ASYMMETRIC IMPACTS OF OIL PRICES ON MAJOR OIL · PDF fileThis study investigates the effects...

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:

[email protected], and [email protected].

mailto:[email protected]

mailto:[email protected]



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



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.


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.


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.


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.


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.


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.


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.


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]


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.


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.


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.


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***


Panel rho-Statistic 0.004



Group rho-Statistic -13.681***


















Panel rho-Statistic -3.631***






Model 2 Panel v-Statistic 5.559***






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









Panel rho-Statistic -1.970728**



Group rho-Statistic 10.32920***










Model 2 Panel v-Statistic 6.093007***



Panel ADF-Statistic -2.265328**












Panel rho-Statistic -1.464563*


Panel ADF-Statistic -13.30888**



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.


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



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



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


Total

Trade

Balance


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.


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



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



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



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.

ASYMMETRIC IMPACTS OF OIL PRICES ON MAJOR OIL · PDF fileThis study investigates the effects...

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