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ASYMMETRIC IMPACTS OF OIL PRICES ON MAJOR OIL
EXPORTING AND IMPORTING COUNTRIES
Shudhasattwa Rafiq1
Pasquale Sgro1
1Department of Economics, School of Business, Deakin University, Geelong, Australia Email:
ASYMMETRIC IMPACTS OF OIL PRICES ON MAJOR OIL
EXPORTING AND IMPORTING COUNTRIES
Abstract:
This study investigates the effects oil price shocks on three measures of oil exporters’ and oil importers’
external balances: total trade balance, oil trade balance and non-oil trade balance. We employ three
second generation heterogeneous linear panel models as well as one recently developed non-linear panel
estimation technique which allows for cross sectional dependence. With respect to 28 major oil
exporting countries, while an increase in oil prices leads to an improved real oil trade balance, it is
detrimental to the non-oil and total trade balance situations. This finding might be due to the expenditure
effect arising from increases in proceeds from oil exports. A decrease in oil prices is found to be
beneficial for both total and oil balances in these oil exporting countries. For 40 major oil importers,
they seem to be increasingly shielded from positive oil shocks over the 1970s and 1980s; however, it is
oil price declines that they need to worry about. A decline in oil prices have a negative impact on both
total and real oil trade balances, resulting from increased oil imports in emerging economies. Hence, a
decline in oil prices is beneficial to oil exporters due to the quantity effect outweighing the price effect,
while for oil importers a stable oil price is more desirable than a price decline. These results are
important to note if we are to get a good grasp on the magnitude of the trade and macroeconomic effect
of oil price changes and what the policy responses should be.
Keywords: oil shocks, price asymmetry, oil exporters, oil importers, non-linearity.
JEL Classification: Q20; E24; C33
ASYMMETRIC IMPACTS OF OIL PRICES ON MAJOR OIL
EXPORTING AND IMPORTING COUNTRIES
1. Introduction
As one of the major inputs in global production process, oil is likely to remain the most
prominent source of energy for many decades to come, even under the most optimistic
assumptions about the growth in alternative energy sources. In response to two consecutive oil
shocks in the early and late 1970s, a considerable number of studies have examined the impact
of oil price shocks on economic activity. Pioneering works by Hamilton (1983, 1988, and 1996)
in 1980-90s on the relationship between oil prices and economic activities spurred researchers
to look into this issue in greater detail. Since then, a handful of studies have investigated the
macroeconomic impacts of oil-price shocks, focusing particularly on the effects of oil prices
on economic growth and inflation in oil importing countries (Mork and Olsen, 1994; Barsky
and Kilian, 2004; Hamilton, 2005; among others).
Comprising almost 20 percent of world trade, petroleum products represent the largest
product category in trade value terms (UNCTAD, 2013). Nonetheless, studies investigating the
impacts of oil shocks on external balances are quite few (Bruno and Sachs, 1982; Ostry and
Reinhart, 1992; Gavin, 1990, 1992). While oil prices are expected to have various impacts on
oil exporters’ and importers’ external balances, very few studies actually compare the
differential impacts of oil shocks on oil-exporting and oil-importing countries. Killian et al.
(2009) is arguably the pioneering study to establish that oil prices impact oil-importing and oil-
exporting countries differently. This study has included several different measures of oil-
exporters’ and oil-importers’ external balances, but it has only estimated impulse responses
ignoring the presence of price asymmetry and non-linearity. However, it has been widely
argued by scholars and policymakers that the impact of positive and negative oil shocks on the
macroeconomy vary both in sign and magnitude (Narayan and Sharma, 2011; Apergis, 2015;
Narayan and Gupta, 2015; among others). These asymmetric channels of oil price
transmissions have important implications for the appropriate policy response in the
macroeconomic environment. In addition, in the light of the current decline of commodity
prices, especially, in oil prices, studying the impacts of oil price declines is as warranted as
analysing the impact of positive oil shocks in oil-exporting and oil-importing countries.
Le and Chang (2013) examine the linkages between oil shocks and trade balances in
Malaysia (a net oil exporter), Japan (a net oil importer) and Singapore (an oil refiner) and
correctly point out that oil prices do impact importers’ and exporters’ trade performances
differently. While this study implements contemporary time series econometric methodologies,
it also does not address the asymmetry puzzle within a non-linear framework. By contrast, our
study contributes to the existing oil prices-trade literature by accommodating these two salient
features of this nexus. In particular, our study investigates the effects of both positive and
negative oil shocks on three measures of oil-exporters’ and oil-importers’ external balances:
total trade balance, oil trade balance and non-oil trade balance. This is also the pioneering study
to look at these non-linear asymmetric linkages from two separate perspectives, i.e. the oil
exporters’ and the oil importers’. The study also extends the existing literature by implementing
both linear and non-linear panel data econometric methodological approaches. Using large
panels of major oil-exporting and oil-importing countries, the study employs three second
generation heterogeneous linear panel models and a recently developed non-linear panel
estimation methodological approach, which allows for cross sectional dependence. Two novel
findings of this study are: i) in terms of total and real oil trade balance, oil exporters invariably
benefit from oil price declines accruing from increases in oil exports because of increased
operational flexibility in substituting energy sources in the production processes of the oil
importing countries, like China (Bloch et al., 2015) and a greater pressure on minimizing the
cost of production due to increased global competition, and ii) oil-importing countries are
increasingly shielded from positive oil shocks; however, it is oil price declines that they should
be worried about, given that a decline in oil prices is found to have a negative impact on both
total and real oil trade balances resulting from increased oil imports in emerging net oil
importing economies.
The paper is organised as follows: Section 2 provides a critical review of the existing
literature by linking it with historical oil price trends, while it elaborates on the theoretical
linkages between oil price shocks and the macroeconomy. Section 3 offers a description of data
sources, as well as of the econometric methodologies implemented, while Section 4 reports the
results, including a number of robustness tests. Section 5 discusses policy implications that
emerge from the analysis and concludes the paper.
2. Oil and the macroeconomy
As oil is directly linked to the production process, it can have a significant impact on inflation,
employment and output in the case of oil importing countries. Following two consecutive oil
shocks in the early and late 1970s as can be seen in Figure 1, the seminal paper by Hamilton
(1983) analyses the behaviour of oil prices and the output in the U.S. economy over the period
1948 to 1981, and documents that every U.S. recession between the end of World War II and
1973 (except the 1960-61 recession) has been preceded, with a lag of around three-fourths of
a year, by a dramatic increase in the price of crude petroleum. He further notes that the post-
1972 recessions in the US were mainly caused by OPEC’s supply-oriented approach. In his
subsequent works, Hamilton (1988, 1996) strengthens his conviction that there is an important
correlation between oil shocks and recessions. Since then, researchers have supported and
extended Hamilton’s results, linking oil prices with production and output (Burbridge and
Harrison, 1984; Gisser and Goodwin, 1986; Mork, 1989; Mork and Olsen, 1994; Lardic and
Mignon, 2006). However, over the 1990s and 2000s periods, there have been several instances
of falling oil prices. Jimenez-Rodriguez and Sanchez (2005), Cunado and Gracia (2005), and
Killian and Vigfusson (2011) shed light on the possible effects of major unexpected declines
in oil prices that occurred in 1986, 1998 and late 2008 (Figure 1). All these studies used time
series analyses and only in the context of oil importing developed countries.
Figure 1: Annual WTI Oil Prices (US$ Barrel) and Trends in the Oil and Economy Nexus
Source: British Petroleum Statistical Review or World Energy 2015
In addition to income and production, oil prices may impact an economy’s inflation and
interest rates (Cologni and Manera, 2007), exchange rates (Chen and Chen, 2007); stock prices
(Jones and Kaul, 1996; Huang et al.1996; Sadorsky, 1999; Huang et al., 2005; and Papapetrou,
2001), and unemployment (Keane and Prasad, 1996). Another strand of the literature
investigates the impact of uncertainties arising from oil price volatility (Lee and Ni, 1995;
Ferderer, 1996; Guo and Kliesen, 2005; Rafiq et al., 2009). The above studies document that:
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Oil shocks
and output.
Oil shocks and other
macro variables.
Oil shocks and
external balances.
Oil price asymmetry
and output
Oil price
asymmetry and
external balances?
i) oil price shocks have important effects on aggregate macroeconomic indicators, such as
GDP, interest rates, investment, inflation, unemployment and exchange rates, ii) the impact of
oil price changes on the economy is asymmetric; that is, the negative impact of oil price
increases is not the same as the positive impact of oil price decreases, iii) the majority of these
studies are undertaken in the context of oil-importing developed countries from North America
and Europe, and iv there have been few academic endeavours made to analyse the impact of
oil price shocks on external balances.
In response to the rapid globalization process, a small number of studies emerged
analysing the trade channels through which oil can impact the macroeconomy. Preceded by
Backus and Crucini (2000), this group includes some prominent studies, i.e. Killian et al.
(2009), Bodenstein et al. (2011), and Le and Chang (2013). While Backus and Crucini (2000)
and Bodenstein et al. (2011) use dynamic general equilibrium approaches, both Killian et al.
(2009) and Le and Chang (2013) employ impulse responses type time series analyses to
investigate the nexus between oil prices and trade performances in oil-importing countries.
Backus and Crucini (1998) focus on oil supply and technology shocks as potential drivers of
oil price fluctuations, whereas Bodenstein et al. (2011) and Kilian et al. (2009) include oil
demand shocks as well. While the primary focus of these three papers is the US economy, more
recently, Le and Chang (2013) investigate the impact of oil prices on trade balance, along with
its oil and non-oil components for Malaysia as an oil exporter, Singapore, as an oil refinery,
and Japan, as an oil importer. While all these studies agree on the differing effects due to oil
supply and demand shocks, and on the varying impacts of oil shocks on importers and
exporters, none of the previous literature has considered the asymmetric effects of these shocks,
as well as their differential behaviour for both exporters and importers. As all of them employ
time series techniques, there is a genuine need for identifying these linkages within a panel
framework to comprehend the group (oil-exporters versus oil-importers) dynamics arising from
these impact channels. The study, however, bridges these gaps in the current trade and
commodity prices literature by undertaking both linear and non-linear advanced econometric
methodologies to ascertain the asymmetric impact of oil shocks in two separate panels of the
major oil-importing and oil-exporting countries.
2.1 Theoretical linkages
Oil price changes impact real economic activities from both the supply and demand side
(Jimenez-Rodriguez and Sanchez, 2005). Increases in oil prices are reflected on a higher
production cost that exerts adverse effects on supply (Brown and Yucel, 1999). The higher
production cost lowers the rate of return on investment, which, in turn, affects negatively
investment demand. In addition, increased volatility in oil prices may affect investment by
increasing uncertainty on future price movements (Rafiq et al., 2009). Consumption demand
is also influenced by changes in oil prices as they affect product prices by changing the
production cost. As oil is directly linked to the production process, it can also have a significant
impact on inflation, employment and output; that is an oil price shock can increase inflation by
increasing the cost of production. It also affects employment, as inflationary pressure may lead
to a fall in demand and this, in turn, is expected to lead to a cut in production, which can also
contribute to higher unemployment (Loungani, 1986). The employment-oil price relationship
holds true for not only industrial production, it is equally true for agricultural employment (Uri,
1995). Oil price shocks also affect the implementation of monetary policy through their effect
on inflation. Moreover, rises in oil prices increase the cost of imports in the case of oil-
importing countries (Dohner, 1981).
The asymmetric impact of oil prices on trade can be grouped into four different
combinations: i) the impact of positive oil shocks on net oil exporters, ii) the impact of positive
oil shocks on net oil importers, iii) the impact of negative oil shocks on net oil exporters, and
iv) the impact of negative oil shocks on net oil importers. The effects of positive oil shocks are
comparatively well documented in the current literature. The most obvious impact of oil price
rises on net oil exporting economy is positive. This direct impact can be termed as a revenue
effect, which asserts that increases in oil prices are likely to improve the terms of trade in the
case of the net oil exporters, resulting in increases in oil revenues, an improvement in the trade
balance, and increases in both consumption and investment (Korhonen and Ledyaeva, 2010).
This direct positive shock can be countered by two different indirect effects (Le and Chang,
2013): i) rises in oil prices may result in an inflationary pressure on global markets, which will
eventually increase import prices in both oil-importing and oil-exporting countries. To curb
inflation, monetary authorities across all trading partners may be compelled to increase interest
rates, leading to reduced consumption and investment, thus, lowering the growth rate in the
partner countries. This will result in a decline in the demand for oil and, eventually, resulting
in lower oil exports, while impacting the trade balance in the case of the oil-exporting countries
(the demand effect), and ii) increases in oil prices constitute a negative supply shock to oil-
importing countries’ production processes which may lead to a slowdown in these countries,
reducing their imports and exerting a negative effect on the trade balance of oil-exporting
countries (the supply effect). Overall, the gain from an oil price hike for an oil exporter is
completely dependent on the interplay between the magnitudes of these three effects, namely,
revenues, demand and supply effects. Furthermore, even if the overall impact is positive, there
are other concerns, such as the presence of the Dutch disease, volatility and exhaustibility of
the positive impact and dependency on trade partners (Le and Chang, 2013).
Given that increased oil prices are considered to be a negative terms-of-trade shock for
oil-importing countries, they are assumed to be those that are adversely effected by the event
(IT DOES NOT MAKE FULL SENSE AND IT NEEDS TO BE REWRITTEN OR
EXPLAINED BETTER) (Kim and Loungani, 1992; Backus and Crucini, 2000). Hence, due to
this term-of-trade effect, net oil importers are expected to be left with less production and
exports thereof, putting a downward pressure on their trade balances. However, Killian (2010)
questions this transmission of the negative effect by depicting that this negative cost shock
might not always be large enough, as the cost share of oil could be very small in the domestic
production process for some oil-importing countries (the cost share effect). Given the
increased availability of alternative energy sources, it may not be a big task for the net oil
importers to reduce the cost share of oil in their domestic production process. Furthermore, the
net oil importers can also reduce the adverse effect of oil shocks by increasing non-oil exports
to their oil-exporting counterparts, thus, improving their trade balance (the trade composition
effect). Hence, both the sign and the magnitude of the overall impact resulting from positive
oil shocks for oil-importing countries, are also ultimately determined by the cross interaction
of all these effects.
While oil price declines may reduce oil revenues in the case of oil exporters (the
revenue effect), they can have a direct effect on increasing exports, because of the increased
demand from oil-importing countries (the demand effect). Due to the rapid globalization and
freer trade, countries and corporations are now extremely flexible in changing their fuel mix in
their production process, thus, impacting import allocation instantly. Oil is always and
undoubtedly a preferred source of energy as long as cost and efficiency are concerned. Hence,
a negative oil shock might prove to be a gift rather than a curse to a net oil exporter, with the
changing world trade scenario?????.
A decline in oil prices puts a net oil importer in a better term-of-trade situation. This
might bring negative consequences as well. As oil is a cheaper source, the importers may
increase oil imports and put further pressure on external balances (both the cost share and the
trade composition effect). This in due course may increase both the efficiency and production
in non-oil sectors and, thus, increase exports of non-oil goods and services, leading to a positive
effect on non-oil output and trade balance. Trade is also influenced by the trend in real
exchange rates across trading partners (Beckerman, 1951; Sing, 2001; Chin, 2004; Ozturk,
2006; Le and Chang, 2013). The theoretical linkages between exchange rates and trade are well
documented in Kreuger (1983).
From the above discussion three key observations are in order: i) given the changed
world scenario arising from a greater integration between countries and production processes,
it is very difficult to determine the exact effect of oil shocks on oil-exporting and oil-importing
economies, ii) it is not enough to look at the impact of oil prices at the total trade balance, as a
country or a group of countries may always benefit from mixing up trade and production
allocations between oil and non-oil sectors. This may not be difficult for corporations and
countries as now-a-days production and trade processes are highly flexible and efficient, and
iii) real exchange rate differentials between oil exporters and oil importers moderate this trade
interaction.
3. Data and estimation strategies
This study analyses the impact of oil price shocks on real total trade balance, real oil trade
balance and real non-oil trade balance for 28 major oil-exporting and 40 major oil-importing
countries, spanning the period 1981 to 2013 [Appendix Table 1]. While the rationale for
selecting 28 major oil exporters is data availability, we have selected 40 major oil exporters
based on their aggregate value of oil imports. All these 40 countries in the exporters’ panel
have consistently been importing more than 2 billion US dollar of oil over 5 consecutive years
from 2007 to 2011. We use West Texas Intermediary (WTI) crude oil prices available from
British Petroleum. Data on the overall trade balance, along with its oil and non-oil components,
exchange rates and consumer price indexes (CPIs) are obtained from the World Economic
Outlook database of International Monetary Fund. The CPI is used to convert nominal data
into its real terms, all measured at 2005 prices.
Following Hatemi-J (2012), we decompose oil prices into their cumulative sums of
positive and negative oil shocks. Given the above observations, this study explores the impacts
of oil price shocks on major oil-importing and oil-exporting countries based on one symmetric
(Model 1) and one asymmetric (Model 2) frameworks as follows:
Model 1:
𝑌1𝑖𝑡 = 𝛼1𝑖𝑡 + 𝛽1𝑖𝑡𝑂1𝑖𝑡 + 𝛿1𝑖𝑡𝐸1𝑖𝑡 + 휀1𝑖𝑡 (1)
Model 2:
𝑌2𝑖𝑡 = 𝛼2𝑖𝑡 + 𝛽2𝑖𝑡𝑂2𝑖𝑡+ + 𝛾2𝑖𝑡𝑂2𝑖𝑡
− + 𝛿2𝑖𝑡𝐸2𝑖𝑡 + 휀2𝑖𝑡 (2)
where, Y stands for total trade balance, or oil trade balance, or non-oil trade balance; O, O+,
O- and E represent oil prices, positive oil shocks, negative oil shocks and exchange rates,
respectively. i = 1, 2,……., 28 in the case of oil exporters and i = 1, 2, …..,40 in the case of oil
importers; t = 1981, 1982,………., 2011. At the outset, Ot (oil prices at time t) can be expressed
as the following random walk process:
𝑂𝑡 = 𝑂𝑡−1 + 휀1𝑡 = 𝑂0 + ∑ 휀1𝑖𝑡𝑖=1 , (3)
where t=1,2,….T, the constants O0 is the initial value, and the variables 휀1𝑖 represent the white
noise disturbance term. Positive and negative shocks are defined as: 휀1𝑖+ = max(휀1𝑖, 0), and
휀1𝑖− = min(휀1𝑖, 0), respectively. Hence, we can write 휀1𝑖 = 휀1𝑖
+ + 휀1𝑖− . Therefore:
𝑂𝑡 = 𝑂𝑡−1 + 휀2𝑡 = 𝑂0 + ∑ 휀2𝑖+ + ∑ 휀2𝑖
−𝑡𝑖=1
𝑡𝑖=1 (4)
Finally, the positive and negative shocks of each variable can be defined in a cumulative form
as 𝑂𝑡+ = ∑ 휀2𝑖
+𝑡𝑖=1 and𝑂𝑡
− = ∑ 휀2𝑖−𝑡
𝑖=1 .
Based on the above-specified relationships across variables, the analysis employs both
linear and non-linear panel data estimation procedures to identify the linkage between oil price
shocks and trade. We implement two different linear estimators, namely, Fully Modified Least
Squares (FMOLS) due to Kao and Chiang (2000); the methodology corrects the standard
pooled OLS for serial correlation and endogeneity in the regressors that are normally present
in the long-run relationship (Baltagi and Kao, 2001; Salim and Rafiq, 2012), and three second
generation Mean Group-type estimators, as they allow for panel heterogeneity and cross
sectional dependence (Sadorsky, 2013; Rafiq et al, 2015). We further implement a very recent
non-linear panel estimation methodological approach offered by Kapetanios et al. (2014)
[KMS, 2014, hereafter], which also allows for cross sectional dependence.
3.1 Estimation results
At the outset, we implement Dickey and Fuller (1979), Philips and Perron (1988), Breitung
(2000), Levin et al. (2002) and Im et al. (2003) tests to investigate whether the series follows
a unit root process. [They are not needed given the presence of cross dependence; 2nd generation
unit root tests are sufficient to do the job]. We further employ a very recent non-linear unit root
test offered by Emirmahmutoglu and Omay (2014). The test is particularly appropriate for
examining unit roots in non-linear asymmetric heterogeneous panels. The empirical
distributions of the test, generated by 5000 replications, are used to obtain p-values. For all the
tests, the lag length is chosen using the SIC criterion. The results for O, O+, O-, and E for both
oil-exporting and oil-importing countries are reasonably consistent, indicating that the
variables contain unit roots at their levels1. Prior to estimation, we test for endogeneity of oil
prices and their positive and negative shock components under both of the model settings for
both oil exporters and oil importers. From now onwards, we divide the empirical analysis into
two parts. In the first part we present the results for oil-exporting countries and then we offer
the discussion of the results relating to the oil-importing countries. [WE NEED CROSS
DEPENDENCE TESTS ALONG WIT 2ND GENERATION UNIT ROOT TESTING HERE]
3.1.1 Results for oil-exporting countries
Once it is confirmed that the variables are nonstationary, it is imperative to perform
cointegration tests for each of the two model settings for real total trade balance, real oil trade
balance and real non-oil trade balance. We test for the presence of the long-run relationship
using Pedroni (1999, 2001) panel cointegration tests. The results are reported in the Appendix,
Table 2. They strongly reject the null hypothesis of no-cointegration. Once, the long-run
relationship is established through the cointegration test, we estimate long-run linkages
between oil shocks and real total trade balance, real oil trade balance and real non-oil trade
balance by using the FMOLS methodological approach. It is worth noting here that FMOLS
estimators correct the standard pooled OLS for both serial correlation and endogeneity (Baltagi
and Kao, 2001). The symmetric and asymmetric long-run estimates for oil-exporting countries
are reported in Table 1. With regards to the symmetric model specification (Model 1), oil prices
are positively linked with the total and the real oil trade balance, while negatively impact the
non-real oil trade balance in the case of oil exporters. However, while we look at the
asymmetric model results (Model 2), the new findings are quite remarkable. Increases in oil
prices reduce both the total and the non-oil trade balance and increase the real oil trade balance.
This result is expected based on the clear indication of the revenue effect. However, the
startling result lies with the impact of a decline in oil prices. In this respect, the negative revenue
effect seems to be subsided by increased oil exports due to positive supply and demand effects
from oil-importing countries. However, this increased oil money seems to result in importing
more non-oil goods which have eventually raise the trade deficit in non-oil sectors. All these
events lead to the presence of positive effects on real oil and total trade balances and a negative
impact on the non-oil trade balance, due to the decline in oil prices
[Insert Table 1 about here]
1 Results are not reported considering space limitation. However, results will be provided upon request.
We also check for short-run causality based on the FMOLS approaches. The empirical findings
are presented in Table 2. Having negative and significant error correction terms across all
equations, but one, indicating that the process of error correction from the short-run towards
the long-run equilibrium occurs in virtually across all model specifications. As far as symmetric
models are concerned, there exists bi-directional causality between oil prices and the total trade
balance, the real oil trade balance and the non-oil trade balance across oil-exporting countries.
Our results are consistent with those by Le and Chang (2013) who illustrate that in the case of
Malaysia, an oil-exporter country, oil prices Granger cause both the total and the oil trade
balance. However, in our case oil prices also cause the non-oil trade balance. In terms of the
asymmetric model, there exists bi-directional causality between positive and negative oil price
shocks, as well as between oil [what oil ????] and the non-oil trade balance. At the same time,
there is only uni-directional causality running from the total trade balance to positive oil shocks,
while there is no feedback with regards to oil price increases. There is also bi-directional
causality between oil price declines and the total trade balance, indicating a stronger power of
negative shocks in explaining the total trade balance in the short-run. This is not surprising
given our long-run estimates that in oil-exporting countries negative oil price shocks are rather
more effective in causing total trade than their positive counterparts.
[Insert Table 2 about here]
While the methodology of the FMOLS addresses both model endogeneity and serial
correlation, this estimation procedure is not always reliable if the panel contains cross-sectional
dependence [THEREFORE ALL THE ABOVE WERE REDUNDANT AND COULD BE
ELIMINATED]. Unit root tests assuming cross-sectional independence can have lower power
if cross sectional dependence is in existence in data. There are three tests for identifying cross
sectional dependence in the contemporary panel data econometric literature namely, Friedman
(1937), Frees (1995) and Pesaran’s (2004) cross sectional dependence (CD) tests. The results
of all these three tests are reported in Table 3.
[Insert Table 3 about here]
The findings highlight that there is enough evidence to reject the null hypothesis of cross-
sectional independence. Furthermore, if we assume a homogeneous panel then the models can
be estimated within standard panel regression methodologies, i.e. pooled OLS (POLS),
FMOLS, Dynamic OLS (DOLS), and various fixed effects (FE), random effects (RE) or
Generalized Method of Moments (GMM) specifications (Sadorsky, 2014; Rafiq et al., 2015).
Nonetheless, the assumption that all the drivers that affect emissions and energy intensity
across all the 28 countries are homogenous is quite unrealistic. Moreover, in our panel setting
we have included countries from different economic, social and cultural backgrounds. In this
regard, contemporary models with heterogeneous slope coefficients can be estimated using
Mean Group (MG) estimators (Pesaran, 1997; Pesaran and Smith, 1995) or variants of MG
estimators, i.e. Pesaran’s (2006) Common Correlated Effects Mean Group (CCEMG)
estimators, and the Augmented Mean Group (AMG) due to Bond and Eberhardt (2009) and
Eberhardt and Teal (2010). In addition to allowing for heterogeneous slope coefficients across
group members, these estimators account for cross sectional dependence. The results in
relevance to the major oil exporting countries are reported in Table 4.
[Insert Table 4 about here]
With respect to the symmetric model and the case of negative oil shocks in the asymmetric
model, the results from all three mean group type estimations are pretty consistent with those
from the FMOLS estimates. They confirm that oil price declines bring significantly better trade
balance scenarios for oil exporters than increase in oil prices. While decline in oil prices
increase both total and real oil trade balances of an exporter, they reduce the non-oil trade
balance which is probably due to positive revenues flows arising from greater oil exports (the
demand effect).
However, these results could be misleading if there exist structural breaks within
individual series or linkages which might also lead to non-linear relationships within the model
settings. Hence, this part of the empirical analysis undertakes panel unit root tests with
structural breaks, recommended by Carrion-i-Silvestre et al. (2005). The results are presented
in Table 5. They indicate that all statistics reject the null hypothesis of stationarity for each of
the variables in both homogeneous and heterogeneous long-run versions of the test. In addition
to testing for stationarity, this test allows for identifying as many as five structural break dates
within each series.
[Insert Table 5 about here]
Interestingly enough, for the majority of the series under investigation, break dates are
invariably appear around 1990 and 2010. While 2010 is linked with the recent global financial
crisis, 1990 is linked with the global recession occurred at that time. Consequently, we estimate
a non-linear threshold model allowing for cross sectional dependence as it was introduced by
KMS (2014). The results are provided in Table 6. The non-linear model coefficients are almost
mirror images of the FMOLS estimators offered earlier. All the spatial parameters (ρ, r) of the
KMS (2014) approach are statistically significant (at what level???) and less than one,
indicating that the least squares estimators are consistent (Theorem 1 in KMS, 2014).
According to these findings, all the coefficients are significant in both models. While an
increase in oil prices might not be good news for oil exporters at all times, oil price declines
invariably bring good total and oil trade balance outcomes.
[Insert Table 6 about here]
To sum up our results from all three model specifications with respect to 28 major oil exporters,
we can strongly refute that oil prices have positive effects on both total trade and real oil trade
balances in oil-exporting countries and a negative impact on non-real oil trade balances. Oil
price decreases, nonetheless, bring a stronger positive impact on oil-exporting countries
through increases in both total and real oil trade balances. While positive oil prices could
increase real oil trade balances, they reduce both non-oil and total trade balances in these
countries. As far as non-oil trade balances are concerned, any changes in oil prices bring
adverse impacts in non-oil trade balances in oil-exporting countries, as these changes boost
non-oil consumption. This may be due to the oil revenues effect or the positive price signal
effect with respect to both the households and businesses in these economies. Next, we extend
our analysis to the case of the oil-importing countries.
3.1.2 Results for oil-importing countries
Table 7 provides estimates for 40 major oil-importing countries. In the case of the symmetric
model, oil prices have an inverse relationship with both the total and the oil trade balance,
whereas there exists a positive linkage with the non-oil trade balance. With respect to the
asymmetric model settings, while an increase in oil prices does not have any significant impact,
oil price declines cast a very small significant negative impact on both trade and oil trade
balances and a positive effect on the non-oil trade balance. This might be due to the fact that
declines in oil prices boost up fuel imports in these economies, placing them in a worse-off
trade balance situation against their oil-exporting counterparts.
[Insert Table 7 about here]
The causality results in Table 8 indicate the presence of bi-directional causality between oil
prices and the total, the real and the non-real oil trade balance with respect to the symmetric
model, whereas in the asymmetric model settings negative oil price shocks are comparatively
more powerful than positive shocks. These results are consistent with those provided by Le and
Chang (2013) inference regarding the case of Japan, i.e. an oil importer, where the authors also
find that oil prices Granger cause the oil trade balance. While there is bidirectional causality
between price increase and decreases with the oil trade balance, there is only bidirectional
causality between oil price declines and the total trade balance and uni-directional causality
running from oil price declines to the non-oil trade balance. The prominence of negative
shocks, vis-à-vis positive price innovations, might be due to three major shifts in global
economic trends since the 1970s oil shock: i) oil-importing countries are now more immune to
oil price rises due to the increased flexibility in their technological and financial operations, ii)
there has been an increase influx of alternative sources of energy over the last four decades
which might have reduced the dependency on oil in these major oil importers, and iii) the
greater power of a decline in oil prices can be due to the demand effect from all the emerging
countries, i.e. China, India, Brazil, Indonesia and so forth. As many of these net oil-importing
countries are growing at an unprecedented rapid pace over the recent years, declines in oil
prices are always welcomed with huge boost in oil-importing activities.
[Insert Table 8 about here]
Since cross-sectional dependence test results presented in Table 9 indicate that there are cross
sectional dependences across all three panel settings for the oil importers, we perform mean
group type estimations for the oil importers as well. [SOME BRIEF DISCUSSION IS ALSO
NEEDED HERE]
[Insert Table 9 about here]
According to the long-run estimates provided in Table 10, it is apparent that these oil-importing
countries are more insusceptible to positive oil shocks. While any type of shock does not pose
any impact on the total trade balance, the real oil trade balance seems to be negatively affected
by oil price declines.
[Insert Table 10 about here]
As the structural break tests point our breaks mainly around 2010 and 1990 (Table 11), we
perform non-linear threshold estimations. The threshold results further lend support to the fact
that and oil price declines are rather detrimental for oil-importing countries as far as both the
total and the real oil trade balance are concerned (Table 12).
[Insert Tables 11 and 12 about here]
Overall, our results denote that oil-importing countries are generally indifferent to oil price
increases, while both their trade and real oil trade balances suffer from the impact of negative
shocks during oil price declines. The non-oil trade balance improves when oil prices go down
due to the lower cost associated with imported items. In other words, stable oil prices are more
desirable than drastic price declines.
4 Robustness checks
We check for the robustness of our results by examining the impact of both symmetric and
asymmetric oil shocks on real oil exports and real oil imports of net oil exporters and importers,
respectively. In these regard, the analysis performs all three (a brief naming here is required)
empirical methodological approaches to identify long-run estimates and short-run causalities.
The findings for oil exporters are presented in Tables 13 and 14, while those for oil importers
are reported in Tables 15 and 16.
The results from both long-run estimates and causality test indicate that for an oil
exporter, declines in oil prices invariably increase oil exports in both the short- and the long-
run across all three estimation approaches. Therefore, positive oil shocks significantly decrease
exports with respect to the linear FMOLS and non-linear KMS estimators. Hence, as far as oil
exports are concerned, declines in oil prices are always a preferred scenario for these 28 major
net oil-exporting countries.
[Insert Tables 13 and 14 about here]
According to the findings reported in Tables 15 and 16, while oil imports are not significantly
affected by positive oil shocks, under the two alternative estimates, i.e. FMOLS and KMS, oil
imports significantly increase in response to declines in oil prices in the 40 major oil-importing
nations. This lends strong support to our previous conviction that the demand effect from
emerging net oil-importing economies is placing the net oil exporter in a better trade position
in the case of oil price declines, albeit the magnitude of this increase in oil imports seems to be
relatively small. Hence, a negative shock in oil prices increases oil imports, putting further
negative pressure to real oil trade balances, as shown earlier.
[Insert Tables 15 and 16 about here]
The findings from all the robustness checks are highly consistent with the main empirical
results, while decreases in oil prices are found to be very beneficial for oil exporters; by
contrast, it could place oil importers in worse positions.
5. Conclusions and policy implications
This study investigated the impact of both symmetric and asymmetric oil shocks on the total
trade balance, the real oil trade balance and the non-real oil trade balance in 28 major oil-
exporting and 40 major oil-importing countries. In line with a battery of preliminary tests for
stationary, cross-sectional dependence, structural breaks and cointegtation, the analysis
employed three major estimators to detect the short- and long-run interactions. In particular,
the analysis undertook four major linear procedures, i.e. FMOLS and three Mean Group-type
estimates, and a non-linear threshold type methodology by KMS (2014). The analysis also
performed robustness checks by linking oil prices and exports for oil exporters and oil prices
and imports for oil importers. The robustness check results lent strong support to the deductions
regarding trade and oil linkages from the main estimations.
According to the findings, increases in oil prices boosted the real oil trade balance due
to the presence of the revenues effect; however, they decreased both total and non-real oil trade
balances arising from the higher price or the expenditure effect. By contrast, declines in oil
prices increased both total and real oil trade balances. This positive scenario occurs because of
increased exports in response to the greater demand from oil-importing economies, i.e. the
demand effect. This result was further supported by the robustness check for oil exports as they
were found to be significantly growing in times of oil price declines; for the case of oil
importers, oil import increased significantly during the decline process. The non-oil trade
balance was negatively impacted in response to both positive and negative oil shocks, as any
sort of change in oil prices provided a positive revenue signal to the oil-exporting countries.
The results for the major oil importers revealed that these countries were shielded
against positive oil shocks. This might be due to their flexibility in financial and productive
operations, and the presence of energy alternatives to oil. Declines in oil prices rather seem to
play a stronger role in reducing both the trade and the real oil trade balance. As the robustness
check results indicated, one of the reasons for this adverse effect was increased imports from
these countries in the timing of price declines. The non-real oil trade balance increased during
negative oil price shocks, as oil price declines boosted exports in these oil-importing
economies.
Two novel findings of this study are: i) in terms of the total and the real oil trade balance,
oil exporters invariably benefit from oil price declines accruing from increases in oil exports,
because of increased operational flexibility in substituting energy sources in the production
processes in oil-importing countries, i.e. China (Bloch et al., 2015) and a greater pressure on
minimizing the cost of production due to an increased global competition, and ii) as a logical
consequence of the pervious findings, although oil-importing countries are increasingly
shielded from positive oil shocks, it is oil price declines that they should be worried about
because such declines in oil prices put a negative pressure on their total and real oil trade
balances, while declines in oil prices increase oil imports due to the presence of the demand
effect.
In summary, while declines in oil prices are beneficial to oil exporters, for an oil
importer stable oil prices are more desirable than such price declines. These results are important
to note if we are to get a good grasp on the magnitude of the trade and macroeconomic effect of oil
price changes and what the policy responses should be. [IT HAS BEEN STATED ABOVE, WHILE IT
LEAVES MORE CONCERNS THAT IT INFORMS THE AUDIENCE-I RECOMMEND
DELETION]
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Appendix Table 1: Country Selection
Major Oil Importers Major Oil Exporters
Australia Indonesia Singapore Algeria Malaysia
Austria Ireland Spain Angola Mexico
Bangladesh Israel Sri Lanka Argentina Nigeria
Belgium Italy Sweden Bahrain Norway
Brazil Japan Switzerland Canada Oman
Chile Kenya Taiwan Colombia Qatar
Hong Kong Rep. of Korea Thailand Rep. of Congo Saudi Arabia
China Mainland Morocco Tunisia Cote d'Ivoire Sudan
Dominican Republic Netherlands Turkey Denmark Syria
Finland New Zealand United States Ecuador Trinidad and Tobago
Germany Pakistan Egypt UAE
Greece Peru Equatorial Guinea United Kingdom
Guatemala Philippines Iran Venezuela
Hungary Portugal Kuwait
India Romania Libya Note: Oil exporting countries are selected on the basis of data availability and importers are the ones who have been consistently importing more than 2 billion USD of oil since 2007.
Appendix Table 2: Panel Cointegration Tests for Oil Exporters
Models Test Total Trade
Balance
Real Oil Trade
Balance
Real Non-oil
Trade Balance
Model 1 Panel v-Statistic -0.715
Panel rho-Statistic -0.881
Panel PP-Statistic -7.475***
Panel ADF-Statistic -19.727***
Group rho-Statistic -2.265**
Group PP-Statistic -11.548***
Group ADF-Statistic -10.811***
Model 2 Panel v-Statistic -1.204
Panel rho-Statistic 0.004
Panel PP-Statistic -7.755***
Panel ADF-Statistic -9.396***
Group rho-Statistic -13.681***
Group PP-Statistic -17.181***
Group ADF-Statistic -12.506***
Model 1 Panel v-Statistic -0.605
Panel rho-Statistic 3.508
Panel PP-Statistic -12.551***
Panel ADF-Statistic -8.157***
Group rho-Statistic -2.809***
Group PP-Statistic -11.036***
Group ADF-Statistic -8.498***
Model 2 Panel v-Statistic -0.063
Panel rho-Statistic 1.209
Panel PP-Statistic -3.066***
Panel ADF-Statistic -2.464***
Group rho-Statistic -3.616***
Group PP-Statistic -11.737***
Group ADF-Statistic -8.214***
Model 1 Panel v-Statistic -1.280
Panel rho-Statistic -3.631***
Panel PP-Statistic -8.079***
Panel ADF-Statistic -7.434***
Group rho-Statistic -2.696***
Group PP-Statistic -7.326***
Group ADF-Statistic -6.369***
Model 2 Panel v-Statistic 5.559***
Panel rho-Statistic 1.155
Panel PP-Statistic -4.155***
Panel ADF-Statistic -5.571***
Group rho-Statistic -7.896***
Group PP-Statistic -12.427***
Group ADF-Statistic -9.766*** Note: ***, ** and * represent 1%, 5%, and 10%, respectively. Lag length is chosen by Akaike Information criterion.
Appendix Table 3: Panel Cointegration Tests for Oil Importers
Test Models Total Trade
Balance
Real Oil Trade
Balance
Real Non-oil Trade
Balance
Model 1 Panel v-Statistic -5.063964
Panel rho-Statistic -4.220241***
Panel PP-Statistic -7.884773***
Panel ADF-Statistic -10.17858***
Group rho-Statistic -0.284532***
Group PP-Statistic -8.647552***
Group ADF-Statistic -7.818435***
Model 2 Panel v-Statistic -4.513772
Panel rho-Statistic -1.970728**
Panel PP-Statistic -10.58748***
Panel ADF-Statistic -11.68358***
Group rho-Statistic 10.32920***
Group PP-Statistic -10.48713***
Group ADF-Statistic -10.15593***
Model 1 Panel v-Statistic -2.901856
Panel rho-Statistic -3.667490***
Panel PP-Statistic -7.394724***
Panel ADF-Statistic -15.71536***
Group rho-Statistic -6.706578***
Group PP-Statistic -21.98385***
Group ADF-Statistic -18.58668***
Model 2 Panel v-Statistic 6.093007***
Panel rho-Statistic 0.847358
Panel PP-Statistic -6.050206***
Panel ADF-Statistic -2.265328**
Group rho-Statistic -3.356628***
Group PP-Statistic -28.46455***
Group ADF-Statistic -23.71459***
Model 1 Panel v-Statistic -6.321890
Panel rho-Statistic -4.692842***
Panel PP-Statistic -9.195700***
Panel ADF-Statistic -10.22901***
Group rho-Statistic -7.384340***
Group PP-Statistic -5.235351***
Group ADF-Statistic -6.499957***
Model 2 Panel v-Statistic -6.281358
Panel rho-Statistic -1.464563*
Panel PP-Statistic -12.87004***
Panel ADF-Statistic -13.30888**
Group rho-Statistic -7.911356***
Group PP-Statistic -9.636149***
Group ADF-Statistic -8.426279*** Note: ***, ** and * represent 1%, 5%, and 10%, respectively. Lag length is chosen by Akaike Information criterion.
Table 1: Long-run FMOLS estimates for Oil Exporters
Dep
Variable/ Coefficients
Total Trade Balance Real Oil Trade Balance Real Non-oil Trade Balance
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
O 0.062171***
(59.9450)
0.174591***
(53.2032)
-0.135246***
(-31.2963)
O+ -1.65E-05***
(-5.00511)
0.006419**
(2.05276)
-0.017427***
(-5.38563)
O- 0.000162***
(17.21120)
0.001645***
(2.23012)
-0.003518***
(-4.48879) Note: ***, ** and * represent 1%, 5%, and 10%, respectively. t-value in parenthesis.
Table 2: Causality Test Result for Oil Exporters
Sources of causation
Total
Trade
Balance
Short Run Long Run EC
Δttb ΔO ΔO+ ΔO- Model 1 Model 2
Δttb - 113.0729***
(0.0000)
3.533896*
(0.0601)
22.88660***
(0.0000)
-0.050871**
(0.0300)
-0.181142***
[0.000000]
ΔO 46.00801***
(0.0000)
- - - 44.88739
(0.115)
-
ΔO+ 1.174349
(0.2785)
- - 37.38416***
(0.0000)
- -0.715511***
(0.0000)
ΔO- 125.8683***
(0.0000)
- 73.71197***
(0.0000)
- - -4.781070***
(0.0000)
Real Oil
Trade
Balance
Δrotb ΔO ΔO+ ΔO- Model 1 Model 2
Δrotb - 92.17109***
(0.0000)
136.2077***
(0.0000)
125.7656***
(0.0000)
-
0.033617***
(0.008)
-0.702802**
(0.00000)
ΔO 128.6548***
(0.0000)
- - - -
0.927936***
(0.000)
-
ΔO+ 288.8712***
(0.0000)
- - 742.9238***
(0.0000)
- -0.074938*
(0.08738)
ΔO- 394.6679***
(0.0000)
- 209.8310***
(0.0000)
- - -5.354057***
(0.00000)
Real non-
Oil Trade
Balance
Δrnotb ΔO ΔO+ ΔO- Model 1 Model 2
Δrnotb - 98619.16***
(0.0000)
5.00E+11***
(0.0000)
5640.264***
(0.0000)
-4.644**
(0.038)
-0.107501***
(0.00000)
ΔO 298.7606***
(0.0000)
- - - 259.9155***
(0.0000)
-
ΔO+ 252.2165***
(0.0000)
- - 64.62844***
(0.0000)
- -0.409699***
(0.01325)
ΔO- 125.4637***
(0.0000)
- 145.5645***
(0.0000)
- - -9.77E-07
(0.17778) Note: ***, ** and * represent 1%, 5%, and 10%, respectively. p-value in parenthesis.
Table 3: Cross Sectional Dependence Tests for Oil Exporters
Tests Pesaran Frees Freidman
CD test p-value CD(Q) test p-value CD test p-value
Total Trade
Balance
Model I
RE Estimation 20.922
***
0.0000 7.526 *** 0.0000 208.154 *** 0.0020
FE Estimation 20.654*** 0.0000 7.526 *** 0.0000 208.154 *** 0.0020
Model II
RE Estimation 19.226*** 0.0001 8.114*** 0.0000 205.319** 0.0000
FE Estimation 20.654*** 0.0000 7.237*** 0.0000 204.416** 0.0000
Real Oil Trade
Balance
Model I
RE Estimation 19.766*** 0.0000 9.372*** 0.0000 241.372*** 0.0000
FE Estimation 19.481*** 0.0000 9.720*** 0.0000 237.100*** 0.0000
Model II
RE Estimation 20.264*** 0.0000 9.056*** 0.0000 182.481*** 0.0000
FE Estimation 13.379*** 0.0000 8.002*** 0.0000 144.999*** 0.0000
Real non-Oil Trade
Balance
Model I
RE Estimation 15.061*** 0.0000 5.650*** 0.0000 182.841*** 0.0000
FE Estimation 13.479*** 0.0000 5.042*** 0.0000 159.314*** 0.0000
Model II
RE Estimation 16.415*** 0.0000 6.188*** 0.0000 142.164*** 0.0000
FE Estimation 9.045*** 0.0000 5.529*** 0.0000 96.652*** 0.0000
Note: FE and RE denote fixed and random effect estimations. ***, **, and * indicate that the test statistics is significant at 1%, 5%, and
10% levels, respectively.
Table 4: Long-run Estimates under Cross-Sectional Dependence for Oil Exporters
Dep.
Var.
Total Trade Balance Real Oil Trade Balance Real Non-oil Trade Balance
MG AMG CCEMG MG AMG CCEMG MG AMG CCEMG
O 0.14**
(2.05)
0.18**
(2.04)
0.12
(1.53)
0.27***
(3.61)
0.28***
(3.68)
0.31***
(3.19)
-0.09**
(-3.87)
-0.13***
(-4.01)
-0.16***
(-3.59)
O+ 6304.25
(1.00)
243.86
(1.00)
86.87
(1.00)
525.78
(1.00)
136.86
(1.01)
747.31
(1.00)
5818.86
(1.02)
-4953.32
(-1.00)
5064.93
(-1.00)
O- 0.11**
(1.78)
0.14**
(2.51)
0.11*
(2.01)
0.26***
(1.00)
0.28***
(3.73)
0.31***
(2.67)
-0.22***
(-3.41)
-0.24***
(-3.68)
-0.21***
(-4.14) Note: z- values are given in the parentheses. ***, **, and * indicate that the test statistics is significant at 1%, 5%, and 10% levels, respectively.
Table 5: Panel Unit Root Test with Structural Breaks (Allowing for Cross Section
Dependence) for Oil Exporters
Variables Carrion-i-Silvestre et al. (LM(λ)) Break Location (Tb)
Test Bootstrap Critical
Value (5%)
TTB
Ψ𝑡
18.4796** 6.8255 1997, 2006
Ψ𝐿𝑀
s
17.2546** 6.8255
ROTB
Ψ𝑡
22.7942** 7.3439 1985, 2010, 1994
Ψ𝐿𝑀
s
21.2444** 7.3439
RNOTB
Ψ𝑡
13.3757** 9.0986 2008, 1984, 1987, 1981, 2010
Ψ𝐿𝑀
s
12.5669** 9.0986
O
Ψ𝑡
14.2213** 9.6200 1988, 1990, 1993, 1995, 1996
Ψ𝐿𝑀
s
13.0725** 9.6200
O+
Ψ𝑡
9.1273** 9.0013 1988, 1989, 1993, 1998, 2005
Ψ𝐿𝑀
s
8.3028** 9.0013
O-
Ψ𝑡
-4.152** -3.227 2009
Ψ𝐿𝑀
s
-4.109** -3.227
Note: The number of unknown structural breaks is set to five. The null of the LM (λ) test implies stationarity. The Gauss
procedure was conducted based on the code provided by Ng & Perron (2001). The tests were computed using the Bartlett kernel,
and all of the bandwidth and lag lengths were chosen according to 4(T/100)2/9. The bootstrap critical values allow for cross-
sectional dependence. Individual country break dates were also computed (available upon request). TTB, ROTB and RNOTB
stands for total trade balance, real oil trade balance and real non-oil trade balance, respectively.
Table 6: Non-Linear Estimates by KMS (2014) for Oil Exporters
Dep
Variable/ Coefficients
Total Trade Balance Real Oil Trade Balance Real Non-oil Trade Balance
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
O 0.0022***
(0.003)
0.0002***
(0.0000)
-0.0003***
(0.0001)
O+ -0.0023***
(0.0005)
0.0005***
(0.0001)
-0.005***
(0.001)
O- 0.0023***
(0.0003)
0.0003***
(0.0000)
-0.0033***
(0.0004)
r 0.0275 0.1825 0.0275 0.0275 0.0275 0.0650
ρ -0.0436***
(0.0062)
-0.0435***
(0.0064)
-0.0579***
(0.005)
-0.0638***
(0.0059)
-0.0891**
(0.0123)
0.3127**
(0.0141) Notes: The estimates are PCCE-KMS estimators recommended by Pesaran (2006), wherein ft = {ӯt, t}. r and ρ are the threshold and spatial
autoregressive parameters, respectively. . ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Table 7: Long-run FMOLS estimates for Oil Importers
Dep
Variable/
Coefficients
Total Trade Balance Real Oil Trade Balance Real Non-oil Trade Balance
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
O - 0.098992***
(-89.1510)
-0.006183 ***
(-7.00096)
0.129194 ***
(-18.0898)
O+ -0.279644
(-0.00072)
- -9.48E-07
[-0.26970]
-1.489959
(-0.13175)
O- -3.48E-06 ***
(-2.71204)
- -1.32E-06 **
(-2.62445)
3.38E-07 ***
(8.82474) Note: ***, ** and * represent 1%, 5%, and 10%, respectively. t-value in parenthesis.
Table 8: Causality Test Result for Oil Importers
Sources of causation
Total
Trade
Balance
Short Run Long Run EC
Δttb ΔO ΔO+ ΔO- Model 1 Model 2
Δttb - 2928.767***
(0.0000)
0.002530
(0.9599)
613.3300***
(0.0000)
-0.749062 ***
(0.0000)
-0.942065***
(0.0000)
ΔO 3883.367***
(0.0000)
- - - -0.835217***
(0.0000)
ΔO+ 0.001899
(0.9652)
- - 0.002065
(0.9638)
-2.51E-10
(1.25018)
ΔO- 530733.2***
(0.0000)
0.007975
(0.9288)
-0.948093***
(0.0000)
Real Oil
Trade
Balance
Δrotb ΔO ΔO+ ΔO- Model 1 Model 2
Δrotb - 630.1203***
(0.0000)
23824.52***
(0.0000)
18108.10***
(0.0000)
-0.750194***
(0.0000)
-0.264234***
(0.0000)
ΔO 4142.140***
(0.0000)
- - - -11.20127***
(0.00000)
-
ΔO+ 2144.027***
(0.0000)
- - 42454.39***
(0.0001)
- -14.88216***
(0.0000)
ΔO- 1754.667***
(0.0000)
- 3220.428***
(0.0000)
- - -9.847991***
(0.0000)
Real non-
Oil Trade
Balance
Δrnotb ΔO ΔO+ ΔO- Model 1 Model 2
Δrnotb - 1807.076***
(0.0000)
0.001857
(0.9991)
0.001895
(0.9991)
-0.796351 ***
(0.0000)
-0.694538 ***
(0.00000)
ΔO 168047.2
(0.0000)
- - - -0.605715 ***
(0.03676)
ΔO+ 0.002659
(0.9987)
- - 0.007594
(0.9962)
-1.19E-08
(0.401935)
ΔO- 50179.16***
(0.0000)
0.001742
(0.9991)
-4.754073***
(0.0000) Note: ***, ** and * represent 1%, 5%, and 10%, respectively. p-value in parenthesis.
Table 9: Cross Sectional Dependence Tests for Oil Importers
Tests Pesaran Frees Freidman
CD test p-value CD(Q) test p-value CD test p-value
Total Trade
Balance
Model I
RE Estimation 10.016*** 0.0000 13.613*** 0.0000 154.344*** 0.0000
FE Estimation 8.674*** 0.0000 12.552*** 0.0000 149.390*** 0.0020
Model II
RE Estimation 55.505*** 0.0000 23.056*** 0.0000 554.019** 0.0000
FE Estimation 89.790*** 0.0000 24.500*** 0.0000 811.530** 0.0000
Real Oil Trade
Balance
Model I
RE Estimation 8.101*** 0.0000 11.953*** 0.0000 132.109*** 0.0000
FE Estimation 4.359*** 0.0000 11.858*** 0.0000 95.919*** 0.0000
Model II
RE Estimation 57.698*** 0.0000 24.566*** 0.0000 522.437*** 0.0000
FE Estimation 72.050*** 0.0000 22.067*** 0.0000 608.410*** 0.0000
Real non-Oil Trade
Balance
Model I
RE Estimation 13.483*** 0.0000 14.509*** 0.0000 161.608*** 0.0000
FE Estimation 14.201*** 0.0000 14.582*** 0.0000 176.649*** 0.0000
Model II
RE Estimation 56.930*** 0.0000 24.808*** 0.0000 526.805*** 0.0000
FE Estimation 83.082*** 0.0000 25.804*** 0.0000 737.690*** 0.0000
Note: FE and RE denote fixed and random effect estimations. ***, **, and * indicate that the test statistics is significant at 1%, 5%, and
10% levels, respectively.
Table 10: Long-run Estimates under Cross-Sectional Dependence for Oil Importers
Dep.
Var.
Total Trade Balance Real Oil Trade Balance Real Non-oil Trade Balance
MG AMG CCEMG MG AMG CCEMG MG AMG CCEMG
O -0.021
(-0.22)
-0.029
(-0.31)
-0.146
(-1.00)
-0.296**
(-2.59)
-0.435**
(-2.11)
-0.501*
(-1.86)
0.1540**
(2.11)
0.164**
(2.01)
0.154
(1.63)
O+ 3.10e+07
(1.00)
-46588.65
(-1.00)
242.378
(1.35)
4550142
(1.00)
-6985.775
(-1.00)
-37.130
(-1.19)
1.76e+07
(1.00)
-20100.44
(-1.00)
124.009
(0.96)
O- -0.086
(-0.37)
-0.107
(1.00)
-0.301
(-1.08)
-0.090
(-0.65)
-0.199*
(-1.68)
-0.302**
(-2.12)
0.042
(0.29)
0.048331
(0.32)
0.01249
(0.07) Note: z- values are given in the parentheses. ***, **, and * indicate that the test statistics is significant at 1%, 5%, and 10% levels, respectively.
Table 11: Panel Unit Root Test with Structural Breaks (Allowing for Cross Section
Dependence) for Oil Importers
Variables Carrion-i-Silvestre et al. (LM(λ)) Break Location (Tb)
Test Bootstrap Critical
Value (5%)
Oil Importer’s
TTB
Ψ𝑡
18.7085** 9.4154 1983, 1986, 1989, 1998
Ψ𝐿𝑀
s
17.7896** 9.4154
ROTB
Ψ𝑡
10.8766** 9.3390 1988, 1989, 1990, 1997, 2005
Ψ𝐿𝑀
s
10.4668** 9.3390
RNOTB
Ψ𝑡
9.7795** 9.2822 1981, 2010, 2010, 2011, 1995
Ψ𝐿𝑀
s
15.1431** 9.2822
O
Ψ𝑡
-6.121** -3.316
Ψ𝐿𝑀
s
-5.985** -3.316
O+
Ψ𝑡
-13.752** -3.400 1998
Ψ𝐿𝑀
s
-12.332** -3.400
O-
Ψ𝑡
-4.152** -3.227 2009
Ψ𝐿𝑀
s
-4.109** -3.227
Note: The number of unknown structural breaks is set to five. The null of the LM (λ) test implies stationarity. The Gauss
procedure was conducted based on the code provided by Ng & Perron (2001). The tests were computed using the Bartlett kernel,
and all of the bandwidth and lag lengths were chosen according to 4(T/100)2/9. The bootstrap critical values allow for cross-
sectional dependence. Individual country break dates were also computed (available upon request). TTB, ROTB and RNOTB
stands for total trade balance, real oil trade balance and real non-oil trade balance, respectively.
Table 12: Non-Linear Estimates by KMS (2014) for Oil Importers
Dep
Variable/
Coefficients
Total Trade Balance Real Oil Trade Balance Real Non-oil Trade Balance
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
O -0.0048***
(0.0001)
-0.0082***
(0.0045)
0.0086***
(0.0019)
O+ 0.1358**
(0.0164)
- 0.1963***
(0.0056)
-0.2975**
(0.0214)
O- -0.0032***
(0.000)
- -0.0001***
(0.0000)
0.0086***
(0.0019)
r 0.0275 0.0042 0.1425 0.1850 0.0275 0.0245
ρ 0.0043***
(0.0078)
0.0042***
(0.0078)
-0.0002***
(0.0001)
-0.0002***
(0.0002)
-0.0004***
(0.0002)
-0.0044**
(0.0024) Notes: The estimates are PCCE-KMS estimators recommended by Pesaran (2006), wherein ft = {ӯt, t}. r and ρ are the threshold and spatial
autoregressive parameters, respectively. . ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Table 13: Long-run Estimates with Respect to Oil Export for Oil Exporters
Methods Mean Group FMOLS KMS
MG AMG CCEMG
O 0.3117211***
(4.12)
0.3329477 **
(4.48)
0.3193476***
(4.38)
0.199446***
(7.6041958)
0.00069***
(0.00034)
O+ -302.8372
(-1.00)
-40.39888
(-0.98)
-407.0389
(-1.00)
-0.007157*
(-1.87205)
-0.0015***
(0.00234)
O- 0.3034213***
(4.59)
0.3212515***
(4.43)
0.2966485***
(3.43)
0.001902**
(2.11565)
0.0009***
(0.0000) Note: z, t and p- values relating to mean group, FMOLS and KMS are given in the parentheses. ***, **, and * indicate that the test
statistics is significant at 1%, 5%, and 10% levels, respectively.
Table 14: Causality Test with Respect to Oil Export for Oil Exporters
Sources of causation
Short Run Long Run EC
ΔExport ΔO ΔO+ ΔO- Model 1 Model 2
ΔExport 1.058317
(0.3036)
163.5611***
(0.00000)
164.4291***
(0.00000)
-0.062741***
(0.00000)
-0.704085
(0.00000)
ΔO 6.700502***
(0.0096)
0.835128
(2.925483)
ΔO+ 291.5155***
(0.00000)
722.5766***
(0.00000)
-0.060372
(0.13247)
ΔO- 522.9261***
(0.00000)
245.8276***
(0.00000)
-4.696484****
(0.000000)
Note: ***, ** and * represent 1%, 5%, and 10%, respectively. p-value in parenthesis.
Table 15: Long-run Estimates with Respect to Oil Import for Oil Importers
Methods Mean Group FMOLS KMS
MG AMG CCEMG
O -810502.5
(-1.00)
415340.6
(1.00)
1294537
(1.00)
-30.93620***
(70.3075)
-0.0004***
(0.0000)
O+ -9896903
(-1.38)
-6518463
(-1.42)
-2397133
(-0.81)
-267.0521
(-0.06029)
-109.416
(0.0057)
O- -480792.7
(1.00)
1041219
(1.00)
679374.1
(1.00)
1.81E-05**
(1.82126)
0.0004***
(0.000) Note: z, t and p- values relating to mean group, FMOLS and KMS are given in the parentheses. ***, **, and *
indicate that the test statistics is significant at 1%, 5%, and 10% levels, respectively.
Table 16: Causality Test with Respect to Oil Import for Oil Importers
Sources of causation
Short Run Long Run EC
ΔImport ΔO ΔO+ ΔO- Model 1 Model 2
ΔImport 661.9640***
(0.00000)
0.006623
(1.00000)
0.001047
(1.00000)
-0.005356***
(0.0000)
-0.117232***
(0.00000)
ΔO 57.39723***
(0.0000)
-0.024134***
(0.00000)
ΔO+ 0.000144
(0.719482)
0.004076
(1.000000)
-3.74E-09
(-0.09097)
ΔO- 194.8619***
(0.00000)
0.005280
(1.00000)
-0.054605***
(0.00000)
Note: ***, ** and * represent 1%, 5%, and 10%, respectively. p-value in parenthesis.