


































DEM_2019_29to40


© 2019 Nicolaus Copernicus University. All rights reserved.  
http://www.dem.umk.pl/dem 

D Y N A M I C  E C O N O M E T R I C  M O D E L S   
DOI: http://dx.doi.org/10.12775/DEM.2019.002  Vol. 19 (2019) 29−40 

Submitted September 11, 2019  ISSN (online) 2450-7067 
Accepted December 17, 2019 ISSN (print) 1234-3862 

Mitra Lal Devkota* 

 

Impact of Export and Import on Economic Growth: 
Time Series Evidence from India 

A b s t r a c t. This paper examines the cointegration and causal relationships between export, 
import, and economic growth in India using quarterly data from 1996:Q2 to 2019:Q2. Station-
arity properties of the time series data are investigated using Augmented Dickey Fuller (ADF) 
and Phillips-Perron (PP) unit root tests, and the existence of cointegrating relationship is studied 
using Johansen’s cointegration test. Finally, the causal relationships between the variables are 
examined using Vector Error Correction Model (VECM). The results show that, under both 
tests, the time series variables are non-stationary at their levels and are stationary at their first 
differences. The Johansen’s cointegration test shows the existence of a long run equilibrium 
relationship among the variables. The results from the VECM indicate that there is a unidirec-
tional causal relationship running from economic growth to import in India. This implies that 
with an increase in the income of the nation, the nation’s spending will increase, and some of 
the spending will be on import.  
K e y w o r d s: Export, Import, Gross Domestic Product, Causality, Cointegration, India  

J E L Classification: C22; E00; E44 

Introduction 
 Over the past several decades, there has been a great debate among the 
researchers on three hypotheses in trade and development literature. Some 
empirical studies (Michaely, 1977; Bhagwati, 1978; Balassa, 1978; Tang et 

 
*Correspondence to Mitra Lal Devkota, University of North Georgia, Department of Man-

agement and Marketing, Mike Cottrell College of Business, 82 College Circle, Dahlonega, GA 
30597, United States, e-mail: mldevkota@ung.edu 



Mitra Lal Devkota 

DYNAMIC ECONOMETRIC MODELS 19 (2019) 29–40 

30 

al., 2015, among others) argue in favor of Exports Led Growth (ELG) hypoth-
esis, which states that exports make a significant contribution to economic 
growth of an economy (i.e. the flow of causality is from exports to economic 
growth). They claim that the countries exporting a large amount of their pro-
duction grow more rapidly than others do.  A number of empirical studies 
(Krugman, 1984; Bhagwati, 1988; Oxley, 1993; Sharma and Dhakal, 1994; 
Ghatak and Price, 1997, among others) have shown the possibility of Growth 
Led Exports (GLE) in which the flow of causality is from economic growth 
to exports. The third alternative is that of Imports Led Growth (ILG) in which 
the flow of causality is from imports to the economic growth (Awokuse, 
2008). Growth in imports can help in transfer of growth, enhancing foreign 
trade research and development knowledge from developed to the developing 
countries (Mazumdar, 2001). However, the flow of causality depends on the 
time period of the data under study, frequency of the data such as monthly, 
quarterly, and yearly, and the methodology employed in the study.  
  This paper differs from the existing literature on the study of cointegration 
and causality relationships between export, import, and economic growth in 
India in several ways. First, the existing studies have used either annual or 
monthly time series data covering comparatively a shorter period of time 
(Guntukula, 2018; Jyoti Kumari, 2014). We have used quarterly time series 
data that cover a longer period of time period and include recent data (1996:Q2 
to 2019:Q2). The study not including the recent data will not reflect the current 
economic development of the country. Second, most of these studies suffer 
from some weaknesses in the adopted methodologies. For instance, the Aug-
mented Dickey Fuller (ADF) method used for testing the stationarity of the 
time series data is widely criticized for its low power and size properties. We 
have supplemented this by including Phillips Perron unit root test in addition 
to the ADF test. Phillips-Perron test is nonparametric, i.e., it does not require 
one to select the level of serial correlation as in ADF. In addition, the Phillips-
Perron test is robust to general forms of heteroscedasticity in the error term 
and the user does not have to specify a lag length for the test in regression. 
Finally, to the best of our knowledge, no previous studies have employed the 
Granger causality test based on VECM framework to examine both short and 
long run causality  relationships between export, import, and the economic 
growth in India.1 We tested the long run causality between the variables 

 
1 Some studies such as Mishra (2012) and Malhotra and Meenu (2009), among others, only 

study the impact of import on GDP, but exclude the impact of export on GDP. On the other 
hand, studies such as Guntukula (2018) study the impact of export and import on GDP, but fail 
to examine both short- and long-run causal relationships between the variables under VECM 
framework.  



 Impact of Export and Import on Economic Growth: Time Series Evidence… 

 

DYNAMIC ECONOMETRIC MODELS 19 (2019) 29–40 

31 

through the statistical significance of the error correction term (ECT) by a t-
test. On the other hand, we tested the short run causality through the signifi-
cance of the lags of each explanatory variable by a Wald chi-squared test. 
 The rest of the paper is organized as follows. A review of previous empir-
ical studies is carried out in section 1. A detailed description of the data and 
the variables used in the study are presented in section 2. The econometric 
methodology used in the study and discussion of the empirical results are pre-
sented in section 3. The last section concludes the paper. 

1. Literature Review 
 A number of time series and cross-sectional methodologies have been 
used for testing the causality and cointegration relationships between export, 
import, and economic growth.  These studies have used data from both devel-
oped and developing countries. In this section, we review a selected number 
of empirical studies from a plethora of research articles in the area.  
 Alam, Uddin, and Taufique (2009) investigated the existence of gravity 
theory for the import of Bangladesh with its eight major trading partner coun-
tries India, China, Singapore, Japan, Hong Kong, South Korea, USA, and Ma-
laysia using data from 1985 to 2003 in panel approach. They found that the 
gravity theory is consistent with the imports of Bangladesh, that is, the geo-
graphical distance of Bangladesh with its partner countries has significant im-
pacts on its imports. They further document that the GDP of the major partner 
countries had significant positive impact on the import of Bangladesh, and 
that the population of these countries has a mixed impact on imports of Bang-
ladesh. 
 Din (2004) investigated the export-led growth hypothesis for the five larg-
est economies-Nepal, Bangladesh, India, Sri Lanka, and Pakistan of the South 
Asian region using a multivariate time-series framework. The author finds an 
interesting result in view of their increasing outward orientation and adoption 
of export promotion policies as part of their growth strategies in these South 
Asian countries. While controlling for imports, the results showed a bidirec-
tional causality between exports and output growth in Bangladesh, India, and 
Sri Lanka in the short-run. The study also found long-run equilibrium rela-
tionships between exports, imports, and output growth for Bangladesh and 
Pakistan. However, the study found no evidence of a long-run relationship 
between the variables for India, Nepal, and Sri Lanka.  
 Kogid et. al (2011) investigated the causality and cointegration relation-
ships between the economic growth and the import in Malaysia using annual 



Mitra Lal Devkota 

DYNAMIC ECONOMETRIC MODELS 19 (2019) 29–40 

32 

time series data from 1970 to 2007. Their results show no evidence of a coin-
tegrating relationship between economic growth and import in Malaysia. 
However, they find the evidence of a bidirectional causality between the eco-
nomic growth and import. They further document that import could indirectly 
contribute to economic growth, and economic growth could directly contrib-
ute to import.  
 Tagavhi (2012) investigated the relationship between export, import, and 
economic growth in Iran for the period between 1962 and 2011. The empirical 
results show the existence of a long run relationship between the variables 
under consideration. The results further show that, in the long run, export had 
positive relationship with economic growth, and import had a negative rela-
tionship with economic growth.  
 Guntukula (2018) investigated the relationships between export, import, 
and economic growth in India using monthly time series data from April 2005 
to March 2017. The study found a long run relationship between export, im-
port, and economic growth for the study period. The study also found the ev-
idence of a bidirectional causality between exports and economic growth, and 
between imports and economic growth, confirming the existence of a bidirec-
tional causality between exports and economic growth. Similarly, Jyoti Ku-
mari (2014) investigated the relationship between export, import, and eco-
nomic growth using annual data for India for the period post liberalization 
from 1991–92 to 2012–13. The author found that import must be supported 
by export to have continuous growth in the economy. She further documents 
that export has positive effect on economic growth by keeping import fixed, 
while import has negative effect on economic growth by keeping export fixed. 
 Ramos (2001) investigated the Granger causality between exports, im-
ports, and economic growth in Portugal using annual data over the period 
1865–1998. The empirical investigation finds no evidence of a unidirectional 
causality relation between export, import, and output growth. They found the 
evidence of feedback relationships between export and output growth and im-
port and output growth. But they found no significant causality between im-
port and export growth.  

2. Data 
 Our analysis uses the seasonally adjusted quarterly time series data for 
India for the period between 1996:Q2 to 2019:Q2. The variables used in the 
study are real export, real import, and real GDP in local currency (i.e. Indian 
rupees), which are then expressed in natural logarithms. We have transformed 



 Impact of Export and Import on Economic Growth: Time Series Evidence… 

 

DYNAMIC ECONOMETRIC MODELS 19 (2019) 29–40 

33 

the export, import and GDP into natural logarithms because this transfor-
mation is the most commonly used variance stabilizing tool for variables that 
have wide range (Weisberg, 1980). We take the gross domestic product (GDP) 
as the proxy for economic growth because GDP is essentially an indicator of 
aggregate economic activity of a country. These data are obtained from Eco-
nomic research database of Federal Reserve Bank of St. Louis. Statistical soft-
ware packages R and EViews are used for the econometric analyses of the 
data.  
 

 
 

Figure 1. Export, Import and GDP for India:1996–2019 

Table 1. Summary Statistics for export, import, and GDP 
 Export Import GDP 

Min 26.61 26.61 28.84 
1st Qu 27.34 27.41 29.41 

Median 28.61 28.77 30.16 
Mean 28.44 28.58 30.18 
3rd Qu 29.55 29.66 30.95 
Max 29.91 30.09 31.54 

Std. Dev 
Skewness 
Kurtosis 

Jarque-Bera p-value 

1.078 
–0.262 
1.617 
0.014 

1.107 
0.063 
1.648 
0.028 

0.836 
–0.268 
1.571 
0.011 

 Time series plots for the logarithmic values of export, import and GDP are 
shown in figure 1. The plot indicates that the time series data for each of the 
variables have fairly strong upward trends. This gives anecdotal evidence that 

26

27

28

29

30

31

32

96 98 00 02 04 06 08 10 12 14 16 18

Export GDP Import 



Mitra Lal Devkota 

DYNAMIC ECONOMETRIC MODELS 19 (2019) 29–40 

34 

each of the time series data tend to move together. Summary statistics for ex-
port, import, and GDP indicate that these variables have means equal to 28.44, 
28.58, 30.18 with associated standard deviations of 1.078, 1.107, 0.836, and 
coefficient of variation 0.0379, 0.0387, 0.0277 respectively. The Jarque-Bera 
test statistic for each variable has a p-value greater than 0.01. This implies that 
each of the variables’ distributions are normal. This study includes the maxi-
mum range of the time series data available to the author available at the time 
of analysis. 

3. Methodology and Empirical Results 
 Existence of cointegrating relationships and the directions of causalities 
(if any) between export, import and GDP can be decided only by empirical 
investigation. As such, we first study the time series properties of the data by 
using Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) unit root 
tests. We then use Johansen’s cointegration test to examine the existence of 
long run equilibrium relationship between the variables and to find the number 
of cointegrating vectors. Finally, we study the direction of causality and model 
the short and long run causal relationships between the variables by using 
Granger causality test under vector error correction model (VECM) frame-
work. 

3.1. Unit Root Test for Testing Stationarity  

 The initial step in the time series data analysis involves testing the pres-
ence of unit root of each of the variables. For this purpose, we have used Aug-
mented Dickey Fuller unit root test (Dickey and Fuller; 1979, 1981) and Phil-
lips-Perron unit root test (Phillips and Perron, 1988) at the levels and first dif-
ferences of the variables.  

Table 2.  Unit Root Test Results 

Variable 
ADF-Test Phillips-Perron Test 

Levels First Difference Levels First Difference 
GDP 0.25	[0.975] −8.01				[0]* 0.17	[0.970] −8.17 [0]* 

Export −1.22		[0.662] −10.66	[0]* −1.38	[0.589] −10.74 [0]* 
Import −1.45	[0.553] −9.63				[0]* −1.59	[0.483] −9.68 [0]* 

Note: Given figures are the test statistics and their respective p-values are inside the brackets.  
* denotes the rejection of the null hypothesis of non-stationarity of the variables at α = 0.01 
level of significance. 



 Impact of Export and Import on Economic Growth: Time Series Evidence… 

 

DYNAMIC ECONOMETRIC MODELS 19 (2019) 29–40 

35 

 The results of both the ADF test and PP test for the levels and first differ-
ences of the variables are shown in Table 2. These results suggest that all the 
variables are non-stationary in their levels. However, the unit root test applied 
in the first difference of these variables indicate that each of the series is sta-
tionary in their first differences. Thus, we conclude that each of the export, 
import, and GDP are integrated of order one, i.e. I(1). 

3.2 Cointegration Analysis 
 Once establishing the stationarity of the variables, we proceed to the next 
step of our analysis, which is to investigate the existence of long run equilib-
rium relationship between the variables and to determine the number of coin-
tegrating vectors. For this, we employ the Johansen (1988, 1991, 1992) and 
Johansen and Juselius (1990) maximum likelihood cointegration technique. 
This technique is based on Granger’s (1981) ECM representation.  
 To determine the number of cointegrating vectors, we employed both the 
available likelihood ratio tests. These are trace test	(λ-trace) and maximum 
eigenvalue test (λ-max). For both the directions, 1 lag was used (as determined 
by LRT, FPE, AIC, SIC, and HQ criteria). The results for both the trace test 
and maximum eigenvalue tests are reported in Table 3.   

Table 3. Johansen Cointegration Test Results (Trace and max. eigenvalue) 
Null Hy-
potheses 

λ!"#$% 
Stat 

5% Critical 
Value p-value λ&#' 

Stat 
5% Critical 

Value p-value 

r = 0 34.48 29.98 0.0134* 25.69 21.13 0.0106* 
r ≤ 1 8.78 15.49 0.3857 7.43 14.26 0.4394 
r ≤ 2 1.35 3.84 0.2449 1.35 3.84 0.2449 
Note:	rstands	for	the	hypothesized number of cointegrating relationships between export, import, and 
GDP;  H!: r = 0 means that there is no cointegrating relationship betwen export, import and GDP. * indi-
cates the rejection of the respective null hypotheses at α = 0.05  level of significance; the cointegration 
model is based on the vector autoregression model (VAR) with 1 lag as determined by the  LRT, FPE, AIC, 
SIC, and HQ criteria; the critical values for Trace and Max-Eigenvalue statistics are calculated by EViews 
(10).   

 The results indicate that both the tests unanimously identified one cointe-
grating relationship among the export, import, and GDP at α = 0.05 level of 
significance. Thus, we conclude that there is one cointegrating relationship 
between the export, import, and GDP for India for the sample period. In other 
words, there exists a long run equilibrium relationship between the export, im-
port, and GDP for India. 
  



Mitra Lal Devkota 

DYNAMIC ECONOMETRIC MODELS 19 (2019) 29–40 

36 

3.3 Granger Causality and Vector Error Correction Model (VECM) 
 The empirical results from the Johansen and Juselius test for cointegration 
in 3.2 indicate that there is a cointegrating relationship between export, im-
port, and GDP. This also means that a long run equilibrium relationship exists 
between the variables. We next move on to conduct the Granger causality test 
(Engle and Granger, 1987) under VECM framework. This test is a statistical 
procedure used to determine if one time series is helpful in forecasting an-
other.  VECM includes lags of the dependent variables, in addition to its own 
lags (Upadhyaya, Nag and Franklin Jr, 2018). In addition to indicating the 
direction of causality amongst the variables, the VECM also allows one to 
distinguish between short-run and long-run Granger causality relationships 
because it can capture both the short-run dynamics between time series and 
their long-run equilibrium relationship (Masih and Masih, 1996; Devkota and 
Panta (2018)).  

Table 4. Granger Causality Results from Vector error correction model (VECM) 

Response  𝜒(statistic  𝐸𝐶𝑇 
 ∆Export ∆Import ∆GDP  

∆Export – 0.15 0.12 1.33 
∆Import 0.04 – 6.68** –2.93** 
∆GDP 1.464 0.004 – 1.35 

Note: ∆Export, ∆Import and ∆GDP denote the first differences of the logarithmic values of the export, 
import, and the GDP respectively, (**) denotes the rejection of the null hypothesis at the 1% level of sig-
nificance. Number of lags was selected as identified by using the LRT, HQ, AIC, SC, and HQ criteria. 

 We tested the long-run causality through the statistical significance of 
each of the error correction term (ECT) by an individual t-test, and the short-
run Granger causality through the significance of the lags of each explanatory 
variable by a Wald 𝜒3test. A variable 𝑋4 is said to cause another variable 𝑌4 
in the Granger sense if the one step ahead forecast of 𝑌4 in the regression model 
improves the quality of the model and/or forecasts by taking into account the 
historical values of 𝑋4 (see, Din, 2004; Syczewska, 2014; and Osinska, 2011, 
for details). The results for both the long run and short run Granger causalities 
are reported quantitatively in Table 4, and then qualitatively in Table 5. 
 The results indicate that the coefficient of the error correction term of im-
port variable is negative and statistically significant at the 1% level of signif-
icance. This implies that there are long run causalities running from GDP and 
export to import. Also, the chi-square statistic for the causality from GDP to 
import is statistically significant at the 1% level of significance. This suggests 
that there is a short run Granger causality running from GDP to import. 
It means that both the GDP and export Granger cause import in India. 



 Impact of Export and Import on Economic Growth: Time Series Evidence… 

 

DYNAMIC ECONOMETRIC MODELS 19 (2019) 29–40 

37 

Table 5. Causality Results based on Vector Error Correction Model 
  Causality Short-Run Long-Run Direction of Causality 

From To  
Export GDP No No None 

 
Unidirectional 

 
Unidirectional 

GDP Export No No 
Import GDP No No 
GDP Import Yes** Yes** 

Export Import No Yes** 
Import Export No No 

Note: ** denotes the rejection of the null hypothesis at the 1% level of significance; number of lags in the 
VECM was determined using the LRT, FPE, AIC, SIC, and HQ criteria. 

 The interpretations of the results of our study are straightforward. We have 
evidence of unidirectional causality running from GDP to import. This implies 
that an increase in economic growth increases power to purchase foreign 
goods and services. Similarly, we have evidence of unidirectional causality 
running from export to import. A possible explanation could be that when 
a country exports goods and services, it sells them to a foreign market which 
brings in money into the country. This money can then be used to purchase 
goods and services for the country.   
 The diagnostic testing of the estimated model (with the GDP as the de-
pendent variable and export and import as independent variables) is performed 
using the residual analysis based on Breusch-Godfrey serial correlation LM 
test and the results are shown in table 6. Under this test, the null hypothesis 
states that there is no serial autocorrelation of residuals. The chi-squared test 
statistic of 1.858151 with a p-value of 0.3949 suggests that the null hypothesis 
of no serial correlation is not rejected at the 1% level of significance, and thus 
confirms the adequacy of the model. 

Table 6. Breusch-Godfrey serial correlation LM test results  
F-Statistic 0.875485 Prob F(2,84) 0.4204 

Obs*R-Squared 1.858151 Prob Chi-Squared (2) 0.3949  

Conclusions and Discussion 
 The present study investigated the causal relationships between the export, 
import, and GDP for India using the seasonally adjusted quarterly time series 
data for the period between 1996:Q2 and 2019:Q2.  
 The empirical results suggest that each of the export, import and GDP are 
non-stationary in their levels and are stationary in their first differences. In 
addition, the Johansen cointegration test suggests the existence of a long run 
equilibrium relationship between the variables. Finally, the Granger causality 



Mitra Lal Devkota 

DYNAMIC ECONOMETRIC MODELS 19 (2019) 29–40 

38 

test based on VECM framework suggests the existence of unidirectional 
causal relationships from GDP to import, and from export to import. Thus, the 
sample data suggest that none of the ELG, GLE, and ILG hypotheses are valid 
for India. 
  Our finding of existence of long run equilibrium relationships between 
export, import, and economic growth for India is in line with Guntukula 
(2018) for India, Din (2004) for Bangladesh and Pakistan, and Moroke and 
Manoto (2015) for South Africa. However, our finding is contrary to Din 
(2004), who found no evidence of a long run equilibrium relationship between 
these variables for India, Nepal, and Sri Lanka. Also, our finding of the rejec-
tion of the ELG hypothesis is consistent to Mishra (2011) for India. However, 
this finding is contrary to the finding of Moroke and Manoto (2015) for South 
Africa, Awokuse (2007) for Bulgaria, and Guntukula (2018) for India. In ad-
dition, our finding of the rejection of the ILG hypothesis is contrary to the 
finding of Moroke and Manoto (2015) for South Africa and Awokuse (2007) 
for Poland. Finally, our findings of the rejection of the GLE hypothesis is con-
trary to the finding of Guntukula (2018) for India and Awokuse (2007) for 
Bulgaria.  
 We recognize, however, that this study only examined the impact of ex-
port and import on economic growth, thereby ignoring the myriad of other 
factors that also may affect the economic growth (Zang and Baimbridge, 
2012). In addition, as Sharma and Dhakal (1994) mention, due to some limi-
tations of the causality tests employed in this study, our results should be 
viewed with caution. Finally, as Ratanapakorn and Sharma (2007) rightly ar-
gue, one should use at least 30 years of data to run a cointegration test.  Un-
fortunately, the unavailability of data led us to use only 24 years of quarterly 
data. Similar to those authors, our objective is to investigate a shorter time-
period, and hence, the results should be viewed with some degree of caution.   

 
References 
Awokuse, T. O. (2008). Trade Openness and Economic Growth: Is Growth Export-led or Im-

port-led? Applied Economics, 40(2), 161–173. 
Alam, M., Uddin, G., & Taufique, K. (2009). Import Inflows of Bangladesh: the Gravity Model 

Approach. International Journal of Economics and Finance (ISSN 1916-971X), 1(1), 
131–139. 

Balassa, B. (1978). Exports and Economic Growth: Further Evidence. Journal of Development 
Economics, 5(2), 181–189. 

Bhagwati, J. N. (1978). Appendix to "Anatomy and Consequences of Exchange Control Re-
gimes". In Anatomy and Consequences of Exchange Control Regimes, NBER, 219–221. 

Bhagwati, J. N. (1988). Export-promoting Trade Strategy: Issues and Evidence. The World             
Bank Research Observer, 27–57.    



 Impact of Export and Import on Economic Growth: Time Series Evidence… 

 

DYNAMIC ECONOMETRIC MODELS 19 (2019) 29–40 

39 

Devkota, M. L., & Panta, H. (2018). An Inquiry into the Effect of the Interest Rate, Gold            
Price, and the Exchange Rate on Stock Exchange Index: Evidence from Nepal. Dynamic 
Econometric Models, 18, 49–65. https://doi.org/10.12775/DEM.2018.003       

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time 
Series with a Unit Root. Journal of the American Statistical Association, 74(366a),  
427–431. 

Dickey, D.A., & Fuller, W.A. (1981). Likelihood Ratio Statistics for Autoregressive Time Se-
ries with a Unit Root. Econometrica, 1057–1072. 

Din, M. U. (2004). Exports, Imports, and Economic Growth in South Asia: Evidence using 
a Multivariate Time-series Framework. The Pakistan Development Review, 105–124. 

Engle, R. F., & Granger, C. W. (1987). Co-integration and Error Correction: Representation, 
Estimation, and Testing. Econometrica, 251–276. https://doi.org/10.2307/1913236  

Ghatak, S., & Price, S. W. (1997). Export Composition and Economic Growth: Cointegration 
and Causality Evidence for India. Weltwirtschaftliches Archiv, (H. 3), 538–553. 

Granger, C. W. (1981). Some Properties of Time Series Data and Their Use in Econometric 
Model Specification. Journal of Econometrics, 16(1), 121–130.  
https://doi.org/10.1016/0304-4076(81)90079-8 

Guntukula, R. (2018). Exports, Imports and Economic Growth in India: Evidence from Coin-
tegration and Causality Analysis. University of Hyderabad, India, 2(615), 221–230. 

Johansen, S. (1988), Statistical Analysis of Cointegration Vectors, Journal of Economic Dy-
namics and Control, 12, 231–254, https://doi.org/10.1016/0165-1889(88)90041-3 

Johansen, S. (1991), Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian 
Vector Autoregressive Models, Econometrica, 59,1551–1580.   
https://www.jstor.org/stable/2938278 

Johansen, S. (1992). Determination of Cointegration Rank in the Presence of a Linear Trend. 
Oxford Bulletin of Economics and Statistics, 54(3), 383–397.    
 https://doi.org/10.1111/j.1468-0084.1992.tb00008.x 

Johansen, S. & Juselius, K. (1990), Maximum Likelihood Estimation and Inference on Cointe-
gration—with Applications to the Demand for Money, Oxford Bulletin of Economics 
and Statistics, 52, 169–210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x 

Kogid, M., Mulok, D., Ching, K. S., Lily, J., Ghazali, M. F., & Loganathan, N. (2011). Does 
Import Affect Economic Growth in Malaysia. Empirical Economics Letters, 10(3),  
297–307. 

Krugman, P. (1984). Import Protection as Export Promotion. Monopolistic Competition and 
International Trade. 

Kumari, J. (2014). Export, Import and Economic Growth in India: A Study. The Indian Eco-
nomic Journal, 62(3), 1157–1168. 

Malhotra, M., & Meenu (2009). Imports-growth Relationship in India: Causality Analysis. In-
dian Journal of Economics, 90(356), 33–46. 

Masih, A. M., & Masih, R. (1996). Energy Consumption, Real Income and Temporal Causality:  
Results from a Multi-country Study Based on Cointegration and Error-correction Mod-
elling Techniques. Energy Economics, 18(3), 165–183. 

Mazumdar, J. (2001). Imported Machinery and Growth in LDCs. Journal of Development Eco-
nomics, 65(1), 209–224. 

Michaely, M. (1977). Exports and Growth: An Empirical Investigation. Journal of Develop-
ment Economics, 4(1), 49–53. 

Mishra, P. K. (2011). The Dynamics of Relationship Between Exports and Economic Growth 
in India. International Journal of Economic Sciences and Applied Research, 4(2),  
53–70. 



Mitra Lal Devkota 

DYNAMIC ECONOMETRIC MODELS 19 (2019) 29–40 

40 

Mishra, P. K. (2012). The Dynamics of the Relationship between Imports and Economic 
Growth in India. South Asian Journal of Macroeconomics and Public Finance, 1(1),  
57–79. 

Moroke, N. D., & Manoto, M. (2015). How Applicable is Export-led Growth and Import-led 
Growth Hypotheses to South African economy? The VECM and Causality Approach. 
Journal of Governance and Regulation, 4(2), 15–25.   
http://dx.doi.org/10.22495/jgr_v4_i2_p2 

Osińska, M. (2011), On the Interpretation of Causality in Granger Sense, Dynamic Econometric 
Models. http://dx.doi.org/10.12775/DEM.2011.009 

Oxley, L. (1993). Cointegration, Causality and Export-led Growth in Portugal, 1985. Econom-
ics Letters, 43(2), 163–166. 

Phillips, P. C., & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Bio-
metrika, 75(2), 335–346. 

Ramos, F. F. R. (2001). Exports, Imports, and Economic Growth in Portugal: Evidence                     
from Causality and Cointegration Analysis. Economic Modelling, 18(4), 613–23. 

Ratanapakorn, O., & Sharma, S. C. (2007). Dynamic Analysis between the US Stock             
Returns and the Macroeconomic Variables. Applied Financial Economics, 17(5), 69– 
–377. https://doi.org/10.1080/09603100600638944 

Sharma, S. C., & Dhakal, D. (1994). Causal Analyses between Exports and Economic Growth 
in Developing Countries. Applied Economics, 26(12), 1145–1157. 

Syczewska, E. M. (2014), The EURPLN, DAX and WIG20: The Granger Causality             
Tests Before and During the Crisis, Dynamic Econometric Models, 14, 93–104,  
 http://dx.doi.org/10.12775/DEM.2014.005 

Taghavi, M., Goudarzi, M., Masoudi, E., & Gashti, H. P. (2012). Study on the Impact of Export 
and Import on Economic Growth in Iran. Journal of Basic and Applied Scientific Re-
search, 2(12), 12787–12794. 

Tang, C. F., Lai, Y. W., & Ozturk, I. (2015). How Stable is the Export-led Growth Hypothesis? 
Evidence from Asia's Four Little Dragons. Economic Modelling, 44, 229–235. 

Upadhyaya, K. P., Nag, R., and Mixon Jr, F. G. (2018). Stock Market Capitalization  
and the Macroeconomics of Transition Economies: The Case of India. Dynamic  
Econometric Models, 35–47. http://dx.doi.org/10.12775/DEM.2018.002 

Weisberg, S. (2005). Applied linear regression (Vol. 528). John Wiley & Sons.           
Zang, W., & Baimbridge, M. (2012). Exports, Imports and Economic Growth South Korea and 

Japan: A Tale of two Economies. Applied Economics, 44(3), 361–372. 


