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AN EMPIRICAL INVESTIGATION OF THE IMPACT OF FDI, EXPORT AND GROSS
DOMESTIC SAVINGS ON THE ECONOMIC GROWTH IN BANGLADESH
Md. Mamun Miah1
Shapan Chandra
Majumder2+
1,2Department of Economics, Comilla University, Cumilla, Bangladesh.
(+ Corresponding author)
ABSTRACT
Article History
Received: 6 August 2020
Revised: 10 September 2020
Accepted: 23 September 2020
Published: 8 October 2020
Keywords
ARDL BT
ECM
Bangladesh
Population growth
Domestic savings
FDI inflows
Export
Inflation.
JEL Classification:
C3, O4, O11, O43.
Bangladesh is a developing country with a huge population. So it is necessary to ensure
better economic performance of Bangladesh. The purpose of the paper is to empirically
investigate the impact of FDI, export, and gross domestic savings on the economic
growth of Bangladesh and also tries to show the impact of inflation, industry value-
added, and population growth on economic growth. We conduct the research with data
covering the year from 1972 to 2017. Autoregressive Distributed Lag Bound Testing
(ARDL BT) and Error Correction Model (ECM) are applied. The result of the ARDL
model shows that the coefficient of FDI is 0.05 indicating that if FDI rises 1% then
growth of the GDP will rise 0.05%. The coefficient of one year lag FDI is negative but
insignificant. Again 1% rise in exports leads 0.03% rise in growth. Gross domestic
savings positively affect GDP growth but statistically not significant. Inflation
negatively affects the economic growth of Bangladesh. If inflation decreases by 1% then
GDP growth will increase by 0.04%. Industry value added has positive effects on
growth, a 1% increase in Industry value-added leads to a significant increasing in
growth by 8.68%. Population growth negatively impacts economic growth. If the
growth of the population decreases by 1% then 1.88% will increase the growth. Long
run relation of the variables is ensured by the bound test and ECM-1 is significantly
negative and indicating that adjustment is corrected by 145%. Hypotheses testing
ensure except export other variables are short-run determinants of growth.
Contribution/Originality: This study contributes to the existing literature by showing the empirical
contribution of Export, FDI, Gross Domestic Savings, inflation, industry value-added, and population growth on
the Economic Growth in Bangladesh using ARDL ECM approach and also be beneficial for policymakers to take
necessary steps.
1. INTRODUCTION
Bangladesh is a country with a huge population in the world that fails to achieve its goals of development due
to political instability, corruption, lack of good governance. Bangladesh is a small country with an emerging
economy. After the liberation, the situation of this country was a beggar description. Besides, this country has to
fight natural calamities. As economic growth is made up of various factors, it's not possible to cover all of the
factors. Table 1 exposes the GDP growth in Bangladesh from 2010 to 2017. In 2010 it was 5.57% and continuously
increases. In 2015 it was 6.55% and in 2016 and 2017 it was respectively 7.11% and 7.28%.
The Economics and Finance Letters
2020 Vol. 7, No.2, pp. 255-267.
ISSN(e): 2312-430X
ISSN(p): 2312-6310
DOI: 10.18488/journal.29.2020.72.255.267
© 2020 Conscientia Beam. All Rights Reserved.
The Economics and Finance Letters, 2020, 7(2): 255-267
256
© 2020 Conscientia Beam. All Rights Reserved.
Table-1. GDP growth in Bangladesh from 2010 to 2017.
Year
GDP growth (annual %)
2010
5.571802274
2011
6.46438388
2012
6.521435078
2013
6.013610365
2014
6.061059359
2015
6.552652796
2016
7.113489474
2017
7.284184092
Source: World Bank (2019).
From Figure 1 we see that from 2010 to 2012 there was an increasing trend in exports but after then exports
decreases. In 2015 it was 17.34% and in 2017 it was 15.04% shown by the blue line. Whereas Gross Domestic
Savings continuously increases are shown by the red line.
Figure-1. The trend for exports and gross domestic savings in Bangladesh from 2010 to 2017.
Source: World Bank (2019).
Table 2 shows the data for the inflation rate and population growth in Bangladesh. In 2010 the inflation rate
was 7.144%. It has an increasing trend till 2012 after then decreases and in 2017 it was 6.27%. Population growth in
Bangladesh is approximately stable from 2010 to 2014 then decreases in 2017and it was 1.05%.
Table-2. Inflation and Population growth in Bangladesh from 2010 to 2017.
Year
Inflation, GDP deflator (annual %)
Population growth (annual %)
2010
7.14464873
1.119887888
2011
7.859446035
1.151949049
2012
8.164597746
1.17243498
2013
7.174948668
1.177318892
2014
5.668788528
1.157188028
2015
5.872764205
1.120144259
2016
6.72784119
1.080165239
2017
6.278683267
1.048898039
Source: World Bank (2019).
Table 3 shows the FDI (Foreign direct investment) inflows in Bangladesh from the fiscal year 2010 to 2018. In
2010 it was 913.09 million USD and gradually increases. But in 2014 it was decreasing compared to 2013. In 2015,
2016, 2017 and 2018 FDI inflows were $1833.87, $2003.53, $2454.81 and $2580.44 million.
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257
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Table-3. FDI Inflows from the fiscal year 2005 to 2018.
Fiscal year
FDI Inflows(in million USD)
2010
913.09
2011
779.04
2012
1194.88
2013
1730.63
2014
1438.49
2015
1833.87
2016
2003.53
2017
2454.81
2018
2580.44
Source: Bangladesh bank.
For any country, the policymakers should know very well for the possible factors of economic growth as they
are associated with the development of the country because if they know the possible factors they can respond and
can take initiative to boost countries well being. A developing country like Bangladesh it's also crucial to know the
potential determinants that have an impact on growth.
Many of the researcher's works with determinants of economic growth in Bangladesh like Ahamed and Tanin
(2010) explored that FDI is an important determinant of economic growth for Bangladesh. Sultan (2008) shows
export, import, and industry value added are important factors for growth. In this study we try to show the impact
of FDI, export, gross domestic savings, inflation, industry value added and population growth on economic growth
and also have a purpose for finding long and short-run determinants of economic growth among the variables.
Based on the data's nature we used the ARDL model for conducting this research. Where the result shows FDI,
exports, industry value added and population growth are important for GDP growth. And also found that long-run
determinants of GDP growth are exports where the rest of the variables are short-run determinants so it is
important to remove government ineffectiveness to increase FDI, exports, and industry value-added.
The paper consists of the following sections, where part 2 gives the problem statement of the study, section 3
contains the literature review, and the objectives of the paper are revealed in section 4. Significance of the study,
methodology, empirical results, and discussions, and finally conclusions and recommendations are described
through sections 5, 6, 7, and 8 respectively.
2. PROBLEM STATEMENT
With a huge population, Bangladesh is a developing country. So it is necessary to ensure better economic
performance for this country. For this, it is a prerequisite to know about the impact FDI, export and gross domestic
savings, inflation, industry value added, and population growth on economic growth. And it's also important to
empirically investigate the long and short-run determinants of economic growth.
3. LITERATURE REVIEW
There are enormous theoretical and empirical investigations on the topic and some of those are including in
table 4. We detect economic growth's determinants of various countries, where several determinants are selected. In
this paper, we try to find the potential impact of FDI, export, gross saving, inflation, industry value-added, and
population growth on economic growth in Bangladesh.
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© 2020 Conscientia Beam. All Rights Reserved.
Table-4. Summary of Literature Review.
Author(s)
Country and Sample
Methodology
Findings
Chirwa and Odhiambo
(2016)
Both developing and
developed countries
Various
econometric
methods
For both developing and developed
countries, the important
determinants of economic growth
are fiscal policy, trade, human
capital, demographics, and monetary
policy.
Sultan (2008)
Bangladesh;
1965-2004
OLS Regression;
Multivariate and
Vivariate
Cointegration
Test; Causality
Test
Industry value added has long-run
impacts on GDP.
Kasidi and
Mwakanemela (2013)
Tanzania; 1990 -2011
The linear
regression
equation and
Cointegration test
Inflation negatively affects economic
growth and no cointegration is found
between them.
Chowdhury and Hossain
(2018)
Bangladesh;
1979-2017
Using different
preventive checks
Inverse relation exists with
economic development and
population growth.
Klasen and Lawson
(2007)
Uganda
Panel Data
Analysis
Growth of population paused per
capita growth.
Anaman (2004)
Brunei,
1971-2001
ARDL Model
Growth of export and size of
government influence long-run
growth rates.
Behname (2012)
Southern Asia; 1977-
2009
Panel Data
Analysis
FDI is a crucial determinant of
growth where human capital, capital
formation, and infrastructure are
positively related to economic
growth and population, technology
gap and inflation are negatively in
Southern Asia.
Sun and Heshmati
(2010)
China;
2002 to 2007
Non-parametric
Approach
Trade volume and Trade structure
positively accelerate regional
productivity.
Baiashvili and Gattini
(2020)
111 countries of low -,
middle - and high -
income countries
Panel GMM
Technique
Income levels and FDI have a U
shaped relationship.
Majumder and Rana
(2016)
Bangladesh,
OLS
Export and GDP per capita are
mostly influenced components of
economic growth in the country.
Fetahi-Vehapi, Sadiku,
and Petkovski (2015)
European
countries(South East);
1996 to 2012
Panel GMM
Technique
Trade openness, FDI, and Human
capital positively influence economic
growth.
Fitzová and Zídek
(2015)
Czech and Slovak
Republics
Cointegration,
VECM, and
Granger
Causalities
Exports and economic growth are
positively related.
Moudatsou (2003)
European Union (EU)
countries
Panel Data
Analysis
FDI is a positive determinant of
growth rate.
Anyanwu (2014)
Africa;
1996 to 2010
Two‐step Least
2SLS and
Two‐stage
Efficient
Generalized
Method of
Moments.
Openness does not have a positive
impact on growth.
Har, Teo, and Yee
(2008)
Malaysia;
OLS Regressions
Economic growth and FDI inflows
have a significant relationship and
The Economics and Finance Letters, 2020, 7(2): 255-267
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© 2020 Conscientia Beam. All Rights Reserved.
also for FDI.
Dinh, Vo, and Nguyen
(2019)
Developing Countries;
2000–2014
VECM and
FMOLS
FDI accelerates growth both for the
long run short-run and money
supply, domestic investment and
Domestic credit are important long-
run economic determinant.
Dao (2012)
Forty-three Developing
Economies
Multivariate linear
regression
Population growth influenced GDP
Per capita.
Saaed (2007)
Kuwait;
1985 to 2005
Co-integration and
ECM
Inflation and economic growth have
negative relation.
Majumder. (2016)
Bangladesh,
1975-2013
VECM Approach
Inflation and economic growth have
positive long-run relation.
Ahamed and Tanin
(2010)
Bangladesh; 1975- 2006
2SLS Procedure
FDI positively impacts the growth of
the economy.
Ali and Saif (2017)
Pakistan; 1976-2015
Maximum
Likelihood
Estimation
Approach; VECM;
Granger Causality
Agriculture, energy consumption,
trade liberalization, and FDI have a
positive influence on GDP.
Chizonde (2016)
Zambia
ARDL Approach
Physical capital, Exchange rate,
inflation, price of crude oil is long-
run economic determinants.
Darko (2015)
Ghana; 1975-2013
Vector
Autoregressive
Model
GDP per capita depends on export,
oil, and mineral rents.
Ghazanchyan, Stotsky,
and Zhang (2015)
Asian countries; 1980 to
2012
Panel Data
Analysis
Private and Public investments
strongly influence growth but the
exchange rate does not.
Qadri and Waheed
(2011)
Pakistan; 1978 to 2007
The Standard
Cobb
Douglas
Production
Function,
Sensitivity
Analysis
ARDL Model
Human capital positively influences
economic growth.
Majumder and Donghui
(2016)
Bangladesh,
1975-2013
There is a long-run significant
relationship between remittances and
economic growth in the country.
Simionescu, Lazanyi,
Sopkova, Dobeš, and
Balcerzak (2017)
V4 Countries; 2003-
2016
Bayesian
Generalized Ridge
Regression
FDI promotes economic growth.
The expenditure on education
generates economic growth.
Tridico (2008)
Emerging and
Transition Economies;
1999-2005
OLS Regression
Analysis
Human capital and Export capacity
are important for economic growth.
4. OBJECTIVES OF THE STUDY
The main objective of this research is the empirical investigation of the impact of FDI, Export and Gross
Domestic Savings on the Economic Growth in Bangladesh.
Where specific objectives are the following:
i. To find out the current situation of FDI inflows, Export, Inflation, Growth of population, and gross
domestic savings in Bangladesh.
ii. To reveal the long and short-run determinants of growth in Bangladesh.
5. SIGNIFICANCE OF THE STUDY
Detecting all of the determinants that have an impact on economic growth is not easy as it consists of various
factors. Here we try to briefly discuss some of them. This study helps in finding the influence of selected variables
on growth by the Autoregressive Distributed Lag Bound Testing (ARDL BT) approach and also helps in finding
both short and long-run determinants of growth in Bangladesh. This paper may be helpful for existing literature.
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© 2020 Conscientia Beam. All Rights Reserved.
6. METHODOLOGY
6.1. Data and Sample
We conduct this research with the data covering the year from 1972 to 2017 where the secondary data is
collected from WDI (World Bank, 2019) .
6.2. Model Specification
The final econometric model is provided below by the equation (2),
………… (1)
By taking natural logarithm the final econometric model is
…......... (2)
Here, =GDP growth (annual %), = GDP growth(One year lag), =FDI, net inflows
(current US$), = FDI(One year lag), =Exports of goods and services (% of GDP), =Gross
domestic savings (%of GDP), =Inflation,(annual %), = One year lag of inflation, =Industry
value added (constant 2010 US$), = One year lag of Industry value-added, =Population
growth, = One year lag of Population growth.
6.3. Hypotheses of the Study
H1: FDI positively affects economic growth.
H2: Exports positively affects economic growth.
H3: Gross domestic savings positively affects economic growth.
H4: Inflation negatively affects economic growth.
H5: Industry value added positively affects economic growth.
H6: Population growth negatively affects economic growth.
7. EMPIRICAL RESULTS AND DISCUSSION
7.1. Unit Root Test
Table-5. Augmented Dickey-Fuller Test (ADF).
Variable
Level
1st difference
Decision
t-statistics
t-statistics
lnGDPG
-4.291117***
-2.666190*
I(0)
lnFDI
-6.170034***
-5.163381***
I(0)
lnExp
-3.566072 **
-7.497741 ***
I(0)
lnInf
-3.112398**
-9.870831***
I(0)
lnGDS
-0.859094
-8.430113***
I(1)
lnIVA
2.362661
-10.07564***
I(1)
lnPG
-1.854644*
-2.695684*
I(0)
Note: 10%, 5%, and 1% level of significance are denoted with *, **, ***.
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Augmented Dickey-Fuller test is used to test the stationarity. The results are presented below in table 5 where
lnGDPG, lnFDI, lnExp, lnInf, lnPG is stationary at I(0); whereas the data of lnGDS and lnIVA is stationary at I(1)
at 1, 5 and10 percent significance level.
7.2. Lag -Length Criteria
As all of our data is stationary at mixed order we can apply Autoregressive Distributed Lag Model for the
study developed by Pesaran and Shin (1999). From automatic lag selection criteria by SIC, we found appropriate lag
2 which are presented in Appendix Table 1.
7.3. Top of Form Bottom of Form ARDL Model
Table 6 shows the ARDL Model, where the negative coefficient of one-year lag GDP growth is significant. The
coefficient of FDI is 0.05 refers to a 1% increase in it then growth will increase by 0.05%. The coefficient of one year
lag FDI is negative but insignificant.
Exports positively affect GDP growth. From the model 1% increases in exports leads 0.03% increase in GDP
growth. Gross domestic savings also positively affect GDP growth but statistically not significant. Inflation
negatively affects the economic growth of Bangladesh. If inflation goes down by 1% then GDP growth will increase
by 0.04%.
Industry value added positively affects economic growth.1% increase in IVA leads to an 8.68% increase in GDP
growth and significant at 1percent level. Population growth negatively accelerates the growth of the economy; a 1%
decrease in it then GDP growth will increase by 1.88%.
Table-6. ARDL Model Estimates.
Variable
Coefficients
Standard error
Probability
lnGDPGt-1
-0.448984
0.060153
0.0000
lnFDIt
0.048847
0.020244
0.0242
lnFDIt-1
-0.033729
0.019976
0.1048
lnExpt
0.313499
0.133422
0.0277
lnGDS
0.072598
0.125246
0.5678
lnInft
-0.036804
0.028102
0.2032
lnInft-1
0.047606
0.027926
0.1017
lnIVAt
8.680891
1.438476
0.0000
lnIVAt-1
-8.821380
1.506027
0.0000
lnPGt
-1.879197
0.633603
0.0069
lnPGt-1
1.527422
0.682103
0.0351
Constant
1.677531
1.385177
0.2382
R square
Adjusted R square
Durbin Watson value 0.927916
0.893441
1.551148
Note: 10, 5, and 1% significance level are denoted with *, **, ***.
Table 7 shows the diagnostic tests of the ARDL model; ensures the normal distribution of data as the Jarque-
Bera probability value is 0.725. No autocorrelation and heteroscedasticity are detected in the model as the p-value of
serial correlation and heteroscedasticity tests are 0.218 and 0.11 respectively.
Table-7. Diagnostic test.
Test
Test statistic
P-Value
Normality Test Jarque –Bera
J-B=0.643
0.725
Autocorrelation Breusch-Godfrey LM
F value=1.639
0.218
Heteroskedasticity Breusch-Pagan-Godfrey LM test
F value=1.839
0.110
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7.4. ARDL Bound Testing
From table 8, the value 86.62 of ARDL BT estimation result at a 1 percent significance level admits that there
exists a long-run relationship among the selected variables.
Table-8. ARDL BT estimation result.
K
F-stat
Significant
Lower bound, I(0)
Upper bound, I(1)
10%
2.12
3.23
6
86.62
5%
2.45
3.61
2.5%
2.75
3.99
1%
3.15
4.43
7.5. Cointegration form of ARDL Model
From table 9, the short-run analyses of the model we see that the coefficient of ∆lnFDI, ∆ lnExp, ∆ lnGDS, and
∆ lnIVA are positively significant at 1% and 5% level, meaning that all of these variables positively accelerate the
economic growth of Bangladesh in the short run. Where the coefficients of ∆ lnInf and ∆ lnPG are negative meaning
that in the short-run these variables have a negative impact on economic growth.
ECMt-1 is negative and significant refers that adjustment is corrected by 145% from short to long run.
Table-9. ARDL- ECM model.
Variable
Coefficient
Standard error
Probability value
∆lnFDIt
0.048847
0.020244
0.0242**
∆ lnExpt
0.313499
0.133422
0.0277**
∆ lnGDSt
0.072598
0.125246
0.5678
∆ lnInft
-0.036804
0.028102
0.2032
∆ lnIVAt
8.680891
1.438476
0.0000***
∆ lnPG
-1.879197
0.633603
0.0069***
ECMt-1
-1.448984
0.060153
0.0000***
Note: 10%, 5%, and 1% level of significance are denoted with *, **, ***.
Table-10.
Long run coefficient.
Variable
Coefficient
Standard error
Probability value
lnFDI
0.010434
0.014396
0.4759
lnExp
0.216358
0.091558
0.0270**
lnGDS
0.050103
0.086927
0.5700
lnInf
0.007455
0.023245
0.7513
lnIVA
-0.096957
0.102230
0.3528
lnPG
-0.242774
0.182814
0.1972
Constant
1.157729
0.965429
0.2427
Note: 10%, 5%, and 1% level of significance are denoted with *, **, ***.
7.6. Long Run Coefficient of ARDL model
Table 10 shows the long-run coefficient of the ARDL model where FDI, Exports, Gross domestic savings;
Inflation has a positive coefficient, and Industry value-added and Population growth have negative coefficients.
7.7. Determinants of Economic Growth
For finding the determinants of growth we notice Table 11, we see that hypothesis H1 is rejected; meaning that
FDI isn't a long run rather a short run determinant and significant at a 5 % level. Hypotheses H2 is accepted as
both the t statistics are significant in both the short and long run at a 5% level. Where 1% increases in export will
boost 0.22% of GDP growth in Bangladesh. This evidence is empirically proved by Fitzová and Zídek (2015).
As hypotheses, H3 is rejected for both two terms. H4 is also rejected in the same way.H5 shows that industry
value added is a short run determinant rather than long run.H6 is also rejected where population growth is a short -
run determinant.
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Table-11. Determinants of economic growth.
Hypotheses
T statistics
Decision
Short-run
Long run
H1: FDI positively affects Economic Growth
2.412908**
0.724786
Rejected H1
H2: Exports positively affects Economic Growth
2.349689**
2.363066**
Accepted H2
H3: Gross domestic savings positively affect Economic
Growth
0.579642
0.576374
Rejected H3
H4: Inflation negatively affects Economic Growth
-1.309655
0.320707
Rejected H4
H5: Industry value added positively affects Economic
Growth
6.034783***
-0.948416
Rejected H5
H6: Population growth negatively affects Economic Growth
-2.965892***
-1.327984
Rejected H6
Note: 10%, 5%, and 1% significance level are denoted by *, **, ***.
7.8. Stability Test
The stability test of the model is proved through the CUSUM and CUSUM squares test and shows our ECM-
ARDL model is stable. The results are given in figure 2(a) and 2(b); where the color of the blue line doesn’t cross
the red line. So we can say in the long run this model is stable.
-15
-10
-5
0
5
10
15
96 98 00 02 04 06 08 10 12 14 16
CUSUM 5% Significance
Figure-2(a). Stability Checking by CUSUM test.
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
96 98 00 02 04 06 08 10 12 14 16
CUSUM of Squares 5% Significance
Figure-2(b). Stability Checking by CUSUMSQ Test.
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7.9. Granger Causality Test
Pairwise Granger Causality Test is presented in Appendix Table 2 shows no causal relation between lnGDPG
and lnInf; lnFDI and lnGDS; lnFDI and lnInf; lnFDI and lnIVA; lnExp and lnGDS; lnGDS and lnInf; lnGDS and
lnPG. Unidirectional causality is found between lnGDPG and lnFDI; lnPG and lnGDPG; lnFDI and lnPG; lnGDS
and lnExp; lnInf and lnExp; lnEx and lnPG; lnIVA and lnGDS; lnInf and lnIVA; lnIVA and lnPG. A bidirectional
causal relation is found between lnGDS and lnGDPG; lnExports and lnGDPG; lnIVA and lnGDPG; lnPG and
lnGDPG.
8. CONCLUSION AND POLICY RECOMMENDATION
The empirical investigation for finding the influence of FDI, export, and gross domestic savings on economic
growth is essential for any country. In this paper, we analyze that empirical investigation in Bangladesh covering
the year 1972 to 2017. Secondary data is drawn from World Bank (2019).
We apply ARDL BT and ECM ARDL BT test. Our selected variables are GDP growth, FDI, Exports, Gross
domestic savings, Inflation, Industry value-added, and Population growth.
The result of the ARDL model shows the negative coefficient of one-year lag GDP growth. The GDP growth
will rise by 0.05% if FDI increases by 1%. The coefficient of one year lag FDI is negative but insignificant. Exports
positively affect GDP growth. From the model, if the increase in exports is 1% then GDP growth will increase by
0.03%. Gross domestic savings also positively affect GDP growth but statistically not significant. Infla tion
negatively affects the economic growth of Bangladesh. If inflation goes down by 1% then GDP growth will increase
by 0.04%. Industry value added positively affects economic growth. 1% increase in Industry value-added leads an
8.68% increase in GDP growth. The growth of the population has negative impacts on economic growth. If it
decreases 1%, 1.88% will be GDP growth. Durbin Watson's value is 1.55.
ARDL bound testing approach shows a long-run association among variables. The ECMt-1 is negative and
significant indicating that adjustment will be corrected by 145% from short to long run. Hypotheses testing ensure
except exports other determinants are short-run determinants. Cusum and Cusum squares test ensures the stability
of this ARDL model.
Industrial goods and technology importing may accelerate the growth of the industry in Bangladesh. As export
is an important determinant in Bangladesh it is urgent to have a look at exporting. In this case, export policy and
export incentives will be helpful.
As inflation negatively impacts growth, policymakers should focus on this is issue to maintaining a low rate of
inflation. Population growth should be checked to boost economic growth. In this case, female education can
contribute a lot. Besides they have to make self-sufficient.
Funding: This study received no specific financial support.
Competing Interests: The authors declare that they have no competing interests.
Acknowledgement: All authors contributed equally to the conception and design of the study.
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APPENDICES
Appendix Table-1. Optimum Lag Selection Model.
Lag
LogL
LR
FPE
AIC
SC
HQ
0
171.0979
NA
8.29e-14
-10.25612
-9.935492
-10.14984
1
421.1312
375.0498
3.12e-19
-22.82070
-20.25566
-21.97046
2
516.9354
101.7920*
2.71e-20*
-25.74596*
-20.93651*
-24.15176*
Note:* 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.
Appendix Table-2. Results of granger causality tests.
Granger Causality Tests
Null Hypothesis:
Obs
F-Statistic
Prob.
LNFDI does not granger cause LNGDPG
33
1.51946
0.2363
LNGDPG does not granger cause LNFDI
2.57438
0.0941
LNEXP does not granger cause LNGDPG
40
10.4701
0.0003
LNGDPG does not granger cause LNEXP
2.28626
0.1166
LNGDS does not granger Cause LNGDPG
39
7.41011
0.0021
LNGDPG does not granger cause LNGDS
7.47371
0.0020
LNINF does not granger cause LNGDPG
38
1.53979
0.2294
LNGDPG does not granger cause LNINF
0.18893
0.8287
LNIVA does not granger cause LNGDPG
40
12.2854
9.E-05
LNGDPG does not granger cause LNIVA
5.60132
0.0078
LNPG does not granger cause LNGDPG
40
11.7673
0.0001
LNGDPG does not granger cause LNPG
0.50727
0.6065
LNEXP does not granger cause LNFDI
37
3.09352
0.0591
LNFDI does not granger cause LNEXP
3.19295
0.0544
LNGDS does not granger cause LNFDI
33
0.32502
0.7252
LNFDI does not granger cause LNGDS
2.08130
0.1436
LNINF does not granger cause LNFDI
34
0.10134
0.9039
LNFDI does not granger cause LNINF
0.55537
0.5798
LNIVA does not granger cause LNFDI
37
2.07752
0.1418
LNFDI does not granger cause LNIVA
1.25213
0.2995
LNPG does not granger cause LNFDI
37
1.29927
0.2867
LNFDI does not granger gause LNPG
21.8609
1.E-06
LNGDS does not granger cause LNEXP
40
2.42467
0.1032
LNEXP does not granger cause LNGDS
1.87199
0.1689
LNINF does not granger cause LNEXP
40
4.64417
0.0163
LNEXP does not granger cause LNINF
0.90063
0.4155
LNIVA does not granger cause LNEXP
44
13.8328
3.E-05
LNEXP does not granger cause LNIVA
3.85900
0.0296
LNPG does not granger cause LNEXP
44
0.93162
0.4025
LNEXP does not granger cause LNPG
15.9085
9.E-06
LNINF does not granger cause LNGDS
39
0.36631
0.6960
The Economics and Finance Letters, 2020, 7(2): 255-267
267
© 2020 Conscientia Beam. All Rights Reserved.
LNGDS does not granger cause LNINF
2.26809
0.1190
LNIVA does not granger cause LNGDS
40
10.0985
0.0003
LNGDS does not granger cause LNIVA
1.38620
0.2634
LNPG does not granger cause LNGDS
40
1.43661
0.2514
LNGDS does not granger cause LNPG
2.02681
0.1469
LNIVA does not granger cause LNINF
40
2.32500
0.1127
LNINF does not granger cause LNIVA
5.61251
0.0077
LNPG does not granger cause LNINF
40
0.61634
0.5457
LNINF does not granger cause LNPG
4.53529
0.0177
LNPG does not granger cause LNIVA
44
0.10696
0.8988
LNIVA does not granger cause LNPG
47.9784
3.E-11
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