilized to examine the stationarity. The unit root tests are carried out for all the variables in the
model by using the Augmented Dickey-Fuller (ADF) test. The ADF test for one unit root is
based on the following regression
t
n
i itittXtXX 11
whereXtcan be real inward FDI, real exports and real GDP, t represents time, t is random error
term, and n is the number of lag, selected in terms of Schwarz Criterion (SC). The null
hypothesis is = 0. If this null hypothesis is not rejected, the corresponding time series will be
non-stationary; otherwise, the time series will be regarded as stationary and said to be integrated
of order zero, denoted as I(0). Unless the null hypothesis is rejected one should correct the
variables by taking their appropriate log transformation or differences.
06.RESULTS AND DISCUSSIONAs mentioned in the above my focus is to put more weightage on econometric analysis rather
than descriptive analysis. However several graphs have been included to illustrate and identify
the relationship between various explanatory variables and the real GDP.
06.1 Descriptive Analysis
In accordance with the title of the paper, my first effort is to illustrate the relationship between
real GDP and Foreign Direct Investment (FDI) during the period of last two decades starting
from 1990.
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Figure01: Relationship between Real GDP and FDI
Source: Central Bank Annual Reports
It is apparent that both real GDP and FDI are illustrating increasing trend over the time even
though FDI has shown little bit fluctuating manner. Especially, FDI has been increasing
dramatically after 2005 compared to the other periods while the real GDP has been showing only
a gradual increment. Mainly, the outward economic policy which has been promoted during the
period of 2000s has significantly influence the inward of FDI after 2005. Apart from the
behavior of two series, the most important fact is that the positive relationship between real GDP
and FDI by showing the impact of FDI on real GDP.
In fact the contribution of international trade is vital in economic performance in the country
with the globalization. Even though it is very difficult to build a clear picture about the
underlying relationship in the context of Sri Lanka, since the total trade is indicating much more
fluctuating manner.
Figure02: Relationship between Total Trade and Economic Growth Rate
0
10000
20000
30000
40000
50000
60000
70000
80000
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
RGDP
FDI
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Source: Central Bank Annual Reports
According to the above graph, it is obvious that no specific pattern between two series of data as
the previous graph on real GDP and FDI. It implies that total trade is not a significant factor in
explaining economic growth in Sri Lanka during the sample period.
However not only FDI, but domestic investment also plays a massive role, therefore it is wise to
identify the actual performance of both domestic investment and FDI.
Figure03: Domestic Investment Ratio and Growth Rate of FDI
-50
0
50
100
150
200
250
300
350
400
450
GRRGDP
TOT
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Source: Central Bank Annual Reports
It is obvious that domestic investment shows a smoothing pattern over the time while growth rate
of FDI indicate fluctuate pattern as usually. Basically FDI inflows depend on various factors
including both domestic and international economic conditions. Consequently FDI more
generally more capricious compared to domestic investment. Therefore it is very essential to
maintain a stable domestic economic and political culture in order to maximize the FDI inflows
since we are unable to influence the global scenarios.
06.2 Econometric AnalysisBefore moving to estimate the VAR model, I checked the stationary of the variables as
mentioned in the methodology. I used Augmented Dickey Fuller (ADF) test as a unit root test
along with the Akaike Info Criteria (AIC) and according to the ADF results all the variables are
stationary in their level forms. The following table indicates the stationarity of all other variables
at their level forms since the probability value of each series is less than 0.05.
Table- 01: ADF test results for level form of the variables
-100
-50
0
50
100
150
200
250
300
350
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
DINVEST
GRFDI
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Series Prob. Lag Max Lag Obs
GRFDI 0.0065 1 3 16
GRL 0.0001 0 3 18
GRRGDP 0.0158 0 3 18
GRDINVEST 0.0295 1 3 17
GRTOT 0.0096 0 3 18
Since all the variables are stationary at the level forms, they can be interpreted as I(0) variables
where both OLS and VAR models can be applied to analyze the effect of FDI on economic
growth of Sri Lanka. However, it is quite better to apply VAR model rather than OLS method,
since VAR model facilitates a path way to identify the short run dynamics of concerned
relationship. Once the initial VAR model estimated, I re-estimated the VAR model by applying
the appropriate lag length. In the lag selection criteria, the Schwarz Information Criteria was
employed since the research dealing with the small time period. According to the Schwarz
Information Criteria, one lag was included and this lag length was justified by the other criteria
as well. Furthermore the stability of the VAR model is quite crucial to provide a solid basis for
policy analysis. Hence, the Auto Regressive Root Graph was considered for that task and the
graph can be illustrated as follows.
Auto Regressive Root Graph
In accordance with the following graph, since all the variables are inside the circle and
consequently the estimated VAR model is pretty well to explain the short run dynamic of FDI on
economic growth. Apart from the stability of VAR model, in accordance with the methodology
after estimating the VAR model I used Impulse Response Function (IRF), Variance
Decomposition (VD) and Granger Casualty Test (GCT) to interpret the results of VAR model.
Figure04: Auto Regressive Root Graph
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Impulse Response Function
IRFs trace out the expected responses of current and future values of each of the variables to a
shock in one of the VAR equations. In this regards, shocks can be defined or measured in
different ways. The shock may be equal to the one standard deviation or one unit of the residual;
otherwise one can follow the generalized impulse method depending on the statistical package
which they are using. In this study I gave shock to the residual of each endogenous variable
which is equal to the one standard deviation and the following graphs illustrate the possible
outcomes of these shocks.
Figure05: Impulse Response Functions
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Inverse Roots of AR Characteristic Polynomial
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According to the results of the Impulse Response Function, it can be seen that at 5 percent
significance level the response of GRRGDP is not statistically significance with respect to the
shocks of each endogenous variables. Even though the shock of GRFDI was unable to create a
-4
-2
0
2
4
6
1 2 3 4 5 6 7 8 9 10
Response of GRRGDP to GRL
-4
-2
0
2
4
6
1 2 3 4 5 6 7 8 9 10
Response of GRRGDP to GRRGDP
-4
-2
0
2
4
6
1 2 3 4 5 6 7 8 9 10
Response of GRRGDP to GRFDI
-4
-2
0
2
4
6
1 2 3 4 5 6 7 8 9 10
Response of GRRGDP to TOT
-4
-2
0
2
4
6
1 2 3 4 5 6 7 8 910
Response of GRRGDP to DINVEST
Response to Nonfactorized One S.D. Innovations 2 S.E.
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statistically significance response, the trend is much crucial. Specifically, the graph of response
of GRRGDP to GRFDI is showing that a positive shock of GRFDI will create a positive and
increasing effect on GRRGDP up to 2 years and then this effect gradually decreasing and after 3
years time the effect will die out. Furthermore all other variables such as growth rate of total
trade, growth rate of domestic investment and growth rate of labour have not been able to
maintain a considerable effect on growth rate of real GDP. As a whole, FDI can influence
GRRGDP compared to the other endogenous variables, even though the relationship is not
statistically significance.
Variance Decomposition Analysis
Variance decomposition decomposes variance in an endogenous variable in to the component
shocks to the endogenous variables in the VAR. The variance decomposition gives the
information about the relative importance of each random innovation in the VAR. The column
S.E. in the below Variance Decomposition table is the forecast error of the variable for each
forecast horizon. The source of this forecast error is the variation in current and future values of
the innovations to each endogenous variable in the VAR.
Table- 02: Variance Decomposition Analysis
Period S.E. GRL GRRGDP GRFDI TOT DINVEST
1 2.203661 1.252729 98.74727 0.000000 0.000000 0.000000
2 2.725159 1.206248 93.07922 3.091372 0.844502 1.778662
3 2.800075 1.167074 92.49826 3.022618 1.185792 2.126255
4 2.822757 1.165754 92.40363 3.015655 1.256422 2.158538
5 2.827174 1.165268 92.39060 3.014418 1.268709 2.161003
6 2.827545 1.165219 92.38993 3.014289 1.269493 2.161068
7 2.827584 1.165219 92.38991 3.014292 1.269514 2.161065
8 2.827586 1.165219 92.38990 3.014294 1.269516 2.161071
9 2.827586 1.165219 92.38990 3.014294 1.269517 2.16107310 2.827586 1.165219 92.38990 3.014294 1.269518 2.161074
Moreover, it can be seen according to the above graph, GRRGDP accounts for its variance in a
magnificent proportion followed by the GRFDI. Even though GRFDI maintained the second best
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relative importance among the other endogenous variables, its accounts only for quite low
proportion. Consequently, the effect of the GRFDI on GRRGDP is considerably low in the Sri
Lanka economy.
Granger Causality Test
Basically, Granger Causality Test can be employed in order to examine the direction of the
causality among the variables. Granger-causality requires that lagged values of particular
variable are related to subsequent values in another variable, keeping constant the lagged values
of secondly mentioned variable and any other explanatory variables. The results of the Granger
Causality test can summarized as follows.1
GRFDI does not Granger Cause GRRGDP 16 0.09430* 0.9107GRRGDP does not Granger Cause GRFDI 0.60541 0.5631
* - Significance at 10 percent level
The results of Granger Causality Test imply that GRFDI Granger cause GRRGDP, however this
causality is significant only at 10% significance level and there is no reverse relationship inbetween these two variables. This direction of causality stresses that even though GRFDI causes
to influence the GRRGDP, in fact the magnitude of this relationship is quite low since it is only
significant at 10% level. The same results can be found by reviewing the literature also, for a
example Athukorala(2003). According to the Athulorala (2003), It is evident in the results that
the regression analysis does not provide much support for the view of a robust link between FDI
and growth in Sri Lanka. In fact, the inflows of FDI maintain a considerable level; the economic
and political back ground of the country is still unfavorable and insufficient to get the maximum
benefits from the inward FDI. Therefore the contribution of FDI on economic growth is still
maintaining a lower level. Moving to the other pair wise causalities, there is bi direction
causality in between growth rate of total trade and growth rate of FDI and also it is significant at
5% level. The rationale behind this is when the trade agreements are expanded and when the
1Refer the appendices for the full output
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country is more open to the world, total trade shows a increasing pattern and since the country is
more open to the world there is a higher potential to attract the FDI. Moreover GRFDI also
Granger causes to GRDINVEST and this causality is significance at 10% level. It is obvious that
when the foreign companies setup their business domestically, there should be an encouragable
infrastructure facilities and stable financial system. Thus, in order to ensure attraction business
vicinity, domestic investment should be increased.
07.CONCLUSIONS AND RECOMMENDATIONSThis study attempted to quantify the relationship between FDI and economic growth of Sri
Lanka using VAR analysis. As a whole, even though GRFDI shows positive effect on GRRGDP
the magnitude of this effect is quite low. According to the Impulse Response Function, a shock
in GRFDI may cause to increase the economic growth for two years and then it leads to pull
down to economic growth and however after three years time the effect will die out. Variance
decomposition proposed that the variation which is explained by GRFDI is quite low. Moreover,
Granger causality test discovered that one way causality which is going from GRFDI to
GRRGDP and however there is an only 90% confidence about this direction of causality. In fact
this also justified that even though GRFDI can influence the GRRGDP in a positive manner this
is considerably low. Furthermore, the results indicated that there is a bi-directional causality in
between GRFDI and GRTOT.
In the current context of Sri Lanka, the significance of FDI is at a lower level even though there
is a potential to utilize FDI to enhance the growth rate in Sri Lanka. However, the factors which
can enhance the contribution of FDI such as infrastructure facilities, stable economic and
political situations are not working smoothly currently. In fact after finishing the civil war
situation, FDI inward has grown rapidly even if the encouragable vicinity is not present yet.
Therefore, this study strongly recommends that to build and maintain supportable infrastructure
facilities along with stability economic condition in the country to improve the performance of
FDI in order to achieve a higher contribution to economic growth.
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REFERENCES
APPENDICES
01.Lag Selection Criteria of VARVAR Lag Order Selection CriteriaEndogenous variables: GRL GRRGDP GRFDI TOTDINVEST
Exogenous variables: C @TREND
Date: 04/29/11 Time: 22:15
Sample: 1990 2008
Included observations: 17
Lag LogL LR FPE AIC SC HQ
0 -306.4447 NA* 1.02e+10* 37.22879* 37.71892* 37.27751*1 -288.5269 21.07973 3.02e+10 38.06199 39.77743 38.23251
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
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02.Re-estimation of VAR with selected lag lengthVector Autoregression Estimates
Date: 04/25/11 Time: 09:56Sample (adjusted): 1991 2007
Included observations: 17 after adjustments
Standard errors in ( ) & t-statistics in [ ]
GRL GRRGDP GRFDI TOT DINVEST
GRL(-1) -0.188427 -0.004981 -3.075031 -6.958239 -0.052814
(0.25790) (0.35023) (11.3149) (12.6476) (0.19659)
[-0.73061] [-0.01422] [-0.27177] [-0.55016] [-0.26865]
GRRGDP(-1) -0.157946 -0.176813 -8.887991 2.792415 0.497850
(0.25901) (0.35174) (11.3636) (12.7021) (0.19744)
[-0.60979] [-0.50269] [-0.78215] [ 0.21984] [ 2.52152]
GRFDI(-1) 0.010269 0.004898 0.024353 0.007831 -0.003589
(0.00740) (0.01005) (0.32476) (0.36301) (0.00564)
[ 1.38723] [ 0.48728] [ 0.07499] [ 0.02157] [-0.63604]
TOT(-1) -0.009409 -5.61E-05 0.080391 -0.070772 0.003328
(0.00754) (0.01024) (0.33080) (0.36977) (0.00575)
[-1.24779] [-0.00548] [ 0.24302] [-0.19140] [ 0.57906]
DINVEST(-1) 0.134220 -0.316694 1.345447 -3.231800 0.682953
(0.29323) (0.39820) (12.8646) (14.3799) (0.22352)
[ 0.45773] [-0.79532] [ 0.10459] [-0.22474] [ 3.05546]
C -1.702748 12.29321 71.72536 48.74796 6.103630(7.88097) (10.7022) (345.757) (386.483) (6.00745)
[-0.21606] [ 1.14866] [ 0.20744] [ 0.12613] [ 1.01601]
@TREND 0.100077 0.226718 -1.891894 7.220164 -0.054734
(0.12446) (0.16901) (5.46033) (6.10349) (0.09487)
[ 0.80409] [ 1.34142] [-0.34648] [ 1.18296] [-0.57692]
R-squared 0.390179 0.191375 0.110276 0.161284 0.642348
Adj. R-squared 0.024286 -0.293800 -0.423559 -0.341945 0.427757
Sum sq. resids 48.56123 89.55192 93469.89 116786.1 28.21704
S.E. equation 2.203661 2.992523 96.67983 108.0676 1.679793
F-statistic 1.066374 0.394446 0.206573 0.320498 2.993360
Log likelihood -33.04366 -38.24560 -97.32550 -99.21848 -28.42901
Akaike AIC 4.711019 5.323012 12.27359 12.49629 4.168119Schwarz SC 5.054107 5.666100 12.61668 12.83938 4.511207
Mean dependent 1.522056 5.735961 39.81887 37.17824 25.12941
S.D. dependent 2.230918 2.630898 81.03043 93.28849 2.220576
Determinant resid covariance (dof adj.) 5.39E+09
Determinant resid covariance 3.80E+08
Log likelihood -288.5269
Akaike information criterion 38.06199
Schwarz criterion 39.77743
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03.Variance Decomposition (Multiple Graph)
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Percent GRRGDP variance due to GRL
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Percent GRRGDP variance due to DINVEST
Variance Decomposition
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04.Granger Causality TestPair wise Granger Causality Tests
Date: 05/02/11 Time: 22:47
Sample: 1990 2008
Lags: 2
Null Hypothesis: Obs F-Statistic Prob.
GRRGDP does not Granger Cause GRL 17 3.81695 0.0521
GRL does not Granger Cause GRRGDP 0.06351 0.9388
GRFDI does not Granger Cause GRL 16 1.36654 0.2951
GRL does not Granger Cause GRFDI 1.00847 0.3961
TOT does not Granger Cause GRL 17 1.79098 0.2086
GRL does not Granger Cause TOT 0.37585 0.6945
DINVEST does not Granger Cause GRL 17 1.07134 0.3732
GRL does not Granger Cause DINVEST 0.88814 0.4368
GRFDI does not Granger Cause GRRGDP 16 0.09430 0.9107
GRRGDP does not Granger Cause GRFDI 0.60541 0.5631
TOT does not Granger Cause GRRGDP 17 1.51781 0.2584
GRRGDP does not Granger Cause TOT 0.04270 0.9583
DINVEST does not Granger Cause GRRGDP 17 0.71308 0.5098
GRRGDP does not Granger Cause DINVEST 5.94303 0.0161
TOT does not Granger Cause GRFDI 16 0.04848 0.9529
GRFDI does not Granger Cause TOT 0.02182 0.9785
DINVEST does not Granger Cause GRFDI 16 0.12103 0.8872GRFDI does not Granger Cause DINVEST 0.07149 0.9314
DINVEST does not Granger Cause TOT 17 1.26513 0.3173
TOT does not Granger Cause DINVEST 1.00747 0.3940