Download Can Foreign Direct Investments Influence Sri Lankan Economic

Can Foreign Direct Investments Influence Sri Lankan Economic Growth?
An Econometric Analysis
Deyshappriya N.P.R.
Faculty of Management
Uva Wellassa University
Sri Lanka
[email protected]
Abstract
According to the early economics literature, factor accumulation was the key component of
economic growth, but in this respect he recent economic history had highlighted additional
factors such as total factor productivity, international trade and foreign direct investment. As
a whole, the economic integration of developing countries has increased dramatically in
1990s, consequently most of the developing countries concern about the foreign direct
investment (FDI) as a tool of economic growth, since it provides more job opportunities,
technological transfers and foreign reserves in order to achieve a higher level of economic
growth. In the context of Sri Lanka, especially prior to 1970’s FDI was not seen as an
instrument of economic growth. The period of 1970-1977 was characterized by a highly
regulated economy although FDI was encouraged by the White paper in 1972, the climate for
such foreign investment or any private investments were not congenial. Since 1977, the
country has practiced the open economy policy, therefore has vigorously promoted foreign
capital inflows where FDI particulars are viewed as a necessary condition to accelerate the
growth. In this setting therefore, it has a timely importance to examine the effects of FDI on
economic growth.
In this study, I attempt to identify the relationship between FDI and Sri Lankan economic
growth. The current study is basically based on time series data during the period of 1990 –
2009 which have been collected from the various issues of Central Bank annual reports. In
accordance with the theory macroeconomics time series data follow the unit root process,
Augmented Dicky Fuler test was employed to check the stationary of the variables. After
checking the stationary of the variables by employing ADF test, the Vector Autoregressive
(VAR) model has been employed to identify the short run dynamics, followed by the Granger
Causality Test. According to the results, though FDI positively related to economic growth of
Sri Lanka, the magnitude of contribution is quite low compared to the other determinants of
economic growth. Hence, it is very crucial to provide and maintain the encouraging vicinity
for FDI, in order to enhance the contribution of FDI by getting the optimum benefit of the
FDI inflows.
Keywords: Foreign Direct Investment, Economic Growth, ADF Test, VAR Model, Granger
Causality Test
01. Introduction
01.1 Background of the study
Many policy makers and academics contend that foreign direct investment (FDI) can have
important positive effects on a host country‟s development effort. In addition to the direct
capital financing it supplies, FDI can be a source of valuable technology and knowledge
while fostering linkages with local firms, which can help jump-start the economic growth .
Based on these arguments, developed 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 inflows 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 a closed economic situation,
there were plenty of limitations for international trade and FDI. However, “White Paper”
which is presented in 1966 and foreign advisory committee that 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 in 1978, in order to pave the way to attract many 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 export income.
Therefore this study mainly focused on analyzing the effect of FDI on Sri Lankan economic
growth. According to the structure of the study, hereafter the reader can go through problem
statement, research objective, literature review, methodology, data analysis, results and
discussion and conclusions and recommendations.
01.2 Problem statement
However, 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 a developed category from the current situation of under
development. Consequently, it is doubtable whether the contribution of FDI on Sri Lankan
economic growth is significant. Therefore, it is worthwhile to create a clear picture about the
effect of FDI on Sri Lankan economic growth and hence more specifically, the research
question can be interpreted as follows;
Is FDI an important factor in explaining Sri Lankan economic growth?
For this purpose the current research paper, I have employed the number econometric tools in
order to quantify the question based relationship.
01.3 Objective of the study
In accordance with the research problem, the key objective of this research is, identifying 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 this unique
objective.
02. 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 has increased from about
US$ 200 billion in 1993 to US$ 1.3 trillion in 2000 (UNCTAD,2000). FDI with a rapid
growth has made researchers and government policy makers interested. Foreign Direct
Investment (FDI) is one form of capital flows which has 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
enterprise‟s 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, increasing of tax revenue, introducing of advanced management skills and
technologies, benefiting the 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 country‟s savings and investments, 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 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 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 „Granger cause‟ 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.
03. Methodology
03.1 Data
Since 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 were
considered.
03.2 Theoretical Model
In economic literature, Cobb-Douglas production function which has been established by
Charles Cobb and Paul Douglas in 1900–1928 provides extensive applications for growth
accounting. Since it is a more realistic production function, current study is engaged in this
production function in order to launch a solid theoretical background. Cobb- Douglas
production function can be interpreted as follows.
Y  AK  L1
In above function;
Y- Output level
A- Total Factor Productivity
K- Capital
L – Labour
α and (1- α) – Labour and Capital elasticity of output
Based on the Cobb Douglas production function, I developed 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.
GRRGDP   0GRDINV 1 GRL2 GRFDI 3 GRTOT 4
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
03.3 Estimation techniques
03.3.1 Unit Root Test
Before moving down to empirically estimated 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
X t    X t 1  t  i 1 i X t i   t
n
Where Xt can be real inward FDI, real exports and real GDP, t represents time, ξ t is random
error term, and n is the number of lag, selected in terms of Schwarz Criterion (SC). The null
hypothesis is δ = 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.
03.3.2 Vector Auto Regression (VAR) Model
In this effort to identify the relationship between economic growth and FDI, basically I had
intended to use Vector Auto Regression (VAR) model to identify the short run dynamics of
the mentioned realtionship based on the integratedness of the variables. Based on the
following theoretical VAR model, the model has been estimated by considering the all
explanatory variables. Here it considers only bi-variants model to explain the theoretical
based of VAR.
y1t    b11 y1t-1    b1q y1t-q  b 21 y 2t-1    b 2q y 2t-q  e y1t
y 2t    c11 y1t-1    c1q y1t-q  c 21 y 2t-1    c 2q y 2t-q  e y2t
Since the interpretation of coefficients of the VAR model is quite complex and meaningless
things, in accordance with the econometrics theories I used Impulse Response Function,
Variance Decomposition and Granger Casualty Test to evaluate the outcome of the VAR
model.
04. Results and Discussion
As mentioned 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.
04.1 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.
Figure – 01: Relationship between Real GDP and FDI
Source: Central Bank Annual Reports
It is apparent that both real GDP and FDI are illustrating an increasing trend over the time
even though FDI has shown a little bit of 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 influenced 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 on the
underlying relationship in the context of Sri Lanka, the total trade is indicating much more
fluctuating manner.
Figure – 02: Relationship between Total Trade and Economic Growth Rate
Source: Central Bank Annual Reports
According to the above graph, it is obvious that there is 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.
Figure – 03: Domestic Investment Ratio and Growth Rate of FDI
Source: Central Bank Annual Reports
It is obvious that domestic investment shows a smoothing pattern over the time; while growth
rate of FDI indicates fluctuate pattern as usually. Basically FDI inflows depend on various
factors including both domestic and international economic conditions. Consequently, FDI
can be identified as a more general and more capricious measurement compared to domestic
investments. 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.
04.2 Econometric Analysis
04.2.1 Results of the Unit Root Test
Before 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
Series
GRFDI
GRL
GRRGDP
GRDINVEST
GRTOT
Prob.
0.0065***
0.0001***
0.0158**
0.0295**
0.0096***
Lag
1
0
0
1
0
Max Lag
3
3
3
3
3
Obs
16
18
18
17
18
*** - 1% Significance level
** - 5%Significancelevel
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 is 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 is dealing with a 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.
04.2.2 VAR Model and Auto Regressive Root Graph
The coefficients of the VAR model are usually not going to be interpreted since it is a more
complex and meaningless task. However, I used three analytical tools to explain the VAR
output namely, Impulse Response Function (IRF), Variance Decomposition (VD) and
Granger Casualty Test (GCT). Apart from that, the stability of VAR estimation is quite
important. In that sense, the auto regressive root graph was utilized as follows.
Figure – 04: Auto Regressive Root Graph
Inverse Roots of AR Characteristic Polynomial
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
In accordance with the above graph, since all the variables are inside the circle, the estimated
VAR model has a higher level of stability to explain the short run dynamic of FDI on
economic growth.
04.2.3 Variance Decomposition Analysis
Variance decomposition decomposes the variance in an endogenous variable in to the
component when shocks are given to any 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
1
2
3
4
5
6
7
8
9
10
2.203661
2.725159
2.800075
2.822757
2.827174
2.827545
2.827584
2.827586
2.827586
2.827586
1.252729
1.206248
1.167074
1.165754
1.165268
1.165219
1.165219
1.165219
1.165219
1.165219
'_________
GRRGDP
__________
__________
98.74727
__________
93.07922
__________
92.49826
__________
92.40363
__________
92.39060
______
92.38993
92.38991
92.38990
92.38990
92.38990
GRFDI
TOT
DINVEST
0.000000
3.091372
3.022618
3.015655
3.014418
3.014289
3.014292
3.014294
3.014294
3.014294
0.000000
0.844502
1.185792
1.256422
1.268709
1.269493
1.269514
1.269516
1.269517
1.269518
0.000000
1.778662
2.126255
2.158538
2.161003
2.161068
2.161065
2.161071
2.161073
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 relative importance among the other endogenous variables, it accounts only for
quite low proportion. Consequently, the effect of the GRFDI on GRRGDP is considerably
low in the Sri Lankan economy.
04.2.4 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.
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 significant with respect to the
shocks of each endogenous variables. Even though the shock of GRFDI was unable to create
a statistically significant 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 will gradually
decrease and after 3 years of 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 significant.
Figure – 05: Impulse Response Functions
Response to Nonfactorized One S.D. Innovations ± 2 S.E.
Response of GRRGDP to GRL
Response of GRRGDP to GRRGDP
6
6
4
4
2
2
0
0
-2
-2
-4
-4
1
2
3
4
5
6
7
8
9
1
10
2
Response of GRRGDP to GRFDI
3
4
5
6
7
8
9
10
9
10
Response of GRRGDP to TOT
6
6
4
4
2
2
0
0
-2
-2
-4
-4
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
Response of GRRGDP to DINVEST
6
4
2
0
-2
-4
1
2
3
4
5
6
7
8
9
10
04.2.5 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 the lagged values of particular
variable that is related to subsequent values in another variable. Further, we need to keep
constant the lagged values of secondly mentioned variable and any other explanatory
variables. The results of the Granger Causality test can be summarized as follows.1
1
Refer the appendices for the full output
GRFDI does not Granger Cause GRRGDP
GRRGDP does not Granger Cause GRFDI
16
0.09430*
0.60541
0.9107
0.5631
* - Significance at 10 percent level
The results of Granger Causality Test imply that GRFDI Granger causes GRRGDP, however
this causality is significant only at 10% significance level and there is no reverse relationship
in between 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 an example Athukorala(2003). According to Athukorala (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 country is more open to the world, total
trade shows an 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 significant at 10% level. It is obvious that when the foreign companies
setup their business domestically, there should be a promotable infrastructure facilities and
stable financial system. Thus, in order to ensure the attraction of business vicinity, domestic
investment should be increased.
05. Conclusions and recommendations
This study attempted to quantify the relationship between FDI and economic growth of Sri
Lanka using VAR analysis. As a whole, even though GRFDI shows a 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 the economic growth. However, after three years of 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 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 currently working smoothly. In fact after finishing
the civil war situation, FDI inward has grown rapidly even if the promotable vicinity is not
present yet. Therefore, this study strongly recommends that to build and maintain supportable
infrastructure facilities along with a stability of economic condition in the country by
attracting FDI in order to achieve a higher economic growt h.
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International Business Studies, 35(4): 259-276.
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on Issues in International Investment Agreements
Appendices
01. Lag Selection Criteria of VAR
VAR Lag Order Selection Criteria
Endogenous variables: GRL GRRGDP GRFDI
TOT DINVEST
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
1
-306.4447
-288.5269
NA*
21.07973
1.02e+10*
3.02e+10
37.22879*
38.06199
37.71892*
39.77743
37.27751*
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
02. Re-estimation of VAR with selected lag length
Vector Autoregression Estimates
Date: 04/25/11 Time: 09:56
Sample (adjusted): 1991 2007
Included observations: 17 after adjustments
Standard errors in ( ) & t-statistics in [ ]
GRL(-1)
GRL
GRRGDP
GRFDI
TOT
DINVEST
-0.188427
-0.004981
-3.075031
-6.958239
-0.052814
(0.25790)
[-0.73061]
(0.35023)
[-0.01422]
(11.3149)
[-0.27177]
(12.6476)
[-0.55016]
(0.19659)
[-0.26865]
GRRGDP(-1)
-0.157946
(0.25901)
[-0.60979]
-0.176813
(0.35174)
[-0.50269]
-8.887991
(11.3636)
[-0.78215]
2.792415
(12.7021)
[ 0.21984]
0.497850
(0.19744)
[ 2.52152]
GRFDI(-1)
0.010269
(0.00740)
[ 1.38723]
0.004898
(0.01005)
[ 0.48728]
0.024353
(0.32476)
[ 0.07499]
0.007831
(0.36301)
[ 0.02157]
-0.003589
(0.00564)
[-0.63604]
TOT(-1)
-0.009409
(0.00754)
[-1.24779]
-5.61E-05
(0.01024)
[-0.00548]
0.080391
(0.33080)
[ 0.24302]
-0.070772
(0.36977)
[-0.19140]
0.003328
(0.00575)
[ 0.57906]
DINVEST(-1)
0.134220
(0.29323)
[ 0.45773]
-0.316694
(0.39820)
[-0.79532]
1.345447
(12.8646)
[ 0.10459]
-3.231800
(14.3799)
[-0.22474]
0.682953
(0.22352)
[ 3.05546]
C
-1.702748
(7.88097)
[-0.21606]
12.29321
(10.7022)
[ 1.14866]
71.72536
(345.757)
[ 0.20744]
48.74796
(386.483)
[ 0.12613]
6.103630
(6.00745)
[ 1.01601]
@TREND
0.100077
(0.12446)
[ 0.80409]
0.226718
(0.16901)
[ 1.34142]
-1.891894
(5.46033)
[-0.34648]
7.220164
(6.10349)
[ 1.18296]
-0.054734
(0.09487)
[-0.57692]
R-squared
Adj. R-squared
Sum sq. resids
S.E. equation
F-statistic
Log likelihood
Akaike AIC
Schwarz SC
Mean dependent
S.D. dependent
0.390179
0.024286
48.56123
2.203661
1.066374
-33.04366
4.711019
5.054107
1.522056
2.230918
0.191375
-0.293800
89.55192
2.992523
0.394446
-38.24560
5.323012
5.666100
5.735961
2.630898
0.110276
-0.423559
93469.89
96.67983
0.206573
-97.32550
12.27359
12.61668
39.81887
81.03043
0.161284
-0.341945
116786.1
108.0676
0.320498
-99.21848
12.49629
12.83938
37.17824
93.28849
0.642348
0.427757
28.21704
1.679793
2.993360
-28.42901
4.168119
4.511207
25.12941
2.220576
Determinant resid covariance (dof
adj.)
Determinant resid covariance
Log likelihood
Akaike information criterion
Schwarz criterion
5.39E+09
3.80E+08
-288.5269
38.06199
39.77743
03. Variance Decomposition (Multiple Graph)
Variance Decomposition
Percent GRRGDP variance due to GRL
Percent GRRGDP variance due to GRRGDP
100
100
80
80
60
60
40
40
20
20
0
0
1
2
3
4
5
6
7
8
9
1
10
Percent GRRGDP variance due to GRFDI
3
4
5
6
7
8
9
10
Percent GRRGDP variance due to TOT
100
100
80
80
60
60
40
40
20
20
0
2
0
1
2
3
4
5
6
7
8
9
10
Percent GRRGDP variance due to DINVEST
100
80
60
40
20
0
1
2
3
4
5
6
04. Granger Causality Test
Pair wise Granger Causality Tests
Date: 05/02/11 Time: 22:47
Sample: 1990 2008
Lags: 2
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Null Hypothesis:
Obs F-Statistic
Prob.
GRRGDP does not Granger Cause GRL
GRL does not Granger Cause GRRGDP
17
3.81695
0.06351
0.0521
0.9388
GRFDI does not Granger Cause GRL
GRL does not Granger Cause GRFDI
16
1.36654
1.00847
0.2951
0.3961
TOT does not Granger Cause GRL
GRL does not Granger Cause TOT
17
1.79098
0.37585
0.2086
0.6945
DINVEST does not Granger Cause GRL
GRL does not Granger Cause DINVEST
17
1.07134
0.88814
0.3732
0.4368
GRFDI does not Granger Cause GRRGDP
GRRGDP does not Granger Cause GRFDI
16
0.09430
0.60541
0.9107
0.5631
TOT does not Granger Cause GRRGDP
GRRGDP does not Granger Cause TOT
17
1.51781
0.04270
0.2584
0.9583
DINVEST does not Granger Cause
GRRGDP
17
GRRGDP does not Granger Cause DINVEST
0.71308
5.94303
0.5098
0.0161
TOT does not Granger Cause GRFDI
GRFDI does not Granger Cause TOT
16
0.04848
0.02182
0.9529
0.9785
DINVEST does not Granger Cause GRFDI
GRFDI does not Granger Cause DINVEST
16
0.12103
0.07149
0.8872
0.9314
DINVEST does not Granger Cause TOT
TOT does not Granger Cause DINVEST
17
1.26513
1.00747
0.3173
0.3940