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American Journal of  Applied 
Statistics and Economics (AJASE)

Impact of  Liquefied Natural Gas Exports on the Nigerian Exchange Rate: An ARDL 
Cointegration Approach, 2000 to 2021 

Kufre Jerome Udoudo1*, Ijeoma Emele Kalu2, Koyejo Oduola3

Volume 3 Issue 1, Year 2024
ISSN: 2992-927X (Online)

DOI: https://doi.org/10.54536/ajase.v2i1.2216
https://journals.e-palli.com/home/index.php/ajase

Article Information ABSTRACT

Received: December 02, 2023

Accepted: December 29, 2023

Published: December 31, 2023

This study aimed to investigate the impact of  liquefied natural gas (LNG) exports on 
the exchange rate of  Nigeria. The investigation employed an autoregressive distributed 
lag model (ARDL) methodology to analyse data spanning the years 2000 to 2021 using a 
biannual dataset. The empirical findings provided evidence of  a statistically significant and 
positive influence of  liquefied natural gas exports on the exchange rate. This is supported 
by the results obtained from the short-term analysis. The research results revealed no causal 
link between the exports of  LNG and the exchange rate in Nigeria. The study concludes that 
LNG exports cause the Naira to depreciate. The research suggested that the government 
should actively endorse and facilitate the expansion of  non-oil and gas sectors, including 
agriculture, manufacturing, and services, to cultivate a more varied export portfolio. 
Moreover, adequate reserves can be used to stabilize the Naira during periods of  volatility 
caused by fluctuations in LNG exports or other external factors.

Keywords

ARDL Model, Exchange Rate, 
LNG Exports, Nigeria

1 Emerald Energy Institute, University of  Port Harcourt, Nigeria
2 Department of  Economics, University of  Port Harcourt, Nigeria
3 Department of  Chemical Engineering, University of  Port Harcourt, Nigeria
* Corresponding author’s e-mail: kufrej@yahoo.com

INTRODUCTION
Nigeria possesses considerable reserves of  natural gas, 
which have been a source of  strategic importance since 
1999 with the establishment of  the liquefied natural 
gas (LNG) industry. This development aligns with the 
Nigerian Gas Master Plan, aiming to broaden the country’s 
revenue streams and diminish reliance on the exportation 
of  crude oil. The exportation of  LNG has facilitated the 
generation of  foreign exchange earnings, the attraction 
of  investment, and the promotion of  economic growth 
(Khan, 2015). Nevertheless, the correlation between LNG 
exports and the exchange rate in Nigeria is intricate and 
diverse, as it is shaped by a range of  economic, policy, and 
external factors. Over the course of  the past two decades, 
Nigeria has experienced substantial growth in its LNG 
sector, establishing itself  as a prominent producer of  
natural gas within the African continent. The exploration 
of  natural gas and subsequent development of  the 
Nigerian LNG plant has significantly contributed to the 
diversification of  the nation’s export portfolio, thereby 
mitigating its substantial reliance on crude oil. Exports of  
LNG refer to the international trade of  LNG, which is a 
clear, odourless, and non-toxic form of  natural gas that 
has been converted into a liquid state through a cooling 
and condensation process. LNG is characterised by its 
transparency, lack of  odour, and non-toxic properties. 
LNG is predominantly comprised of  methane and is 
generated through the process of  cooling natural gas 
to a temperature of  approximately -162 degrees Celsius 
(-260 degrees Fahrenheit). This cooling procedure results 
in a reduction in volume, facilitating efficient and secure 
transportation of  LNG via tanker vessels (Onolehemhen, 
et al., 2017).
LNG exports encompass the commercial transaction 
and transportation of  LNG from the exporting nation to 

various importing countries or global markets. Specialised 
LNG carriers are responsible for the transportation of  
LNG to designated receiving terminals situated in the 
importing nations. At the terminals where the LNG is 
received, a process called vaporisation is employed to 
convert the LNG back into its gaseous state, enabling 
its distribution and utilisation. The significance of  
LNG exports has grown considerably within the global 
energy market due to the various advantages it offers in 
comparison to conventional pipeline gas transportation. 
This technology offers enhanced flexibility with regard 
to the distribution of  natural gas, enabling countries 
lacking direct pipeline connections to effectively access 
these valuable resources. The exportation of  LNG plays 
a significant role in enhancing energy security, promoting 
the diversification of  energy sources, and facilitating 
international trade in natural gas. Countries that export 
LNG, including Qatar, Australia, the United States, 
and Nigeria, have a significant impact on satisfying the 
increasing global demand for natural gas (Chien-Chiang et 
al., 2011; Barril & Navajas, 2015; Felipe et al., 2018). These 
nations allocate resources towards the establishment of  
LNG production facilities, infrastructure, and export 
terminals to facilitate the processes of  liquefaction, 
storage, and transportation that are integral to the 
trade of  LNG. The growth of  LNG exports has been 
propelled by various factors, including the rising need 
for more environmentally friendly energy options, 
economic motivations, geopolitical influences, and 
advancements in LNG technology (Hong, 2013). The 
expansion of  the LNG trade has significantly altered the 
energy sector, facilitating the linkage between regions 
that produce natural gas and those that consume it. 
This development has played a pivotal role in fostering 
energy collaboration and enhancing global integration. In 



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general, the exportation of  LNG constitutes a substantial 
element within the realm of  global energy commerce. 
This practice facilitates the cross-border utilisation of  
natural gas reserves and contributes to the enhancement 
of  energy stability and economic progress for both 
exporting and importing nations.

Exchange Rate Dynamics in Nigeria
Exchange rate dynamics in Nigeria refer to the 
fluctuations and movements of  the Nigerian currency, 
the Naira, in relation to foreign currencies, particularly 
major global currencies such as the US dollar, Euro, 
and British pound. The exchange rate, or the cost of  
exchanging one currency for another, has a major impact 
on a nation’s capacity to trade internationally, attract 
foreign investment, and maintain economic growth and 
prosperity. The value of  the Nigerian Naira is set in part 
by the supply and demand for foreign currency on the 
international market. On occasion, however, the Central 
Bank of  Nigeria (CBN) steps into the market to control 
currency rate stability and prevent undue volatility. Several 
variables affect the fluctuation of  the Nigerian currency 
exchange rate including:

Balance of  Trade
Currency supply and demand in Nigeria are both 
influenced by the country’s trade balance, which is the 
amount by which exports exceed imports. When Nigeria 
has a trade surplus, there is an increased demand for 
Naira, strengthening its value. Conversely, a trade deficit 
puts downward pressure on the Naira. 

Foreign Direct Investment (FDI)
The value of  a currency may rise or fall in response to 
changes in the volume of  foreign direct investment. 
Foreign direct investment (FDI) may cause a rise in 
the value of  the Naira since it shows confidence in the 
country’s economy and boosts demand for the Naira. 

Oil Prices
Since Nigeria is a major oil exporter, the country’s currency 
value is very sensitive to changes in international oil prices 
(Yunusa, 2020). Since a large proportion of  Nigeria’s 
foreign currency revenues come from oil exports, falling 
oil prices might cause the Naira to weaken. 

Inflation and Interest Rates
Depreciation of  a currency may occur if  inflation rates 
are persistently high (Salisu & Ayinde, 2016). Interest rate 
changes and other monetary policy measures taken by the 
central bank affect inflation and, by extension, the value 
of  a currency’s exchange rate.
The Nigerian exchange rate has experienced periods of  
volatility and depreciation over the years, attributed to 
factors such as economic imbalances, external shocks, 
policy decisions, and market expectations (Vincent et al., 
2021). The CBN has used interventions in the foreign 
currency market, capital controls, and foreign exchange 

restrictions to maintain exchange rate stability.
Given Nigeria’s heavy reliance on oil exports and the 
potential for LNG exports to contribute significantly 
to its foreign exchange earnings, it becomes essential to 
examine the relationship between LNG exports and the 
Nigerian exchange rate. The economic dynamics, policy 
ramifications, and possible risks connected with LNG 
exports may all be better understood with a firm grasp of  
this connection. The importance of  the research is huge. 
Using the ARDL cointegration method, this research 
aims to assess LNG exports and the Nigerian currency 
rate from 2000 to 2021. Consequently, this study will 
greatly benefit key stakeholders in Nigeria. Specifically, 
understanding the impact of  LNG exports on the Nigerian 
exchange rate is of  utmost importance for policymakers, 
economists, and industry stakeholders. The exchange rate 
is a crucial determinant of  a country’s competitiveness in 
the global market, investment attractiveness, and overall 
economic stability. Exploring the dynamics between 
LNG exports and the exchange rate will shed light on 
the potential effects of  LNG industry developments on 
Nigeria’s macroeconomic performance.
The main goal of  this study was to:

i. To assess the impact of  LNG exports on the Nigerian 
exchange rate 

ii. To determine the causality between LNG exports 
and the Nigerian exchange rate. 
While many studies in Nigeria and elsewhere have focused 
only on the effects of  exchange rates and volatility on 
exports, others have examined the role of  exchangeratese 
in stimulating economic expansion. This research set out 
to address the gap that exists in the literature. Using an 
autoregressive distributed lag (ARDL) model proposed 
by Pesaran et al. (2001), this study aimed to empirically 
examine the influence of  Nigeria’s LNG exports on the 
country’s exchange rate. 
Consequently, the following hypotheses in null form (H0) 
guided the study:

i. H01: LNG exports have no significant and positive 
impact on the Nigerian exchange rate 

ii. H02: There is no causal relationship between LNG 
exports and the Nigerian exchange rate
The present paper is structured into distinct sections. The 
initial segment encompasses several key components, 
including the introduction, research problem, study 
objective, hypothesis statement, theoretical background, 
and empirical literature review. The subsequent section 
provides an overview of  the methodology employed and 
the data utilised in the study. The subsequent section of  
the manuscript provides an exposition of  the findings and 
subsequent analysis. Finally, the fourth section discusses 
the Conclusion and Recommendations.

Theoretical Background 
This analysis was founded on the theory of  export-led 
development. The export-led growth theory is founded on 
the perspectives of  classical and neo-classical economic 
theory. Export is the primary determinant of  economic 



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development, according to this theory (Schmidt, 2020). 
The theory of  export-led development is an economic 
framework that emphasises the correlation between a 
country’s exports and its overall economic growth. The 
proposition asserts that increasing the quantity and value 
of  exports can have a positive effect on a country’s 
economic development, resulting in improved quality of  
life and higher employment rates. This theory emphasises 
the significance of  increasing a nation’s competitiveness 
in the international market by producing in-demand 
products and services. This is accomplished by offering 
products of  superior quality, at competitive prices, and 
per international standards. To achieve export-driven 
economic growth, nations must implement policies 
and strategies that enable them to access international 
markets, reduce trade barriers, and negotiate advantageous 
trade agreements with other nations. According to the 
theoretical framework utilised in this study, it is posited 
that an increase in LNG exports has the potential to 
stimulate economic growth, thereby influencing the 
exchange rate. Enhanced LNG export revenues have 
the potential to boost the value of  the Nigerian Naira by 
increasing the inflow of  foreign currency and bolstering 
the country’s foreign exchange reserves.

Empirical Literature Review
The study conducted by Musa et al. (2019) examined 
the influence of  crude oil price and exchange rate on 
Nigeria’s economic growth from 1982 to 2018 using the 
ARDL Approach. It was determined that these factors 
exert a notable positive impact on both the long-term and 
short-term durations. The study proposes that a strategy 
of  diversifying revenue streams through agricultural 
activities, industrial development, and investment can 
effectively mitigate the dependence on crude oil and the 
consequent income volatility resulting from fluctuations 
in oil prices.
Sieng et al. (2020) examined the factors influencing 
export levels in Indonesia, the Philippines, Malaysia, and 
Thailand. The study aimed to estimate the effects of  
import, exchange rate, foreign direct investment (FDI), 
inflation, and crude oil on exports. Three econometric 
techniques and three-panel data estimation models 
were employed to achieve this. The findings indicated a 
positive relationship between import and exchange rate 
with exports in all four countries, while FDI exhibited 
a significant negative impact. Based on these results, 
the study recommends that governments prioritise the 
provision of  peace and political stability as a means to 
stimulate exports and attract greater levels of  investment.
Kandil and Mirzaie (2002) looked at how changes 
in exchange rates impacted production and prices in 
different industries in the United States. According to the 
study’s results, expansionary and contractionary variables 
have a neutral effect on the rate of  increase in industrial 
real production. Nevertheless, the appreciation of  the 
dollar results in a noteworthy decrease in price inflation 
across various sectors, with a particular emphasis on the 

finance industry. The outcome aligns with the decrease 
in overall demand caused by net exports and the rise 
in overall supply resulting from the decreased expense 
of  imported intermediate goods. The study’s findings 
indicate that the limited level of  openness observed in 
US industries results in moderate price effects caused by 
external shocks and fluctuations in exchange rates, while 
not significantly impacting output growth. Hence, the 
lack of  empirical support for the detrimental impact of  
dollar appreciation on economic performance in various 
sectors of  the United States refutes the aforementioned 
concerns.
Udoudo et al. (2023) investigated the influence of  LNG 
exports on inflation in Nigeria during the period spanning 
from 2000 to 2021. The study employed the Autoregressive 
Distributed Lag (ARDL) bound co-integration approach 
to examine the impact of  inflation in both the long term 
and short term, revealing diverse effects. The impact of  
natural gas prices and crude oil prices on inflation was 
negative, whereas LNG exports did not have a significant 
effect. The study suggests that the government should 
maintain its support for the LNG sector, with a particular 
emphasis on investing in infrastructure and technology to 
improve competitiveness and efficiency.
Using a VAR (Vector Autoregression) model, Akpan’s 
(2009) research investigated the dynamic connection 
between oil price shocks and important macroeconomic 
indicators in Nigeria. Positive and negative shocks were 
shown to have equally large and asymmetrical effects on 
inflation, with the former causing a rise in real national 
income and the latter in export profits. However, the author 
discovered that some of  the gains are counterbalanced by 
reduced demand for exports due to economic downturns 
experienced by trading partners. The study also revealed a 
strong positive correlation between oil price fluctuations 
and government expenditures but found minimal 
influence on industrial output growth. The author 
emphasizes the need for policymakers to implement 
policies that enhance and stabilize the Nigerian economy, 
prioritizing measures like exploring alternative sources of  
government revenue, implementing fiscal discipline, and 
saving oil boom proceeds to better withstand future oil 
shocks.
In their study, Hassan et al. (2013) conducted an analysis 
to examine the influence of  various macroeconomic 
factors, including exchange rate and economic growth, on 
the performance of  Pakistan’s exports. The researchers 
utilised time series data for their investigation. The 
researchers employed the Augmented Dickey-Fuller 
(ADF) Unit Roots Test and the Autoregressive Distributed 
Lag (ARDL) model to ascertain the long-term association 
between the variables. The research discovered a 
sustained equilibrium connection between the export 
performance of  Pakistan and its determinants. The 
impact of  the exchange rate, gross domestic production, 
and trade openness on export performance are found 
to be positive and statistically significant, whereas the 
influence of  foreign direct investment is deemed to be 



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insignificant. The labour force estimates suggest that 
export-oriented sectors, which necessitate a skilled labour 
force, are adversely impacted by both higher growth and 
a deficiency in skills.
Alam (2010) evaluated the influence of  Taka’s actual 
exchange rate depreciation on Bangladesh’s export 
revenues. The macroeconomic series utilised in this study 
are non-stationary, specifically integrated at order one, but 
they do not exhibit cointegration. The application of  the 
Granger Causality test utilising the vector autoregressive 
(VAR) model yielded results indicating the absence of  a 
causal relationship between real depreciation and export 
earnings. This underscores the necessity of  conducting 
sub-sector analysis, evaluating incentive policies, and 
enhancing the proportion of  local commodity exports. 
However, before enacting devaluation or depreciation 
policies, it is necessary to evaluate the negative impacts 
of  depreciation on macroeconomic indices, particularly 
inflation.
Aliyu’s (2009) research aimed to quantitatively assess 
the impact of  exchange rate fluctuation on the volume 
of  Nigeria’s non-oil exports. The research used a basic 
analytical technique, which postulated that the naira 
exchange rate volatility, US dollar volatility, Nigeria’s 
terms of  trade, and the index of  openness (OPN) all 
have a role in the fluctuation of  non-oil exports. The 
data supported the existence of  a unit root at the level, 
however, the lack of  stationarity was rejected as a null 
hypothesis. The results of  the cointegration study show 
that non-oil exports are linked to the basic variables in 
a stable, long-term equilibrium. The report suggests 
methods to increase openness and promote stability in 
the Nigerian currency market.
Berman et al. (2012) studied the response of  French 
firms to real exchange rate fluctuations from 1995 to 
2005. They found that high-performance firms increase 
their markup and export volume, while low-performance 
firms decrease demand elasticity. This indicates that 
different approaches to pricing the market may explain 
why changes in the value of  one currency have relatively 
little effect on export volumes as a whole. Identifying and 
compensating for the effects of  currency exchange rate 
variations on export volumes was emphasised.
Udoudo et al. (2023) used biannual data to analyse the 
effect of  Nigeria’s LNG exports on the country’s GDP 
from 2000 to 2021. The study used an ARDL model 
and a unit root test to analyze the variables. The results 
showed a positive association between LNG exports 
and the Nigerian economy, with a 1% increase in LNG 
exports resulting in a 0.72% increase in GDP. In the short 
term, a marginal increase in LNG exports would lead to 
a 0.23% GDP gain. To ensure a steady supply of  natural 
gas to Nigeria’s LNG plants, they advised the government 
to increase spending on the sector and take the initiative 
to push for the construction of  floating LNG plants 
in regions where laying pipelines would be too costly. 
However, their study did not consider the effect of  LNG 
exports on Nigeria’s exchange rate.

Using monthly data from 1996–2015, Oluyemi and 
Isaac (2017) studied how currency exchange rates 
affected Nigeria’s exports and imports. They looked 
at the correlation between the USD/NGN exchange 
rate, exports, and imports using a vector autoregression 
(VAR) model with three independent variables. The 
research indicated that imports and exchange rates 
had a positive but statistically insignificant association, 
whereas exports had a negative effect on exchange rates. 
The research found that fluctuations in exchange rates 
did not significantly influence imports and exports in 
Nigeria. The study suggests promoting export activities, 
focusing on the non-oil sector, to foster entrepreneurial 
development and mitigate excessive import levels.
Nguyen’s (2016) study found a significant positive 
correlation between exports and Vietnam’s economic 
growth from 1990 to 2015. The study found that exports 
accelerate industrialization and modernization, positively 
affecting GDP growth in both current and future years. 
The author recommended that local governments and 
export enterprises foster export activities and their effects, 
promoting sustainable economic growth in emerging 
and developing nations reliant on commodity exports. 
However, the study did not consider the potential impact 
of  exports on Vietnam’s exchange rate.
The effect of  currency rate volatility on Nigeria’s export 
from 2008 to 2021 was researched by Musa et al. in 2023. 
The research used secondary data from the Statistics 
Database and the ARDL-Error Correction Model and 
Bound Test. Although only real effective exchange rate 
volatility was statistically significant, the analysis indicated 
that long-run exchange rate volatility was negative. While 
exchange rate volatility generally had a negative short-
term impact, real effective and nominal effective exchange 
rate volatility were statistically significant. To increase 
trade and broaden export markets, the research suggested 
stabilising the value of  the Naira and diversifying Nigeria’s 
export mix.
Ikechi and Nwadiubu’s (2020) study explored the potential 
positive effects of  exchange rate fluctuations on Nigeria’s 
international trade dynamics, specifically looking at the 
potential for increased export and import transactions. 
The research used secondary data from 1996 to 2018 
and employed econometric methodologies to establish 
correlations. The VAR model estimations showed a 
negative correlation between exports, imports, and the 
real effective exchange rate (REER) during the present 
period. An increase in both export and import during 
a specific year resulted in a decrease of  approximately 
0.9% and 0.4% in the REER, respectively. The analysis 
of  variance decomposition analysis revealed that shocks 
play a significant role in accounting for the variations 
observed in the real effective exchange rate and the levels 
of  exports and imports. The impulse response analysis 
showed a negative correlation between exports and the 
real effective exchange rate, while imports significantly 
impacted exports. The ARCH modelling framework 
posits a primary Arch effect and a statistically significant 



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GARCH component. The findings suggest that the Real 
Effective Exchange Rate (REER) exhibits high volatility, 
leading to a clustering effect on import and export trading 
activities in Nigeria. Since financial shocks tend to amplify 
changes in exchange rates, the authors suggest employing 
monetary and fiscal actions to mitigate the detrimental 
effects of  these swings.
Goya (2020) looked into whether or not there was a 
connection between a country’s currency exchange rate 
and the diversity of  the goods it exported as part of  its 
research project. The primary data utilised in this research 
was sourced from the World Trade Flows dataset and the 
International Monetary Fund’s International Financial 
Statistics, covering the period from 1962 to 2000. 
Various estimation techniques were employed, including 
fixed effects, dynamic GMM, Mean Group, and Pooled 
Mean Group estimators. The findings indicate a positive 
association between export variety and a depreciated 
exchange rate, while a negative relationship was observed 
between export variety and exchange rate volatility. 
Furthermore, these relationships were found to be more 
pronounced for goods with higher levels of  technological 
intensity.
Rümeysa’s (2018) research attempted to learn how 
fluctuations in the value of  the Turkish lira affect the 
country’s exports. For this study, the researcher used an 
ARDL border test and a model for correcting statistical 
errors. By evaluating a dataset with monthly observations 
from January 1995 to January 2017, this study looked 
into how changes in exchange rates affected export 
volumes. To check for cointegration between variables, 
the Bound Test Method was used. The analysis found 
that both the long-term and short-term indicators’ 
observed coefficients lined up with expectations. As a 
result, it seems that imports have a favourable effect on 
the industrial output index and exports, both in the short 
and long terms. Nonetheless, it can’t be denied that the 
effective exchange rate index and volatility have been 
negatively affected both in the long and short term.
Djatmiko and Nugroho (2019) performed research 
between 1996 and 2017 to analyse the effect of  Indonesia’s 
oil and gas exports and non-oil exports on the country’s 
foreign exchange reserves. Researchers used 22 yearly 
observations analysed using SPSS 24. Indonesia’s non-oil 
and gas exports and oil and gas exports are the independent 
variables, while the country’s foreign currency reserves are 
the dependent variable. The researcher used multivariate 
linear regression analysis to look at how the independent 
factors affected the dependent one. The research found 
that Indonesia’s foreign currency reserves are influenced 
positively and statistically significantly by both oil and gas 
export and partial export of  goods other than oil and gas. 
The authors suggested that the non-oil and gas sector 
should engage multiple government agencies, including 
the Ministry of  Trade, the Coordinating Ministry for 
Economic Affairs, and the Ministry of  Transportation, 
among others. This collaboration would enable private 
entities to work in synergy with regional governments as 

contributors to the commodity sector.
In their study, Wildan et al. (2020) conducted an analysis 
of  the impact of  macroeconomic factors on the 
management of  natural gas exports in Indonesia. The 
researchers utilised secondary time series data spanning 
a period of  22 years, specifically from 1995 to 2017. The 
variables considered in this study encompassed domestic 
consumption, exchange rate, international price, and 
GDP per capita of  the importing country. The employed 
analytical approach was the auto-regressive distributed lag 
(ARDL) method. The findings of  the analysis indicated 
that, in the immediate term, various factors including 
domestic consumption, exchange rates, natural gas prices, 
and GDP per capita exert a significant influence on the 
magnitude of  natural gas exports. In the long term, the 
outcomes align with those observed in the short term, 
wherein various independent variables, including domestic 
consumption, exchange rates, international prices, and 
GDP per capita, notably influence the magnitude of  
natural gas exports.
Bakari and Mabrouki (2017) analysed the effect of  exports 
and imports on Panama’s GDP. Each year’s data from 1980-
2015 was checked using the Granger-Causality tests and 
the Johansen co-integration analysis of  the Vector Auto 
Regression Model. The results of  the study showed no 
connection between exports, imports, and GDP growth in 
Panama. However, it was shown that imports and exports 
contribute to economic development in both directions. 
These results indicate that international commerce is a 
major contributor to Panama’s thriving economy.
Dogo and Aras (2021) examined how Naira-Dollar 
exchange rate volatility affected Nigerian imports and 
exports between 1990 and 2019. The CBN, NBS, 
and International Financial Statistics provided data 
for all indicators except volatility. The autoregressive 
distributed lagged (ARDL) and exponential generalised 
autoregressive conditional heteroscedasticity (EGARCH) 
models were used to evaluate the short- and long-
term associations between Naira-Dollar exchange rate 
fluctuations and imports and exports. Long-term research 
showed that fluctuations in the Naira-Dollar exchange 
rate were related to Nigeria’s imports and exports, while 
short-term research did not find any such relationship. To 
enhance the trade balance, the report advises government 
to continue export promotion and import reduction.
Using data from 2005’s first quarter to 2020’s fourth 
quarter, Duru et al. (2022) investigated the impact of  
fluctuating currency rates on Nigeria’s exports. The 
amount of  exchange rate volatility, with an emphasis on 
the nominal effective exchange rate, was evaluated using 
the ARCH model and its later expansions, including 
the GARCH, TARCH, and EGARCH models. The 
short- and long-term effects of  exchange rate volatility 
on exports were examined using the Autoregressive 
Distributed Lag (ARDL) Bounds test approach. Results 
showed that currency fluctuations do occur. Furthermore, 
while the influence of  exchange rate changes on exports 
was determined to be statistically negligible, the study’s 



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results nonetheless revealed that such fluctuations had a 
negative effect on exports. The report concludes that the 
Central Bank of  Nigeria should work towards establishing 
consistent exchange rate systems via the implementation 
of  appropriate exchange rate rules. Additionally, the 
government must establish a conducive environment that 
facilitates the production of  goods that are suitable for 
exportation.
Vivoda (2022) looks into the top five LNG producers in 
the world, which are Australia, Qatar, the USA, Russia, 
and Malaysia. By focusing on energy exports, the study 
tried to fix the fact that most research on energy policy 
is focused on energy imports. In the piece, the supplies 
of  LNG by five companies were compared, taking into 
account how much they were different from each other. 
It used eight factors to describe the trends of  LNG 
export diversification among providers from 2009 to 
2021. It also used a well-known method, the Herfindahl-
Hirschmann index of  market concentration, to calculate 
and classify the amount of  LNG export diversification. 
The paper found that the position and the difference 
in natural gas prices between regional markets have the 
most power to explain. This result has important policy 
implications for how LNG-exporting nations act. It also 
shows how sensitive these nations are to economic forces.
AboElsoud (2010) research addressed important 
empirical questions regarding the relationship between 
natural gas exports and Egyptian economic growth by 
extending Dirtsakis’s model with the addition of  the 
labour force into the model and further by addressing 
the issue in a disaggregated framework. This study 
analyzed the issue of  ELG and NGELG hypotheses in 
Egypt using the VAR analysis, quarterly time-series data 
over the period 1991: q1-2009:q4. The empirical results 
tend to favour the effectiveness and validity of  the ELG 
and NGELG hypotheses for Egypt. In other words, 
the results indicated that Natural Gas exports promote 
economic growth. He recommended that Egypt should 
be the key gas “trader” for decades to come because 
based on geographical location we are the African gate to 
the European and Central Asia gas markets.
Truong et al. (2022) conducted a study examining the 
asymmetric effects of  Exchange Rate Volatility (ERV) 
on Vietnam’s international trade during the period 
spanning from January 2010 to December 2019. The 
research employed time-series data and the Nonlinear 
Autoregressive Distributed Lag (NARDL) model to 
analyse the correlation between exchange rate volatility 
(ERV) changes and the trade balance. The study’s 
findings indicate that in the short term, positive changes 
in the exchange rate volatility (ERV) negatively impact the 
trade balance. However, in the long term, improvements 
in ERV have positive effects on the trade balance. The 
negative changes observed in the ERV did not yield a 
statistically significant impact. To ensure the trade balance 
in Vietnam can be maintained over the long term, they 
suggested that the government adopt a policy of  a stable 
currency rate.

METHODOLOGY 
Description of  Data
This study examines the impact of  exporting Liquefied 
Natural Gas (LNG) on the exchange rate of  Nigeria from 
the year 2000 to 2021. The biannual data series of  the 
Naira/Dollar exchange rate, Nigeria LNG exports, Henry 
Hub Natural Gas price, and Brent Crude Oil price have 
been considered. The dependent variable in this study 
was the exchange rate, while the independent variables 
were LNG exports, Natural Gas price, and Crude Oil 
price. This study’s data was collected from reputable 
sources such as the BP Statistical Bulletin, Statista, the 
Energy Information Administration (EIA), and the 
World Development Indicators.

Empirical Methodology
The primary objective of  this research was to analyse the 
effect of  Nigeria’s LNG exports on the country’s currency 
exchange rate. The Autoregressive Distributive Lag 
(ARDL) test for co-integration, established by Pesaran 
et al. (2001), is used in this research. This methodology 
is utilised to conduct both abound and cointegration 
tests. This methodology employs empirical analysis to 
examine the variables’ long-term relationships and short-
term dynamic interactions. One of  the main benefits 
of  this methodology is its utilisation of  regressors that 
exhibit stationarity at either I(1) or I(0), or potentially a 
combination of  both (Ogunnusi & Ajibode, 2023). This 
characteristic assists in circumventing the challenges that 
arise when testing for a unit root. The ARDL approach has 
been found to effectively address endogeneity concerns, 
as demonstrated by Javed and Husain (2020). The 
application of  the ARDL test to examine cointegration in 
a small sample size is relatively straightforward, whereas 
the Johansen technique necessitates a larger sample size 
to conduct the cointegration analysis effectively. The 
aforementioned methodology expeditiously assesses 
the variables, regardless of  their disparate optimal lags 
(Ozturk & Acaravci, 2010). The multivariate model 
(Equation 1) was employed to investigate the association 
between the variables. Prior to conducting further 
analysis, the values were logarithmically transformed to 
mitigate potential heteroscedasticity in the data.
LEXRt = β0 + β1LLNGt + β2LNGPt + + β3LCOPt + ϵt  (1)
where: in Eq. (1) LEXR is the natural log of  the exchange 
rate, LLNG is the natural log of  LNG exports, LNGP is the 
natural log of  Natural Gas price, and LCOP is the natural log 
of  Crude Oil price. Whereas ‘t’ is the time period, however, 
β0, is the intercept, β1, β2, and β3 are the coefficient of  slop 
respectively, and ε is the white noise error.

ARDL Estimation and Specification
Equation 2 presents the formulation of  the multivariate 
unrestricted error correction model (UECM) within the 
framework of  the ARDL-bound approach.
△LEXRt = α0 + ∑p

(i=1)α1i △LEXR(t-1) +  ∑q1
(i=0)α2i △LLNG(t-1) 

+ ∑q2
(i=0)α3i △LNGP(t-1) + ∑q3

(i=0) α4i △LCOP(t-1) + β1LEXRt-1 
+ β2LLNGt-1 + β3LNGPt-1 + β4LCOPt-1 + ϵt                 (2)                                                                                  



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Here, LEXR refers to the natural log of  exchange rate; 
LLNG is the natural log of  LNG export quantity, LNGP 
is the natural log of  natural gas price, and LCOP stands 
for the natural log of  crude oil price. Δ is used to present 
the operator difference, the error is denoted by ϵt; p is 
the optimal lag lengths of  the dependent variable and q1, 
q2 and q3 are the optimal lag lengths of  the independent 
variables, α0 is the constant, α1 - α4 are the coefficients 
of  the differenced variables, β1 - β4 are the coefficients 
of  the lagged variables. The bound test is broken down 
into three distinct steps and relies on joint F-statistics to 
determine the outcome of  the cointegration process.

Step One
This analysis assists in examining the potential long-
term relationship between the series using the ordinary 
least squares (OLS) method. OLS helps determine the 
combined significance of  coefficients at the lagged level 
in Equation 2. The null hypothesis posited for the series 
can be formally expressed as “H0: γ₁ = γ₂ = γ₃ = γ₄ = 
γ₅ = γ₆ = γ₇ = 0”, Conversely, the alternative hypothesis 
is articulated as “H1: γ₁ ≠ γ₂ ≠ γ₃ ≠ γ₄ ≠ γ₅ ≠ γ₆ ≠ γ₇ ≠ 
0.” The utilisation of  bound test is employed to ascertain 
the upper bound value, represented as I (1), and the 
lower bound value, represented as I(0), within the realm 
of  regression analysis. If  the computed F-statistic falls 
below the lower bound value, it becomes implausible to 
dismiss the null hypothesis, thereby signifying the lack 
of  cointegration among the variables. If  the calculated 
F-statistics surpasses the predetermined upper threshold, 
it serves as an indication to refute the null hypothesis 
positing the absence of  cointegration among the 
variables. The manifestation of  this rejection implies the 
existence of  a long-term association and co-movement 
between the variables. As per the scholarly work of  Javed 
and Husain (2020), if  the computed F-statistic lies within 
the confines of  the lower and upper bound values, the 
outcomes of  the test are deemed to be inconclusive.

Step Two
If  cointegration is detected among the variables, the 
subsequent procedure involves estimating the long-run 
coefficients. The ARDL model is expressed in equation 3.
LEXRt = β0 + ∑p

(i=1)β1 LEXR(t-1) + ∑q1
(i=0)β2 LLNG(t-1) + 

∑q2
(i=0)β3 LNGP(t-1) + ∑q3

(i=0)β4 LCOP(t-1)                 (3)

Step Three
The final stage involves the estimation of  the short-
term coefficient when the variables exhibit a long-term 
relationship, utilising an error correction model (ECM) as 
depicted in equation 4 below.
△LEXRt = α0 + ∑p

(i=1) α1i△LEXRt-1 + ∑q1
(i=0) α2i 

△LLNG(t-1) + ∑q2
(i=0) α3i △LNGP(t-1) + ∑q3

(i=0) α4i 
△LCOP(t-1) + φECT(t-1) + ϵt                                         (4)  
Where φ reflects the rate at which the error correction 
term’s adjustment coefficient is adjusted, and the 
coefficients α1-α4 represent the short-run dynamic.

The next step is to determine the causal link between the 
variables once the ARDL-bound cointegration test has 
been completed. For the goal of  analysing the causality, 
the Toda-Yamamoto Granger causality test is used. 
Eddrief-Cherfi and Kourbali (2012) argue that the mere 
existence of  causality within the elements is insufficient 
for determining the directionality. According to Ekeke 
(2020), the version proposed by Toda-Yamamoto is 
considered to be more reliable for conducting Granger 
causality tests. This assertion is based on the fact that the 
Toda-Yamamoto version is justifiable regardless of  the 
co-integration order of  the variables.

RESULTS AND DISCUSSION
Descriptive Analysis
Both the dependent and the independent variables have 
descriptive statistics shown in Table 1. The exchange 
rate average value is 4.456676, with the maximum and 
minimum values recorded at 5.324335 and 3.904902, 
respectively. The maximum value of  LNG exports 
is recorded as 2.677161, while the minimum value is 
1.020651. It is estimated that the average value of  LNG 
exports is 2.244379. The price of  natural gas attained its 
peak at 1.530259 and its minimum at -0.070557, with a 
mean value of  0.708675. The average price of  petroleum 
oil is 3.356965, with a high of  4.054217 and a low of  
2.486000. The table presents the values of  Skewness, 
Kurtosis, Jarque-Bera, and other descriptive statistics 
parameters for the variables.

Multicollinearity Test
The variance inflation factor (VIF) test was administered 
to determine the presence of  multicollinearity among 
the variables. The Centred VIF values for all variables, as 
presented in Table 2, demonstrate values below 10.0. This 
observation provides evidence that the data utilised in 
this study does not exhibit any multicollinearity concerns.

Unit Root Tests for Stationarity
Hatmanu (2020) stated that checking the stationarity 
of  the variables under study is the first stage in the 
methodology of  most time series modelling research. 
The unit root test was used to determine if  the data 
were stationary and the level of  correlation between the 
variables. For this purpose, we used the ADF (Dickey & 
Fuller, 1979) version of  the Dickey-Fuller test. Stationarity 
at the level is not shown by the variables LEXR, LNGP, 
and LCOP, as seen in Table 3. Therefore, at the 1%, 5%, 
and 10% significant levels (Javed & Husain, 2020), it is 
not possible to reject the null hypothesis, which claims 
the non-stationarity of  these variables. After undergoing 
first-level differencing, represented by I(1), the relevant 
variables are significant and stationary. Thus, it may be 
concluded that H0 is false. However, for low values of  
I(0), LLNG displays a stationary characteristic. This lends 
credence to the use of  the ARDL cointegration modelling 
approach used here.



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ARDL Model
The ARDL model is estimated utilising automatic lag 
selection in E-views version 10, as denoted by Equation 
(2). The findings presented in Table 4 demonstrate 
that the LLNG variable exerts a statistically significant 
influence on the present exchange rate. Nevertheless, the 
present values of  LNGP and LCOP do not demonstrate 
a statistically significant impact on the current LEXR 
value. The results of  the empirical analysis indicate 
that the previous values of  LEXR, LLNG, LNGP, and 
LCOP do not have a statistically significant effect on 
the current value of  LEXR, with the exception of  the 
first lag of  LEXR and the fifth lag of  LNGP. These two 
variables show statistical significance at a 1% level. The 
overarching model demonstrates a significant level of  
statistical significance.

The ARDL Bound Cointegration Test
The ARDL bounds cointegration test is utilised to 
determine the existence of  a long-term relationship 

between the dependent and independent variables. The 
assessment of  the co-integration relationship among the 
variables is conducted by utilising Equation (2). The null 
hypothesis proposed in this study postulates the lack of  
a significant long-term association. Based on the findings 
of  Udoudo et al. (2023) and Nkoro & Uko (2016), when 
the calculated value of  the ‘F’ statistic is lower than the 
critical value I(0), it suggests the lack of  a statistically 
significant relationship between the variables, resulting in 
the failure to reject the null hypothesis. If  the calculated 
‘F’ statistic exceeds the critical value of  I(1), it indicates 
a significant relationship and leads to the rejection of  
the null hypothesis. Conversely, a value falling within 
the range of  I(0) and I(1) is regarded as inconclusive. 
The results displayed in Table 5 demonstrate that the 
calculated ‘F’ statistic (18.14008) exceeds the significance 
levels for both I(0) and I(1), indicating the presence of  
a long-term association between the dependent and 
independent variables.

Table 1: Descriptive Statistics
LEXR LLNG LNGP LCOP

 Mean  4.456676 2.243379 0.708675 3.356965
 Median  4.336665 2.443149 0.694079 3.400700
 Maximum  5.324335 2.677161 1.530259 4.054217
 Minimum  3.904902 1.020651 -0.070557 2.486000
 Std. Dev.  0.411366 0.471463 0.406584 0.481371
 Skewness  0.791239 -1.134865 0.351119 -0.326850
 Kurtosis  2.233465 3.053467 2.33798 2.053623
 Jarque-Bera  5.668329 9.449971 1.707581 2.425416
 Probability  0.058768 0.008871 0.425798 0.297391
 Sum  196.0937 98.70867 31.18171 147.7065
 Sum Sq. Dev.  7.276539 9.557929 7.108355 9.963894
 Observations 44 44 44 44

Source: Author’s estimation (2023)

Table 2: Variance inflation factors
Variable Coefficient Variance Uncentered VIF Centered VIF
LLNG 0.001775 86.5191 3.579859
LNGP 0.001079 6.651327 1.618842
LCOP 0.001533 163.5737 3.222225
C 0.005584 51.84556  NA

Source: Author’s estimation (2023)

Table 3: Results of  ADF Unit-Root test
Variable Level First difference Integration degree

t-statistic p-value t-statistic p-value
LEXR 0.753961 0.9919 -3.887904 0.0047 I(1)
LLNG -4.339716 0.0015 -1.376465 0.5828 I(0)
LNGP -1.43085 0.5579 -4.426946 0.0011 I(1)
LCOP -2.528888 0.1161 -4.322646 0.0014 I(1)

Source: Author’s estimation (2023)



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ARDL Long-Run and Short-Run Estimation
The long-run coefficients shown in Table 6 were calculated 
by estimating the long-run model defined by Equation 
(3) after a co-integration connection had been established 
among the variables. The analysis found that at the 1% 
level of  significance, the exchange rate in Nigeria was 
positively affected by LNG exports. This indicates that 
the exchange rate between the Nigerian Naira and the US 
Dollar would rise in tandem with an increase in the value 
of  LNG exports from Nigeria. This conclusion indicates 
that an increase in the Naira’s export volume of  LNG will 
cause the Naira to depreciate by 1.62 per cent relative to 
the dollar. Djatmiko and Nugroho (2019) and Sieng et al. 
(2020) findings are consistent with the results achieved 
here. There is a negative and insignificant relationship 
between the price of  natural gas and Nigeria’s exchange 
rate. The exchange rate of  Nigeria falls by 0.13 percentage 
points when the price of  natural gas at the Henry Hub 
rises, albeit, not statistically significant. The exchange rate 
in Nigeria is significantly influenced by the price of  Brent 
crude oil, and this influence is negative. An increase in the 
export price of  Brent crude oil results in an appreciation 
of  the Naira by 1.53 per cent. This finding agrees with 
Musa et al. (2020) and Henry (2019).
Equation (4) is used to define the short-term ARDL 
model of  LEXR, which is shown in Table 7 along with 
the variables LLNG, LNGP, and LCOP. According to 
the estimations of  the coefficients, the variable, past 
values of  LEXR does have any effect on its present 
values. The present values and lag 2 value of  LLNG 

exhibit a substantial and negative impact on LEXR, 
with statistical significance at the 5% level. This finding 
does not align with the outcomes observed in the long-
run analysis. Additionally, the lag 1 value demonstrates a 
negative and statistically insignificant influence at the 5% 
level. In examining the association between the LNGP 
and LEXR, it is evident that the present LNGP values 
exhibit a negative and substantial magnitude. This stands 
in support of  the outcome in the long term. In support 
of  the long-term findings, the present value of  LCOP 
exhibits a negative but statistically significant outcome. 
The lag 1 coefficient for LCOP is found to be positive 
and lacks statistical significance. Nevertheless, the 
preceding value of  LCOP at a lag of  2 exhibits a negative 
correlation and is deemed statistically insignificant at a 5% 
level. The error correction term in the econometric model 
exhibits a value of  -0.078286, satisfying the econometric 
criteria of  being negative, statistically significant, and 
smaller than one. This finding suggests that there is a 
feedback or convergence rate of  7.8% towards long-run 
equilibrium. This imply that the short run equilibrium 
adjustment to long run is slow. Moreover, the adequacy 
of  the model’s fit is substantiated by the R-squared, 
Durbin-Watson statistic, and F-statistic as presented 
in Table 7. The adjusted coefficient of  determination, 
denoted as R-squared, elucidates that approximately 
81.7% of  the observed fluctuations in the exchange rate 
can be accounted for by the independent variables under 
consideration. All these metrics indicate that the model 
is well-fitted.

Table 4: The results of  ARDL (3, 5, 5, 3) model
Variable Coefficient Std. Error t-Statistic Prob.
LEXR(-1) 0.921714 0.042240 21.82087 0.0000
LLNG -0.107442 0.053872 -1.994402 0.0563
LLNG(-1) 0.154634 0.088724 1.742860 0.0927
LLNG(-2) -0.041227 0.088831 -0.464110 0.6463
LLNG(-3) 0.121113 0.062833 1.927538 0.0645
LNGP -0.077969 0.037433 -2.082929 0.0469
LNGP(-1) 0.067651 0.037421 1.807862 0.0818
LCOP -0.142644 0.048204 -2.959161 0.0063
LCOP(-1) 0.060314 0.076238 0.791136 0.4358
LCOP(-2) -0.045387 0.082226 -0.551974 0.5855
LCOP(-3) -0.093725 0.074243 -1.262412 0.2176
LCOP(-4) 0.101724 0.038039 2.674196 0.0126
C 0.516563 0.202305 2.553382 0.0166
R-squared 0.996723 Mean dependent var 4.505003
Adjusted R-squared 0.995267 S.D. dependent var 0.399963
S.E. of  regression 0.027516 Akaike info criterion -4.091122
Sum squared resid 0.020443 Schwarz criterion -3.542236
Log likelihood 94.82243 Hannan-Quinn criter. -3.892662
F-statistic 684.4153 Durbin-Watson stat 1.705454
Prob(F-statistic) 0.0000

Source: Author’s estimation (2023)



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Table 5: F-bound Test
F-Bounds Test Null Hypothesis: No levels relationship
Test Statistic Value Signif. I(0) I(1)
F-statistic 18.14008 Asymptotic: n=1000

10% 2.37 3.2
k 3 5% 2.79 3.67

2.50% 3.15 4.08
1% 3.65 4.66

Source: Author’s estimation (2023)

Table 6: Long-Run Estimate
Variable Coefficient Std. Error t-Statistic Prob.
LLNG 1.623251 0.421530 3.850851 0.0007
LNGP -0.131799 0.244077 -0.539990 0.5936
LCOP -1.529220 0.497292 -3.075092 0.0048
C 6.598382 1.175499 5.613261 0.0000
EC = LEXR - (1.6233*LLNG-0.1318*LNGP-1.5292*LCOP + 6.5984)

Source: Author’s estimation (2023)

Table 7: Short-Run Estimate
Variable Coefficient Std. Error t-Statistic Prob.
D(LLNG) -0.107442 0.044578 -2.410203 0.0230
D(LLNG(-1)) -0.079886 0.047419 -1.684688 0.1036
D(LLNG(-2)) -0.121113 0.050657 -2.390835 0.0240
D(LNGP) -0.077969 0.033117 -2.354339 0.0261
D(LCOP) -0.142644 0.043638 -3.268813 0.0029
D(LCOP(-1)) 0.037387 0.041410 0.902856 0.3746
D(LCOP(-2)) -0.007999 0.044511 -0.179711 0.8587
D(LCOP(-3)) -0.101724 0.033186 -3.065261 0.0049
CointEq(-1)* -0.078286 0.007672 -10.20478 0.0000
R-squared 0.816793 Mean dependent var 0.032122
Adjusted R-squared 0.769514 S.D. dependent var 0.053489
S.E. of  regression 0.025680 Akaike info criterion -4.291122
Sum squared resid 0.020443 Schwarz criterion -3.911124
Log likelihood 94.82243 Hannan-Quinn criter. -4.153726
Durbin-Watson stat 1.705454

Source: Author’s estimation (2023)

Diagnostics Tests
In order to enhance the dependability of  the estimates, 
model validation is conducted by performing tests related 
to coefficient stability, model accuracy, and hypotheses 
regarding residuals, including normality, serial correlation, 
and homoscedasticity. The visual representations of  
CUSUM and CUSUM of  Squares serve to illustrate the 
constancy of  coefficients, as indicated by the continuous 
residuals of  CUSUM and CUSUM Squared remaining 
confined within the 95% confidence interval (as depicted 
in Figures 2 and 3). The results obtained from the Ramsey 
RESET test (Table 8) suggest that the null hypothesis 
remains unchallenged, thereby implying that the model 

has been suitably formulated. Moreover, it has been 
ascertained that all conjectures pertaining to residual 
normality, serial correlation, and homoscedasticity have 
been corroborated, thereby signifying the validation of  
the model. The results of  the residual diagnostics are 
shown in Figure 1 and Table 8.

Toda and Yamamoto Causality Test
According to Musa et al. (2019), the presence of  co-
integration implies the presence of  a causal relationship 
in at least one direction. The findings of  the Toda-
Yamamoto causality test are displayed in Table 9. In cases 
where all-time series exhibit the same integration orders 



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of  stationary, the Granger test is employed for causality 
analysis. However, if  the time series displays varying 
integration orders of  stationary, the Toda-Yamamoto 
approach (Toda & Yamamoto, 1995) is utilised for 
conducting the causality analysis. The utilisation of  the 
Toda-Yamamoto Granger causality test in this study was 
motivated by the existence of  variables exhibiting diverse 
orders of  integration.  The empirical analysis conducted 
unveils a conspicuous absence of  substantiated evidence 
pertaining to a causal nexus between LNG exports and 
the exchange rate within the Nigerian context. In a similar 
vein, it is worth noting that there exists no discernible 
evidence of  a causal nexus between the exchange rate 
in Nigeria and the exports of  LNG. A two-way (bi-
directional) causality between the exchange rate of  the 
Nigerian currency, and the prevailing market price of  
Brent crude oil. The Toda-Yamamoto causality analysis 
failed to produce any empirical substantiation for the 
presence of  a causal connection among the remaining 
variables under scrutiny.

Hypothesis Test
Hypothesis testing serves as a scientific methodology 
utilised to discern between two assertions, specifically the 
null hypothesis (H0) and the alternative hypothesis (H1). 
In the realm of  hypothesis testing, should the computed 
P-value be equal to or less than the predetermined level 

of  significance, commonly represented as 0.05 or 5%, it is 
customary to reject the null hypothesis. On the contrary, 
in the event that the computed P-value surpasses the 
designated threshold of  significance, the null hypothesis 
is deemed acceptable. 
Hypothesis 1; H01: LNG exports does not impact the 
exchange rate in Nigeria.
On the basis of  the data that are shown in Table 6, it 
is clear that the P-value associated with LNG exports is 
lower than the critical threshold of  0.05. As a result, we 
conclude that the null hypothesis should not be accepted, 
and we come to the conclusion that the exporting of  
LNG has an impact on the exchange rate in Nigeria.
Hypothesis 2; H02: There is no causal relationship between 
LNG exports and exchange rate in Nigerian.
According to the empirical findings outlined in Table 9, it 
can be observed that the p-values pertaining to the causal 
relationship between LNG exports and the exchange rate 
in Nigeria exceed the threshold of  0.05. Based on our 
analysis, it is concluded that the null hypothesis cannot be 
rejected. This suggests that there is insufficient evidence 
to support the idea that there is a causal relationship 
between LNG exports and the exchange rate in Nigeria. 
In the same way, the p-value between exchange rate and 
LNG exports is greater than 0.05 threshold. Therefore, 
and it is concluded that Naira/Dollar exchange rate does 
cause LNG exports.

Figure 1: Histogram normality test
Source: Author’s estimation (2023)

Table 8: Serial Correlation, Heteroskedasticity, and Ramsey RESET tests
Breusch-Godfrey Serial Correlation 
LM Test

F-statistic 1.124215 Prob. F(2,25) 0.3408
Obs*R-squared 3.300637 Prob. Chi-Square(2) 0.1920

Breusch-Pagan-Godfrey 
Heteroskedasticity test

F-statistic 0.420225 Prob. F(12,27) 0.9418
Obs*R-squared 6.294973 Prob. Chi-Square (27) 0.9005
Scaled explained SS 2.242144 Prob. Chi-Square (27) 0.9989

Ramsey RESET Test  Value df Probability
t-statistic  0.010027 26  0.9921
F-statistic  0.000101 (1, 26)  0.9921

Source: Author’s estimation (2023)



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Figure 2: Cumulative sum (CUSUM) of  recursive residuals
Source: Author’s estimation (2023)

Figure 3: Cumulative sum (CUSUM) of  squares of  recursive residuals
Source: Author’s estimation (2023)

Table 9: Toda and Yamamoto Causality Test Result
Null Hypothesis: Chi-square P-value Granger Causality
LLNG does not Granger Cause LEXR 1.784892 0.6182 No
LEXR does not Granger Cause LLNG 5.397990 0.1449 No 
LNGP does not Granger Cause LEXR 2.979326 0.3948 No
LEXR does not Granger Cause LNGP 6.152830 0.1044 No
LCOP does not Granger Cause LEXR 8.772087 0.0325 Yes 
LEXR does not Granger Cause LCOP 11.36121 0.0099 Yes 

Source: Author’s estimation (2023)

CONCLUSION
In this study, we focused on analysing the impact of  the 
LNG exports on the Naira/Dollar exchange in Nigeria 
from the year 2000 to 2021 using a biannual data set. 
The study’s results unequivocally demonstrated that the 
time series variables of  LNG exports, exchange rate, and 
other intervening variables exhibited integration of  order 
zero and one. A relationship was established between the 
dependent and independent variables, characterised by 

long-run equilibrium, short-run dynamics, and causality. 
The primary aim of  this research endeavour was to assess 
the influence exerted by LNG exports on the exchange 
rate within the context of  Nigeria. The empirical results 
indicate that the exportation of  LNG had a favourable and 
statistically significant influence on the exchange rate. An 
increase in LNG exports causes the Naira to depreciate.  
This assertion was substantiated by the outcomes 
observed in the short term.  The second objective of  this 



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study aimed to establish a causal relationship between 
LNG exports and the exchange rate in Nigeria. The re is 
no causality from Exchange rate to LNG exports. Same 
applies from LLNG exports to Exchange rate. 
Given the observed depreciation of  the Naira caused 
by LNG exports, it is recommended that to build a 
more balanced export portfolio, the government should 
diversify export sectors and support the growth of  
other non-oil and gas industries, such as manufacturing, 
services, and agriculture. Moreover, the impact of  LNG 
exports on the exchange rate highlights the significance 
of  maintaining adequate foreign exchange reserves. 
Sufficient reserves can be utilised to stabilise the Naira 
amidst periods of  volatility arising from shocks in LNG 
exports or other external factors. Policymakers should 
maintain effective monitoring and management of  
reserves to ensure stability in exchange rates.
 
REFERENCES
AboElsoud, M. E. (2010). Measuring the Impact of  

Natural Gas Exports on Economic Growth in Egypt: 
Quantitative Study. Available at SSRN 2860555.

Akpan, E. O. (2009, March). Oil price shocks and Nigeria’s 
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