Direct and indirect effects of oil price shocks on ...

ISM UNIVERSITY OF MANAGEMENT AND ECONOMICS MASTER OF SCIENCE IN FINANCIAL ECONOMICS PROGRAMME

Ali Baghirov

MASTER THESIS DIRECT AND INDIRECT EFFECTS OF OIL PRICE SHOCKS ON ECONOMIC GROWTH: CASE OF LITHUANIA

Supervisor: Ruta Rodzko

VILNIUS, 2014

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania

ABSTRACT Baghirov, A. Direct and indirect effects of oil price shocks on economic growth: case of Lithuania [Manuscript]: Master Thesis: Financial Economics. Vilnius, ISM University of Management and Economics, 2014. The current paper aims to investigate the direct and indirect effects of an oil price shock on economic growth of Lithuania taking into consideration its trade linkages with main trade partners. The main objectives of the paper are to estimate possible positive indirect effects of oil price shocks through the trade relationships and to determine if these indirect effects can mitigate the expected negative direct effects of oil price shocks on economic growth of Lithuania. Accordingly, the analysis considers the following seven countries: Russia, Germany, Netherlands, France , Poland, Lithuania and Latvia. The time scope for the current analysis is set from the 2nd quarter of 1995 till the 4th quarter of 2012. The quarterly data on real GDP growth rates of selected countries and quarterly data on oil price growth rates is used. The current methodological approach refers to the structural VAR model developed by Tilak Abeysinghe (2001), however it is set as a simple VAR model with an exogenous variable. In other words, in this study the trade relationships between the listed countries are considered implicitly. Hence, in a further research longer time scope can be chosen and the model can be improved so that changing impulse responses might be estimated. Nevertheless, the selected model allows to determine both direct and indirect effects of oil price shock via estimated impulse responses, which attendantly is the main goal of current study. The empirical analysis indicates that the indirect effects of a 50% increase in oil price growth rates on real GDP growth of Lithuania are positive, while expectedly the direct effects are negative. Moreover, the positive indirect effects through the trade linkages mitigate the negative direct effects of oil price shock both in short and long run.

Key words: direct and indirect effects, oil price shock, VAR model, impulse responses, economic growth, trade linkages.

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TABLE OF CONTENTS ABSTRACT ...................................................................................................................... 2 LIST OF FIGURES ............................................................................................................ 5 LIST OF TABLES ............................................................................................................. 6 INTRODUCTION.............................................................................................................. 7 1.

LITERATURE REVIEW ...........................................................................................10 1.1

Oil price shocks: Conceptual and historical overview ...........................................10

1.2

Transmission channels of oil price shocks ............................................................13

1.3

Effects of oil price shocks on economic growth ....................................................16

1.3.1

Effects of oil price shocks on oil-exporting countries .....................................17

1.3.2

Effects of oil price shocks on oil-importing countries .....................................21

1.4

Trade linkages and macroeconomic activity .........................................................26

1.4.1 Trade linkages and effects of oil price shocks on oil-importing countries .............26 2.

METHODOLOGICAL APPROACH ..........................................................................29 2.1

2.1.1

Augmented Dickey-Fuller (ADF) test ............................................................30

2.1.2

KPSS test .....................................................................................................31

2.2

3.

Stationarity tests ..................................................................................................29

Vector autoregressive (VAR) models ...................................................................31

2.2.1

Advantages of VAR ......................................................................................33

2.2.2

Problems with VAR ......................................................................................34

2.3

Why not Applied General Equilibrium Models? ...................................................35

2.4

Research Design and Model Specification ............................................................36

2.5

Data Selection .....................................................................................................39

EMPIRICAL RESEARCH RESULTS ........................................................................40 3.1

Stationarity tests ..................................................................................................41

3.2

Autocorrelation tests for the real GDP growth rates ..............................................44

3.3

Estimated VAR model .........................................................................................45 3


4.

3.3.1

Variables ......................................................................................................45

3.3.2

Appropriate lag length selection ....................................................................46

3.3.3

Results of VAR model ..................................................................................47

3.4

Estimated direct and indirect effects via impulse responses ...................................52

3.5

Robustness of empirical results ............................................................................58

DISCUSSION ............................................................................................................61 4.1

Main Research Findings ......................................................................................61

4.2

The Linkage of Main Findings to the Literature Reviewed ....................................62

4.3

Limitations and Implications for Further Research ................................................64

CONCLUSIONS...............................................................................................................67 References ........................................................................................................................69 Appendix 1 .......................................................................................................................75 Appendix 2 .......................................................................................................................78 Appendix 3 .......................................................................................................................80 Appendix 4 .......................................................................................................................81 Appendix 5 .......................................................................................................................88

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LIST OF FIGURES Figure 1. The graph describing the transmission channels of oil-price shocks ......................14 Figure 2. Time series plots of residuals of estimated VAR model. .......................................51 Figure 3. Total, direct and indirect impact of a 50% increase in oil price growth rates on real GDP growth of Lithuania...................................................................................................53 Figure 4. Total, direct and indirect impact of a 50% increase in oil price growth rates on cumulative real GDP growth of Lithuania. .........................................................................55 Figure 5. Total, direct and indirect impact of a 50% increase in oil price growth rates on cumulative real GDP growth of Lithuania (Exogenous variable: real oil price growth rates deflated by Lithuanian GDP deflator). ................................................................................58 Figure 6. Total, direct and indirect impact of a 50% increase in oil price growth rates on cumulative real GDP growth of Lithuania (Exogenous variable: nominal oil price growth rates in USD). ...................................................................................................................59 Figure 7. Total, direct and indirect impact of a 50% increase in oil price growth rates on cumulative real GDP growth of Lithuania (Exogenous variable: nominal oil price growth rates in Euro).....................................................................................................................60

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LIST OF TABLES Table 1. The main five military conflicts in the Middle East that have affected the global oil supplies .............................................................................................................................12 Table 2. Summary statistics for real GDP growth rates of selected countries .......................40 Table 3. The results of ADF test without constant and with constant for real GDP growth rates of selected countries ..................................................................................................41 Table 4. The results of KPSS test without trend for real GDP growth rates of selected countries ...........................................................................................................................42 Table 5. The results of ADF test without constant and with constant for real and nominal oil price growth rates ..............................................................................................................43 Table 6. The results of KPSS test without trend for real and nominal oil price growth rates .43 Table 7. The results of lag length selection for the estimated VAR model (real oil price measure deflated by EU GDP deflator, as an exogenous variable) .......................................46 Table 8. The results of equation for Lithuanian real GDP growth from estimated VAR model (Equation 6: Real GDP growth rates of Lithuania) ..............................................................47 Table 9. The results of equation for Lithuanian real GDP growth from estimated VAR model (Equation 6: Real GDP growth rates of Lithuania) ..............................................................48 Table 10. F-tests of zero restrictions from the equation for Lithuanian real GDP growth ......49 Table 11. The p-values of the real oil price measure (EU GDP deflator) from each equation 50 Table 12. Direct, indirect and total effects of a 50% increase in oil price growth rates on real GDP growth rate of Lithuania ............................................................................................54 Table 13. Direct, indirect and total effects of a 50% increase in oil price growth rates on cumulative real GDP growth rate of Lithuania ....................................................................56 Table 14. Cumulative impact of a 50% increase in oil price growth rates on real GDP growth rate of Lithuania ................................................................................................................56

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INTRODUCTION In this thesis I would like to analyze the possible direct and indirect effects of an oil price shock on economic growth of Lithuania. Reviewing the previous literature it can be easily noticed that there are two separate views on the impacts of oil price changes on economic growth and macroeconomic variables. While, some of authors stated that the oil price changes are very important influential factors to reflect economic stability, others considered that the oil price shocks have already lost their position to play a crucial role in affecting an economy. Not so many researches on how the oil price shocks affect developing and emerging countries were done since now. This analysis will mainly concentrate on possible direct and indirect effects of oil price shocks on the economic growth of Lithuania being energy dependent and oil-importing country, whose one of the main trade partners, for instance, is Russia. According to the previous studies there can be some indirect effects of oil price shocks if the trade relationships of a country is also considered. Because, for instance, it is a quite expected fact that Russia being an oil-exporting and Lithuania an oil-importing country would gain and suffer respectively from oil price increases, however, such an important criteria as trade linkages should also be taken into consideration. Keeping in mind all aspects mentioned above, I would like to introduce the main goal of the thesis as following: to analyze and to estimate direct and indirect effects of oil price shocks on Lithuanian economic growth, taking into consideration also its trade relationships with main trading partners. Moreover, to check if Lithuania as an oil-importing and oil dependent country can weaken or even neutralize the possible negative direct effects from oil price shocks by the expectable positive indirect effects of oil price shocks through its trade linkages.

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania The main objectives of the study can be listed accordingly: 1) to define both: short and long term direct and indirect effects of oil price shocks on developing and oil-dependent country through the example of Lithuania; 2) to analyze mainly the indirect effects of the oil price changes on economic growth of Lithuania; 3) to establish an econometric analysis taking into consideration also other main trade partners of Lithuania; 4) to realize an analysis and take into account the GDP growth rates of other included countries; 5) to analyze the results and make concluding remarks on the effects of oil price shocks on economic growth of Lithuania. As it can be noticed this study mainly concentrates on indirect effects rather than direct effects of oil price shocks that is assumed to be positive for an oil-importing country, in this example for Lithuania. So the main problem is defined as following: are the indirect effects of oil price shocks that may come from the trade relationships with main trading partners of Lithuania can actually outperform the direct negative effects of oil price shocks on Lithuanian economic growth. So the hypotheses for current research are set as following: Hypothesis: H0: There are no essential direct and indirect effects of oil price shocks on economic growth of Lithuania. HA: There are reasonable direct and indirect effects of oil price shocks on economic growth of Lithuania. Additional Hypothesis: H0: Positive indirect effects do not have any influence on negative direct effects of an oil price shock on economic growth of Lithuania.

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania HA: Positive indirect effects mitigate the negative direct effects of an oil price shock on economic growth of Lithuania. A Vector Autoregression (VAR) model will be used to analyse how an oil price shock can make an impact on the GDP growth rates of Lithuania. Moreover, the chosen methodology also will allow to estimate both direct and indirect effects of the oil price shock on economic growth of Lithuania. The main data sources will be: Eurostat, OECD statistics database, FRED and National Statistical Offices of selected countries. According to the methodology that will be used, the countries included in the model should have strong trading links, so this aspect will be taken into account while arranging the country list. The data on quarterly oil price growth rates, and quarterly real GDP series of Lithuania and the other countries listed into the analysis will be used. The research will begin with the literature review of papers regarding oil price shocks, transmission channels of oil price shocks, effects on oil-exporting and oil importing countries and previous analysis done on the impact of oil price shocks on economic growth and especially related to direct and indirect effects of oil price shocks. Secondly, a simpler version of the VAR model used in previous analysis by T. Abeysinghe (2001) to estimate the direct and indirect effects of oil price shocks, will be implemented. Moreover, empirical results will be analysed and discussed. Finally, appropriate discussions and conclusions will be presented on the analysed problem.

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1. LITERATURE REVIEW This part of the thesis concentrates on reviewing the previous studies and papers related to oil price shocks, views of different researches on this topic and the main oil price shocks that happened over the history. Moreover, the works conducted on the direct and indirect effects of oil price shocks on economic growth of both: oil-exporting and oil-importing countries will be analyzed. Lastly, taking into consideration the papers about the trade linkages and the effects of oil price shocks will provide a broader justification for the main problem identified earlier. 1.1 Oil price shocks: Conceptual and historical overview As it is known from the history of oil production and extraction, the first successfully realized oil extraction process was done by Edwin Drake in Pennsylvania in 1859. Also as it was stated by James D. Hamilton (2012), since then oil has started to be one of the most dominant energy resources in the world. It can be easily noticed that oil price has never been as high as when it was extracted and produced first time, of course compared to the nowadays value of US dollar. In his another paper Hamilton (2011) shows that, however, as time passed and demand was never staying at the same levels, oil prices showed changes as well. For instance, after the period of 1862-1864 US Civil War oil prices and demand for oil fell essentially. By the beginning of 20th century the role of oil in an economy changed slightly. Compared to the 19th century oil became a more important and essential power factor for industrial and commercial heating sector, as well as for transportation sphere. According to Chuku (2012), “OPSs are unexpected and unpredictable changes in global oil prices, caused by exogenous factors, which are likely to impact on endogenously determined economic variables” (p.413).

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania According to the study by Hamilton (2012), the main global oil price shocks over the last half century were closely related to the conflicts which took place in the Middle East. As an example for the major events could be shown: the closure of the Suez Canal which was related to the conflict between Egypt, Israel, Great Britain and France in October 1956; the oil embargo by the Arabian countries of OPEC in October 1973; the Iranian revolution in November 1978; the Iran-Iraq War that began in September 1980; and the first Persian Gulf War in August 1990. Moreover, the impact of the second Persian Gulf War and the events in Venezuela in December 2002, the revolution in Libya in February 2011 played a crucial role and caused the disruptions in oil supply. However, there were also other unexpected increases in oil prices such as in 2004, which was the consequence of the high oil demand in emerging economies. Additionally, the East Asian Crisis in 1997, disarrangements related to post World War II in 1947 and the Korean conflicts in 1952-53 had some impact on oil price increases. In his paper regarding the 1973 oil crisis, Charles Issawi (1978) discussed more broadly some of these events and showed that the main reason behind the OPEC embargo was the support that Israel received from the US and European countries during the Yom Kippur War in 1973. As a result, Arabian countries were crushed in this War and as a response for the support provided to Israel, Arabian members of OPEC refused to sell oil to the US, UK and other European countries. As it was explored by Dr. Jahangir Amuzegar (2009), following the revolution that happened in Iran in 1979 the price of oil rose to 40 US dollars per barrel and created a serious disorder in oil market. Furthermore, the war started between Iran and Iraq in September 1980 drove the oil prices up and this price volatility lasted until October 1981 when OPEC officially set a benchmark price of 34 US dollars per barrel. However, this official benchmark price could not be held, as later OPEC was not as dominant in determining oil supply as it was earlier. 11

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania According to Hamilton (2011), another oil price shock happened in August 1990 after Iraq intruded Kuwait, also known as the first Persian Gulf War. Appropriately, oil price increased twice during few months. The next more sufficient oil price shocks were caused by the elimination of 2.1 mb/d (millions of barrels per day) of oil from Venezuela in December 2002 and January 2003, and other 2.3 mb/d of oil from April till July. The effects of these events were smaller and shorter, as after the first Persian Gulf War. Table 1 The main five military conflicts in the Middle East that have affected the global oil supplies.

Date

Event

World Supply Disruption

Recession Date

Months from Disruption to Cycle Peak

November 1956

Suez Crisis

10.1%

August 1957

8

November 1973

Yom Kippur War

7.8%

November 1973

0

November 1978

Iranian Revolution

8.9%

January 1980

13

October 1990

Iran-Iraq War

7.2%

July 1981

8

August 1990

Persian Gulf War

8.8%

July 1990

-1

Note. From “The macroeconomics of oil shocks,” by K.Sill, 2007, Business Review, p.24. The decrease in global oil supply during the period of 2005 and 2007, the intensive and phenomenal growth of demand for oil, increasing consumption and price bubble were the main causes for the oil price shock that occurred in 2007-2008 period (Hamilton, 2009). 12

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania 1.2 Transmission channels of oil price shocks As it was mentioned before, oil price changes are almost generally accepted as a significant factor that affects different economies, both: oil-exporting and oil-importing. According to the study by Weiqi Tang, Libo Wu and Zhong Xiang Zhang (2009) there are several channels through which oil prices affect an economy and macroeconomic variables. According to the theory oil prices affect the macroeconomic variables through these six transmission channels: 1) supply-side shock effect; 2) wealth transfer effect; 3) inflation effect; 4) real balance effect; 5) sector adjustment effect; 6) the unexpected (uncertainty) effect. These effects mentioned above were also broadly discussed by Chuku A. Chuku (2012) in this later analysis and listed as following: Supply-side shock effect – From this perspective oil is described as an input of production process. When oil price increases it automatically affects the output through the rising production costs. Consequently, a lower productivity decreases the total output and increases the unemployment. This transmission process scenario is typical for an oil-importing country. For an oil-exporting economy higher revenues as a result of the oil price shocks can contribute the increases in investment opportunities, which will boost the output and decrease the unemployment. Wealth transfer effect – This transmission channel explains how the income is transferred from an oil-importing economy to an oil-exporting economy after the oil price shock occurred. As a result, the consumer demand is decreased in oil-importing economy and increased in oil-exporting economy. Inflation effect – Oil price shocks also trigger the inflation in economy. As it was mentioned, oil price shocks increase the costs of production, which is considered as a price shock as a consequence. 13

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Real balance effect – Through this transmission channel, an oil price shock impacts money demand. As an example, consumers tend to borrow and not to save, it increases the interest rates and decreases the demand for cash. Sector adjustment effect – When an oil price shock happens, the cost of adjusting to these changes in each sector of an economy can be a reason for a slowdown. As a result, energyintensive sectors should be diminished and energy-efficient sectors should be expanded. The unexpected (uncertainty) effect – The investment demand of both consumers and producers is affected through the uncertainty channel of an oil price shock. The future investment plans can be postponed if people do not know if the oil prices will go up or down. Apparently, the uncertainty causes the investment demand to decrease. Output↓ (short-term)

Supply Shock Oil Price↑ Effect

Price

Shock

Inflation↑

PPI↑

Unemployment↑

(Capacity Utilization↓)

Income↓

Micro-Foundations for Price/Monetary Transmission Mechanism Incomplete

Output↓ (long-term)

Investment↑

(Capacity Increase↓)

Trans.

Profit↑

Complete Trans. CPI↑

Monetary Policy:

Md↓;I↓

Cost of living & producing↓

I↑

Real balance of currency↓

Investment↓

Md↑;I↑

Output↓ (longterm)

Controlling inflation (Capacity

Figure 1. The graph describing the transmission channels of oil-price shocks. From “Oil Price Shocks and Their Short- and Long-Term Effects on the Chinese Economy,” by

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania W.Tang, L.Wu and Z.X.Zhang, 2009, East-West Center working papers, Economics series, 102, p.7. Figure 1 describes how an oil price shock affects the various macroeconomic variables through the transmission channels mentioned earlier. Furthermore, according to the reviewed literature, the transmission channels of oil price shocks were described in a more concise way by Martin Schneider (2004) as following: 1) the supply side; 2) the demand side; 3) the terms of trade. As it was mentioned earlier, the supply side effect is explained from the perspective of rising production costs, because oil is considered as a production input. As a consequence, the production capacity is negatively affected. Moreover, effects on investment decisions depend on the expectations of people on the future oil price changes. On the other hand, from the demand side effect point of view the general level of prices affected, as they go up and which cause the income level and the demand to drop. It is also worth to mention that, above mentioned scenarios of effects are experienced by oil-importing economies, as oil-exporting economies would generally gain from higher oil prices. On the other hand, the supply side and the demand side effects were considered as direct effects of oil price shocks, whereas the increase in oil prices affects the economy also indirectly from another transmission channel, which is known as the terms of trade (Abeysinghe, 2001). There is another research by Jaime Marquez (1984) who developed a theoretical model to analyze through which channels the oil price shocks are transmitted. The economies which were included in the model were divided into three groups: 1) the OPEC countries, which use their oil revenues to purchase the manufactured goods from developed economies and do not import from non-OPEC developing economies; 2) the developed economies, these are the 15

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania countries that are affected through the demand side; 3) the non-OPEC developing economies, the countries that are affected through the supply side channel. The main finding from this research related to my study, which have to be mentioned is that the direct effects of oil price shocks can be mitigated by the indirect effects, which can be met only in a multi-country setting. These indirect effects of oil price shock that are coming through the trade linkages and trade partners will be discussed later in next sections. 1.3 Effects of oil price shocks on economic growth According to Ghalayini (2011), “From the middle of twentieth century onwards, crude oil has become one of the main indicators of economic activity worldwide, due to its outstanding importance in the supply of the world's energy demands” (p.127). There are two seperate views on how oil price shocks can affect macroeconomic variables. While some researchers stated that oil prices have an essential impact, others mentioned that oil and oil prices have lost their impact power on economies. In this part of review different thoughts and views will be provided and discussed on how oil prices affect economic activity and mainly economic growth of both oil-exporting and oil-importing countries. As it is mentioned above, one of the most popular views on this issue is that the oil price is very important factor, which plays an essential role in affecting economic activity and it impacts economies through various ways. For instance, Keith Sill (2007) suggested in his paper that, increases in oil prices directly makes an impact on the costs of producers and make them rise, as well as the costs of consumers. This is the way how oil price increases affect economy through supply and demand side. Moreover, oil price changes affect the allocation process of labor and capital between energy and non-energy intensive sectors of an economy. Thus, unexpected oil price increases, also known as oil price shocks may cause the economic slowdown. On the other hand, oil price decreases do not have that much impact on

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania economic growth and do not cause economic boost. This effect is also known as asymmetric effect of oil price on economic growth. Another quite interesting standpoint was presented in the research done by Kiseok Lee, Shawn Ni and Ronald A. Ratti (2005) on the role of price variability and the effects of oil price shocks on macroeconomy suggests that the impact of oil price shocks on economic growth is much greater in an environment where the prices had acted more stable before the oil price shocks occured. Moreover, while an increase happens in oil prices in an environment where the prices have been volatile earlier, a person will not be so interested in reallocation of the resources taking into account the possible costs that would have to be met. The concept that the effects of oil price shocks can be different in times of high or low uncertainty was analyzed in another study by Ine Van Robays (2012). As the uncertainty is taken into consideration by economic agents while making their decisions, it is considered to impact the economic activity. Three types of oil price shocks that can affect the economy were identified: 1) oil supply shocks; 2) oil demand shocks; 3) oil-specific demand shocks. The results showed that the oil demand and the oil supply curves are steeper and the impact of oil price shocks is stronger when the uncertainty is higher. Moreover, all types of oil price shock behave more aggressively when macroeconomic volatility is high. 1.3.1 Effects of oil price shocks on oil-exporting countries In this section of the study, several views and papers will be discussed and analyzed related to the effects of oil price shocks on economic growth of oil-exporting countries. In other words, a general view on the effects of oil price shocks on economic growth will be provided through the examples of different oil-exporting countries. According to the previous researches and works done on the effects of oil prices it can be stated that oil price variability has significant consequences related to the economic activity and economic growth. Apparently, these consequences and effects are considered to affect and influence the oil17

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania exporting and the oil-importing economies in different ways. While oil price increases are generally accepted to have positive effects on oil-exporting countries, they are expected to affect oil-importing countries in a negative way. The asymmetric effect of oil price shocks in oil-importing countries was broadly discussed and analyzed in previous studies. However, the concept of an asymmetric effect of oil price shock in oil-exporting countries can be quite different. Saeed Moshiri and Arezoo Banihashem (2012) in their paper regarding the asymmetric effects of oil price shocks on economic growth of oil-exporting countries suggested that in most oil-exporting countries the revenues from the oil industry are closely connected to the government, making it the most important influential power in economic activities. On the other hand, the government‘s size and its role in economy should be taken into consideration. So an oil price increase in an oilexporting country meaning the higher oil revenues, leads to implementation of new projects and investments. Nevertheless, while an unexpected oil price decrease occurs, those government-based projects and investments stay unfinished. Consequently, in this situation a government has no choice but to borrow to meet the budget deficit occured. One of the most popular theories that explains the effects of oil price increases on economic output growth in an oil-exporting country is the Dutch disease theory. This theory was discussed in the paper by W. Max Corden and J. Peter Neary (1982) and the theory states that higher oil prices, generally, change the industrial structure of the oil-exporting country making it more concentrated on oil industry and non traded sectors. Moreover, it is mentioned that the higher oil revenues lead to the appreciation of local currency, which consequently causes the increase of imports of consumer goods. So, because of the high concentration on imports the competitiveness of the local producers will decrease automatically. Hence, according to the Dutch disease theory an increase in oil prices is not a beneficial situation for the economy of an oil-exporting country. 18

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania The effects of oil price shocks on economic growth of an oil-exporting country can also be discussed from the other perspective as it was done by Jouko Rautava (2002), through the example of the largest oil-exporting country in the world – Russia. It is a generally accepted fact that the Russian economy is highly dependent and affected by the oil prices. The exports of Russia in relation to GDP were around 33% and the half of the export revenues was the share of energy. It was identified that a 10% percent increase in oil prices would lead to a 2.2% increase in the level of GDP. Another research done on the oil dependecy of Russia by Andreas Benedictow, Daniel Fjærtoft and Ole Løfsnæs (2010), states that the economic recovery of country in 2000 is directly connected to the high oil prices. During the period prior the financial crisis of 2008, Russia has had a 7% increase in GDP level since 2001, hence resulting as one of the strongest economies in the world. In the other research the importancy and the role of the oil prices in case of Russia was confirmed one more time by Katsuya Ito (2012). Moreover, it was stated that the economy of Russia is highly sensitive to the oil price changes. The results of analysis showed that in a long-term period 1% increase in oil prices would increase GDP by 0.44%. One of the researches on the effects of oil price shocks on economic growth of oil-exporting country was done on one of the main oil-exporting countries – Venezuela by Omar Mendoza and David Vera (2010). One of the main findings of the analysis is that, the oil price shocks that occured during the period (1984-2008) the analysis captured, have had a positive effect on the Venezuelan economy. Moreover, it is mentioned that oil price increases were more significant and affected economy more intensively than the oil price decreases. Lastly, the asymmetric effect of oil price shocks on economic growth of an oil-exporting country was determined. Another good example for explaining and analyzing the effects of oil price shocks on economic growth of an oil-exporting country is Nigeria. According to the recent analysis by 19

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Agbede Moses Oyeyemi (2013), since oil was discovered in Nigeria it has started to be the dominant factor in the economic life of the country. Oil revenues equaling the large amounts of GDP, exports and total revenues of the country make the economy very sensitive to the changes in oil prices. So the analysis confirmed the positive relationship between oil price increases and economic situation, on the other hand, showing that during the periods of oil price decreases disruption effects occured in balance of payments and government finances. Moreover, it was mentioned that even a small shock in global oil prices will have a long-term effect on the economic growth of the country. The results of another study by Gunu Umar and Kilishi A. Abdulhakeem (2010) also confirmed this fact and showed that oil price increases trigger the GDP ratio to rise and accordingly GDP will fall after the oil price decreases. One more not less significant research that was conducted by Amany A. El Anshasy (2009) on 15 oil-exporting countries and the effects of oil price shocks, showed that unanticipated oil price increases are not harmful in case of long-term growth. Moreover, it was mentioned that fiscal policy has a great contibution in transmitting process of oil price shock into economy and the countries that pay more attention to the public investment share can withstand the oil price shocks in better way than those who are less public investment concentrated. Four main policy implications were suggested for improving the growth as a result of high revenues from an oil price shock: 1) government should diversify its policies and expand the non-oil tax base; 2) government should pay more attention to the social spending rather than cutting the capital expenditure; 3) government should increase the expenditures to improve the infrastructure and public services; 4) lastly, an autonomous wealth fund can be established to transfer the oil revenues.

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania 1.3.2 Effects of oil price shocks on oil-importing countries The former section reviewed the previous researches and works done on the effects of oil price shocks on oil-exporting countries. Correspondingly, in this part of current research, the effects of oil price shocks will be analyzed from the perspective of oil-importing and oil dependent economies. The major research on the effects of oil price shocks on economic activity and macroeconomic variables was done through the example of the US by James D. Hamilton. In his several papers he showed that there is a strong relationship between oil price changes and economic growth of the US economy. In other words, an essential negative correlation between the oil price increases and the economic recessions in US was determined (Hamilton, 1983). Another not less significant aspect of oil price increases is that oil supply interruptions is a good prediction tool for GDP decrease, hence, it can cause the economic slowdowns. However, after the later research by Knut Anton Mork (1989), it was stated that there is no such a negative correlation in the case of oil price decreases and correlation is different or even zero. There is also another strong view by Hamilton (2000) which states that while analyzing and forecasting GDP growth, the nonlinear function of oil price changes have to be used. On the other hand, it should be mentioned that the nonlinear functions do not pay enough attention to endogenous factors that are actually have had a great influence in affecting oil prices over the history. Moreover, the increases in oil prices were considered to be more important factor in affecting economic growth and forecasting GDP than the decreases in oil prices. Another paper on the effects of oil price shocks on oil-importing countries was written by Lutz Kilian (2008a) and the main goal of the work was to review the literature on the effects of energy prices on the US economy. In addition, the author referred to the paper by James D. Hamilton (2005) which gave a broad view of the relation between oil and the macroeconomy of the US. Kilian (2008a) explains why the high gasoline and oil prices that occured in the

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania beginning of 2000s in the US did not cause an essential recession. He suggests that the main reason for the high prices was the huge global demand for industrial commodities and in the short-term period the effects of high prices are positive, however they are lower than average in the long-term period. The other research realized by Rebeca Jiménez-Rodríguez and Marcelo Sánchez (2004) discusses the effects of oil price shocks on the OECD countries being G-7 countries, Norway and the Euro area. The authors implemented a multivariate VAR analysis and used both linear and non-linear models. The main findings of the analysis showed that there is a nonlinear impact of oil price shocks on GDP growth of both oil-importing and oil-exporting countries. The results for the oil-importing countries included in the analysis indicated that there is a negative impact of oil price increases on the growth of all oil-importing countries except Japan. As a sum, the responses of GDP growth in oil-importing economies were almost similar in both linear and non-linear models. This pattern was explained by the special circumstances implemented by the Japanese economy during the period of study. The largest negative influence of oil price increases was felt in the fourth quarter after the shock, except France and Italy, for which it was felt in the third quarter. The effects of oil price shocks faded out after three years in all countries. Moreover, the other similar research by Knut Anton Mork, Øystein Olsen and Hans Terje Mysen (1994) investigated the macroeconomic responses to oil price increases and decreases in seven OECD countries (the United States, Germany(West), Japan, Canada, France, Norway and the United Kingdom). The results of the multivariate model indicated a stronger correlation between GDP growth and oil price shocks, rather than the results from the bivariate model that the authors have used. Except the case of Norway all countries included in the model had a negative correlation between their GDP growth and oil price increases. As in the previous research results for Japan were again

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania different from other countries that have experienced the negative effects from the oil price increases. The comparison of the effects of exogenous oil supply shocks on output in the G7 countries was presented in other paper by Lutz Killian (2008b), which showed that the oil-importing countries like the United States, Italy and France have experienced a significant decrease in GDP growth after the oil price shock. There were some problems in the data for Germany related to the re-unification in 1990, however, according to the baseline results, Germany also had a reduction in GDP growth after the oil price shock occurred. Surprisingly, in this analysis as well, there was not estimated a significant effect on Japanese GDP growth. Another interesting paper by Juan Carlos Ciscar, Peter Russ, Leonidas Parausos and Nikos Stroblos (2004) discusses the sensibility factor of EU economy to oil price shocks. As it’s known the EU economy is significantly sensitive to the oil price increases, despite the fact that oil imports is equal approximately to 1% of GDP in the EU account. The fact that EU imports 75% of its oil consumption from abroad explains why this area is so sensitive in case of oil price shocks. The research that used the GEM-E3 model analyzed the potential effects of an oil price shock on EU economy. In the first scenario that the research implemented the oil price was increased by 10 dollars per barrel and in the second one by 30 dollars per barrel. As a result, the GDP reductions for the EU as a whole were 0.94% and 2.56% in the first and in the second scenario, respectively. The research by Emanuel Anoruo and Uchenna Elike (2009) on the effects of oil price shock on the economic growth of oil-importing African countries; the research by Alireza Keikha, Ahmadali Keikha and Mohsen Mehrara (2012) on the effects of oil price shocks on selected oil-dependent countries; the research by Tiru K. Jayaraman and Evan Lau (2011) on the effects of oil price increases on economic growth of Pacific Island countries, represented

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania almost the similar results that the oil price shocks affected the economic growth of the selected countries negatively. Another interesting research by Evangelia Papapetrou (2009) examined the relationship between the oil price shocks and economic growth during the period of 1982:1 till 2008:8 in Greece, which is a highly oil dependent country. The oil consumption equaled to the 64% of total energy consumption in Greece in 2006. A regime-switching model and a threshold model were used to examine the patterns of this relationship and possible asymmetries between oil prices and economic activity. Conclusively, the results stated that more than 3% month to month increases in oil prices and oil price volatility more than 2.4% during a year negatively affect the economic activity in Greece. The research on another oil-importing country which is Czech Republic, was conducted by Kamil Dybczak, David Voňka and Nico van der Windt (2008). Despite the fact that, Czech Republic is a net-exporter of hard coal, it is also dependent on its oil and natural gas imports. However, in this study the authors by using different approaches represented the fact that the impact of the oil price shock is not so dramatic for Czech economy. This view was based on such factors as; Czech Republic has rich coal deposits which lowers the overall energy dependence and that the shock is weakened by technological improvement. Moreover, the results of the structural CGE model that was used, showed that a 20% increase in Czech Koruna oil price would decrease the GDP by 1.5% in short-term and 0.8% in the long-term period. Furthermore, another valuable study was conducted by Afia Malik (2008) on the possible consequences and challenges that an oil-importing country faces because of the high oil prices through the example of Pakistan, which is highly dependent on oil imports. As it was mentioned, the dependency of Pakistan on oil imports is expected to rise in the future, due to

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania the fact that gas resources of country are draining. As a result, author presented some suggestions on how the economy can cope with the possible difficulties that rise because of the oil price shocks. Firstly, government should diversify its energy supply structure and start to investigate the alternative local energy resources, such as coal. Moreover, energy conservation programs have to be implemented. Thirdly, the government should also improve its taxation policy on petroleum products and pay more attention to non-energy taxes. Finally, it was suggested that Pakistan must try to increase its exports in order to improve its balance of payments. As it was mentioned earlier, there were also some researches that have supported the view that oil price changes or oil price shocks have losted their ability to affect the economic activity and other macroeconomic variables. This statement was analyzed through the example of the downtrent in productivity growth of US economy during the period of 19651978 by Michael R. Darby (1984). The results of this analysis presented that the oil price increases did not play any essential role in affecting the productivity growth and the productivity problem was just a statistical myopia. The results of another analysis by James Tobin (1980) insisted on the fact that the recession during 1974-1975 in the USA and the weak recovery during the period of 1975-78 can not be related to the oil price shock of 1973. Moreover, as it was highlighted later by Olivier J. Blanchard and Jordi Gali (2007), the oil price shocks were not as influential in the 1970s in US as it is described by many researchers and there were some other shocks which were not taken into consideration. Additionally, it was stated that the oil price effects has changed over time. The following reasons were provided to prove why the recent oil price shocks have had fewer effects: 1) the other adverse shocks were very few; 2) oil had smaller share in production; 3) the labour markets were more malleable; 4) the monetary policy has been improved. On the other hand, it also was affirmed by Paul Segal (2007) that oil price shocks have never been such a significant factor 25

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania in affecting economic activity and the oil prices have not affected the economic growth during the recent years as they no longer can influence the inflation. 1.4 Trade linkages and macroeconomic activity The several researchers have supported the view that the countries with stronger trade relationships have similar business cycles. For instance, Robert Inklaar, Richard Jong-A-Pin and Jakob De Haan (2008) stated that the shocks that occur in one country can be transmitted to another country if they have intensive trade linkages. In another study by Vivek Arora and Athanasios Vamvakidis (2005) the effects of the trading partners’ growth on the domestic growth were evaluated. The results of the study suggested that, it is quite advantageous for the industrial countries to trade with the developing countries, due to the high growth capacity of developing countries. On the other hand, the developing economies gain from trading with the industrial countries because of the higher relative income levels in the industrial countries. Another study by Tilak Abeysinghe and Kristin Forbes (2005) that used a structural VAR model, also confirmed the fact that trade linkages is very significant aspect in case of transmitting the effects of shocks occured in one country to its main trading partners. 1.4.1 Trade linkages and effects of oil price shocks on oil-importing countries According to the previous literature that was overviewed, it can be stated that the trade linkages play a significant role in the transmission process of oil price shocks through the economies. These indirect effects of oil price shocks were examined and analyzed in several studies on the oil-importing and oil-exporting countries that have strong trade relationships. One of these studies was done on the Southeast and East Asian countries by Tilak Abeysinghe (2001). The author used a VARX methodology to analyze the direct and the indirect effects of oil price shocks in selected 12 economies. All the countries included in the analysis are net-importing countries, except Indonesia and Malaysia. The results showed that 26

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania these two oil-exporting economies experienced the positive direct effects of oil price shocks. However, Singapore which is a net oil importer showed significantly interesting results, as Indonesia and Malaysia are the main trading partners of Singapore it initially experienced a small positive indirect impact from oil price shocks. The positive indirect effects on Taiwan were a bit lower than Singapore. As a result, it can be stated that though these transmission effects can be not so significant for large economies, they play a crucial role for the small open economies affecting them positively and indirectly. Another similar research that used the same methodology developed by Tilak Abeysinghe was conducted by Iikka Korhonen and Svetlana Ledyaeva (2008), on the trade linkages and macroeconomic effects of oil price shocks on Russian economy. The main trading partners during the period of study (1995-2006) were included in the model, which are all oilimporting economies except Canada. The results of the study indicated that Russia as a net oil exporter gained from oil price shocks both in the short and the long-term period. As it was expected, Russia experienced negative indirect effects from oil price shocks, which were smaller than the positive direct effects. So the net effects for Russia were still positive. The oil-importing countries that experienced the highest negative direct effects from oil price shocks were the US, Japan and China. The results for Japan can be surprising, as it was highlighted earlier that Japan generally was not affected negatively from oil price shock, however these different results can be explained by the methods used in previous studies. As, in the previous studies the direct and indirect effects were not analyzed separately. The countries that experienced the positive indirect effects from Russia were Germany, Canada, France and Japan, while Switzerland and Finland were the economies that experienced the highest positive indirect effects from Russia. It was also found that, the countries that are more energy-efficient tend to endure less when an oil price shock happens. Lastly, the researches done by Berument, Ceylan and Dogan (2010); and by Rasmussen and Roitman 27

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania (2011) also supported the view that the oil-importing economies can actually weaken and mitigate the direct negative effects from the oil price shocks by the indirect positive effects that may come through the intensive trade linkages with an oil-exporting economy. In conclusion, based on the previous literature and studies analyzed in this section of my paper, I would like to substantiate the main problem of study one more time. As it can be noticed, an oil-importing country can experience a positive indirect impact from oil price shock. So I would like to verify if Lithuania as an oil-importing country can mitigate the negative direct effects from oil price increases through its trade linkages with its main trading partners included in the analysis.

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2. METHODOLOGICAL APPROACH In this section of the paper the selected methodological approach will be described and the main justifications will be represented on why the specific method was chosen. Moreover, the appropriateness of the methodological approach will be analyzed taking into consideration the main objectives and goals of the thesis. Another not less important fact is that, the econometric approach is the most suitable way in order to analyze the relationships between various macroeconomic variables. The main goal of current study is to estimate the direct and indirect effects of oil price shocks on economic growth of an oil-importing country through the example of Lithuania. In the later sections the reason of selecting the current methodological approach to evaluate these effects will be discussed more broadly. While looking through the previous studies it can be noticed that the statistical macroeconomic analysis has been widely used by various researchers. However, according to Christopher A. Sims (1980), though these models have performed successfully in most cases there are still some views stating that these models are not formulated and used in the best way. Moreover, taking into account that the statistical macroeconomic models contain large numbers of macroeconomic variables they have to make sure that the macroeconomic theories involved confront reality as well. In order to estimate both direct and indirect effects of oil price shocks on economic growth, a slightly different form of the methodological approach which was structured and developed by Tilak Abeysinghe (2001) will be used. The specific model will be discussed more broadly in later sections of the paper. 2.1 Stationarity tests As is it known while conducting a time series analysis the first step for a researcher is to make sure that the data he uses is stationary. For example, according to Chris Brooks (2008)

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania there are three main reasons for it. Firstly, the stationarity factor of a data can seriously affect its behaviour, for instance, in the case of shocks that can occur over time. So if the data is stationary the impact of the shocks on data will weaken as time passes, however if its not stationary the effects can vary over time. Secondly, inaccurate and doubtful results can be obtained if non-stationary data is used during a research. For example, if the data is nonstationary there can be two totally unrelated variables that can have quite strong adjusted R 2 in their regression analysis which actually would not make a significant sense in this specific case. In addition, last but not least important aspect is that the assumptions that are usually made in analysis will not be true and reliable. So in order to check the stationarity factor of real GDP growth rates and oil price growth rates, two most popular tests such as the Augmented Dickey-Fuller (ADF) test and Kwiatkowski, Phillips, Schmidt and Shin (KPSS) test will be implemented in this study. 2.1.1 Augmented Dickey-Fuller (ADF) test As it was maintained by Marno Verbeek (2004) in his book, while using an ADF test there are three main possibilities that should be taken into consideration, which are: testing with constant, without constant, and with constant and trend. The null hypothesis of an ADF test is that the series are not stationary so that they have a unit root. Hence, if the null hypothesis is rejected it means that the data is stationary and do not have a unit root. However, it is very important to decide whether to include or not to include a constant or trend, or both of them while running an ADF test. It can be estimated by analyzing the time series plots of data, so that it can be seen if it has a trend or not. Moreover, if the plot does not start from origin it means a constant should be included. According to Brooks (2008) another not less significant factor that should be considered while using an ADF test, is to choose the appropriate lag length. It can be done in two ways. First alternative is to consider the frequency of the data, for instance, while using a monthly 30

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania data to include 12 and in the case of quarterly data to include 4 lags. The second option is to take into consideration the information criterion and to include the number of lags that decreases the value of the criterion. Including too many lags can cause that a researcher will reject the null hypothesis of non-stationarity less frequently, while including too few lags can be a reason for an opposite process. 2.1.2 KPSS test The KPSS test is the alternative approach that was introduced in 1992 by Kwiatkowski, Phillips, Schmidt and Shin. As opposed to an ADF test KPSS test has a null hypothesis of stationarity. Hence, if according to results of an analysis the data is stationary, it means the null hypothesis should be accepted (Verbeek, 2004). 2.2 Vector autoregressive (VAR) models According to Brooks (2008), a VAR model that gained its popularity in econometrics with the help of Sims (1980), is a systems regression model which is somewhat a combination of the univariate time series models and the simultaneous equations models. Since then, VAR models have started to be a serious alternative for large-scale macroeconometric models. A VAR model is a dynamic system of equations and the current values of the variables included in the model depend on the past values of their own values and the past values of the other variables in the model. The simplest form of VAR models contains only two variables z1t and z2t; and is known as a bivariate VAR. The values of these two variables depend on the various combinations of the previous k values of these variables and error terms, while vit is a white noise disturbance term with E(vit) = 0, (i = 1, 2), E(v1t v2t) = 0: z1t = β10 + β11 z1t – 1 + … + β1k z1t – k + α11 z2t – 1 + … + α1k z2t – k + v1t z2t = β20 + β21 z2t – 1 + … + β2k z2t – k + α21 z1t – 1 + … + α2k z1t – k + v2t

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania As it can be noticed VAR model has such significant peculiarities as flexibility and ease of generalization, as it could also include moving average errors and formulate a multivariate version of ARMA models. On the other hand, the number of variables included in the model can be increased. As it was mentioned before after the introduction of VAR models by Sims (1980) these models started to be widely used by various economists and researchers; and compete with traditional large scale macroeconometric models. As it was argued by Hilde Christiane Bjørnland (2000), while applying macroeconometric models in order to maintain the identification, the exogenous variables were used to be excluded without any justification, as one of the main acceptations was that variables can be either endogenous or exogenous. However, Sims (1980) suggested that all variables included in an econometric model could be considered as endogenous; moreover, he stated that it is more appropriate to use smallscale models with less constraints. The first step while using a VAR model, is to estimate how many and which variables should be included in the model. Furthermore, the most applicable lag length of the VAR model should be selected. While deciding on which lag length to choose such criteria as Akaike information criterion (AIC) or Bayesian information criterion (BIC) can be taken into consideration that are widely used by researchers. Correspondingly, if a too large lag length is chosen the results of the model will most probably be inadequate, while a too short lag length will harm the significance of parameters. The OLS as a single equation method can be used in estimation process of the VAR models and can provide quite sufficient results, provided that the assumption of the normality of errors has been fulfilled. VAR models are generally known as a significant econometric tool for forecasting or for causal analysis, which analyzes the relationships between the economic variables. This peculiarity of VAR models that involves the causal analysis is known as structural modeling 32

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania which differs from the forecasting approach of these models. Being more concrete, the structural modeling involve more theoretical knowledge than forecasting approach, on how the exogenous and the endogenous variables are distincted (Stock & Watson, 2010). 2.2.1 Advantages of VAR In this subsection the main advantages and strong sides of the VAR models will be discussed. According to Bjørnland (2000) , “The VAR models have the advantage over traditional largescale macroeconometric models in that the results are not hidden by a large and complicated structure (the "black box"), but are easily interpreted and available” (p. 5). Moreover, as it was highlighted also, for instance; by Brooks (2008) that there is no need for the researcher to identify endogenous or exogenous variables, due to the fact that they are all endogenous. This fact plays actually a crucial role, due to the fact that in order to estimate valuable results through the simultaneous equations structural models, the equations that are represented by model should be all identified. For example, in the case of Hausman-type tests it is required that the researcher should structure the identidying restrictions, while VAR models do not require such restrictions to be formed. On the other hand, flexibility is one of the most significant features of VAR models, hence VAR models are able to present a richer structure and take a wider view on the features of the data. This fact is explained by the fact that the variables incldued in a VAR model are not dependent only on their own lags or the combinations of white noise term. In addition, as the variables on the right-hand side (RHS) are identified preliminarily, there is a possibility to implement Ordinary Least Squares (OLS) on each equation. In addition, one more advantage and strong side of VAR models is that, according to the previous studies VAR models are more preferable than traditional macroeconometric models from the standpoint of forecasting. In his book Marno Verbeek (2004) also supported this view and stated that the VAR models usually provide more precis

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania forecasting results because the information that is used during analysis captures also the past values of the other variables. 2.2.2 Problems with VAR Apparently, apart from the strong sides and advantages, VAR models also have some weak sides and limitations in comparison with the other econometric models; that will be described in this subsection of the paper. According to Brooks (2008); firstly, one of the most obvious and accepted problems with the VAR models is that these models are a-theoretical, which means that they do not use so much theoretical information and background to explain the relationships between the variables included in the model. On the other hand, there is a possibility that a researcher who would not have enough skills and experience could get insufficient results with the data he or she has obtained and used. Moreover, it is still unclear how the VAR coefficient estimates have to be explained in right way. Another most probable difficulty that can be faced while using a VAR model is related to selecting the most appropriate lag length for the model. In order to estimate the most suitable lag length for the model there are generally two main approaches, which are cross-equation restrictions and information criteria. While the first approach uses the block F-tests, the information criteria is based on implementing the likelihood ratio (LR) test, which is actually easy to use but has some limitations as well. On the other hand, it can be difficult to apply a VAR model if there are a lot of coefficients to estimate. For example, if c is the number of the equations in the model and d shows the lags of each variable in each equation, the number of the coefficients will be equal to (c+dc2). Another quite arguable statement about using the VAR models is about the stationarity of the components, so the question is that if all of them should be stationary or not. Most of the views on this statement say that it is not recommended to make a differentiation if the components are stationary or not, as in most cases this differentiation will harm the information about the relationship between the variables in long term period. 34

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania 2.3 Why not Applied General Equilibrium Models? The justification of rejecting to implement a general equilibrium model to analyze the main problem of the research will be provided in this part of the paper. Based on the analysis by Antonio M. Borges (1986), it can be stated that the applied general equilibrium models are widely used in macroeconomic research in order to present consistent results to very significant problems that occur. In order to efficiently implement these models a serious caution and coherent resources are required. However, apart from these aspects of these models there is also an inevitable fact that states that it might be not so appropriate and helpful to use the applied general equilibrium models for some issues. One of the main reasons behind this fact is a large and complicated structure of these models. Hence, it might be too difficult to implement an applied general equilibrium model for the current analysis; and also because of a complex structure these models require too much time. Moreover, applied general equilibrium models are not empirically validated, so it is not clear how well these models could explain the data and associated historical facts. Another important weakness would be the fact that because of the general equilibrium adjustment, first generation of these models were supposing that imports and exports were equal while considering a trade balance of an economy. Of course, later as these models started to be used for various types of issues, this simple approach has changed and stronger justifications related to the price elasticity of imports and exports have occurred. However, it is an unarguable fact that applied general equilibrium models still have weaknesses from the foreign sector point of view. On the other hand, general equilibrium models are mainly based on strong theoretical assumptions and it is not always relevant for an empirical work. Secondly, current study considers empirical results and it does not aim to discuss the theory of oil price shock transmission. 35

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania 2.4 Research Design and Model Specification As it was mentioned earlier, according to the main problem of this research the essential goal of this study is to estimate the indirect effects of oil price shocks which can arise through the trade linkages with trading partners and to analyze if these possible positive indirect effects for an oil-importing country can mitigate the negative direct effects of oil price shocks on economic growth of that country, in this case Lithuania. The methodological approach that will be implemented was chosen based on both theoretical background and previous studies that were conducted to analyze this issue. However, a simpler version of the VAR model that was developed and formulated by Abeysinghe (2001) and used in a recent study by Korhonen and Ledyaeva (2008) will be applied. The main reason for this is that, in current thesis trade weights are assumed to be constant for the purposes of simplicity. Korhonen and Ledyaeva (2008) implemented the following baseline model of simultaneous equations: (1) 4

4

𝑦1𝑡 = 𝜆1 + ∑ 𝑚1,𝑘 𝑦1,𝑡−𝑘 + ∑ v1𝑘 (𝑤1,2 ∗ 𝑦2,𝑡−𝑘 + ⋯ + 𝑤1,13 ∗ 𝑦13,𝑡−𝑘 ) 𝑘=1

𝑘=0 4

+ ∑ z1,𝑘 𝑜𝑠1,𝑡−𝑘 + 𝜀1𝑡 𝑘=0

… 4

4

𝑦13𝑡 = 𝜆13 + ∑ 𝑚13,𝑘 𝑦13,𝑡−𝑘 + ∑ v13𝑘 (𝑤13,1 ∗ 𝑦1,𝑡−𝑘 + ⋯ + 𝑤13,12 ∗ 𝑦12,𝑡−𝑘 ) 𝑘=1

𝑘=0 4

+ ∑ 𝑧13,𝑘 𝑜𝑠13,𝑡−𝑘 + 𝜀13𝑡 𝑘=0

yut – GDP growth rate in country u (1,…,13); osut – a measure of oil price shock to country u;

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania wuj – stands for the share of exports from country u to country j; m’s, v’s and z’s – the parameters that should be identified. In addition, as it was stated by Korhonen and Ledyaeva (2008), the main difference of this model (1) developed by Abeysinghe (2001) from a standart VAR or VARX model, is that the impulse responses estimated by model depending on trading patterns change as time passes. Hence, this feature of this model can be considered as a main advantage, because it allows to calculate impulse responses at any time using a trade matrix. On the other hand, it captures both direct and indirect effects of the oil prices on economic growth. The main difference of the model (2) that will be used in this study is that the trade linkages and bilateral export shares will be considered implicitly. It means that, in comparison to the model used by Korhonen and Ledyaeva (2008) the weighted averages of export shares multiplied by the GDP growth series of the countries in each equation will not be considered as endogenous variables, but just as a part of each equation being taken into consideration within the estimated coefficients. It means that the estimated coefficients of GDP growth series will already capture in themselves the weighted averages of export shares of selected countries. However, the model that will be applied will capture and estimate the direct, as well as the indirect effects of an oil price shock, which is the main goal of this study. A wider explanation will be provided further, while describing the baseline model and simultaneous equations system. As it could be understood already, the only endogenous variable of this analysis is real GDP growth rates of the selected countries. The gross domestic product data at market prices was chosen in order to assess not the nominal but real GDP growth of selected countries. Data for Russia was obtained from OECD statistics database, while real GDP series for the other countries were taken from Eurostat. 37

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Finally, the only exogenous variable that is included in the baseline model is the oil price shock measure. Quarterly data for the nominal oil prices were obtained from www.quandl.com, which is in US dollars. In order to choose the most appropriate oil price measure, four oil price measures will be considered which two of them are real and the other two nominal oil price measures: 1) real oil price growth rates deflated by quarterly average EU GDP deflator, 2) real oil price growth rates deflated by quarterly GDP deflator of Lithuania, 3) nominal oil price growth rates in US dollars and 4) nominal oil price growth rates in Euros. So taking in account the main difference of this model from the baseline model (1) used by Korhonen and Ledyaeva (2008) mentioned above, the following model captures the simultaneous equations system: (2) 𝑦1𝑡 = 𝜆1 + 𝑎 1,𝑘 𝑦1,𝑡−𝑘 + 𝑏 1,𝑘 𝑦2,𝑡−𝑘 + 𝑐 1𝑘, 𝑦3,𝑡−𝑘 + 𝑑 1,𝑘 𝑦4,𝑡−𝑘 + 𝑒1,𝑘 𝑦5,𝑡−𝑘 + 𝑓 1,𝑘 𝑦6,𝑡−𝑘 + 𝑔 1,𝑘 𝑦7,𝑡−𝑘 + 𝛾1,𝑘 𝑜𝑠𝑡 + 𝜀1𝑡

... 𝑦7𝑡 = 𝜆7 + 𝑎 7,𝑘 𝑦1,𝑡−𝑘 + 𝑏 7,𝑘 𝑦2,𝑡−𝑘 + 𝑐 7,𝑘 𝑦3,𝑡−𝑘 + 𝑑 7,𝑘 𝑦4,𝑡−𝑘 + 𝑒7,𝑘 𝑦5,𝑡−𝑘 + 𝑓 7,𝑘 𝑦6,𝑡−𝑘 + 𝑔 7,𝑘 𝑦7,𝑡−𝑘 + 𝛾7,𝑘 𝑜𝑠𝑡 + 𝜀7𝑡

yit – GDP growth rate in country i (1,…,7); os – a measure of oil price shock to a country; a’s, b’s, c’s, d’s, e’s, f’s, g’s and γ’s – the parameters that should be identified. These parameters capture the weighted averages of export shares of selected countries and let the model estimate them implicitly.

38

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania As it is known already from the previous literature, the simultaneous equations system can be estimated by using OLS methods. 2.5 Data Selection According to the methodology that will be used, the countries included in the model should have strong trading links. So firstly; Russia, Germany, Latvia and Poland were included in the model as main trading partners of Lithuania. Moreover, as the selected countries should have several other countries in the model which are there main trading partners, France and Netherlands were added to the list of selected countries. Netherlands is one of the main trading partners of Russia, while France is one of the main trading partners of Germany, Poland and Netherlands. As there are some countries included in the model that were in Soviet Union and there is no specific data that can be used before and for the beginning of the 90s, the period of the analysis is set as from the 2nd quarter of 1995 till the 4th quarter of 2012. The main data sources will be: Eurostat, OECD statistics database, FRED and National Statistical Offices of countries selected. Therefore, the data on quarterly real oil price changes and quarterly real GDP series of Lithuania and the other countries listed into the analysis will be used. The measuring technique of both real GDP growth rates and oil price growth rates is quarter on quarter. In addition, the series and the oil price growth rates will be tested for stationarity using Augmented Dickey-Fuller and KPSS tests. Moreover, the autocorrelation test for GDP growth series and oil price measures will be conducted. The program that will be used to conduct the analysis is GRETL. The final calculations for estimating direct, indirect and total effects of an oil price shock will be assessed in Excel.

39


3. EMPIRICAL RESEARCH RESULTS In this section of the paper the main results from empirical analysis that was conducted, will be presented and discussed. Firstly, the results from the stationarity tests and autocorrelation tests that were implemented on GDP growth series and oil price growth rates will be reviewed. Then the results from the estimated VAR model will be described step by step. However, it is worth to mention that these subsections of empirical analysis would not consider the indirect effects of oil price shocks, which is the main goal of the study to be estimated. Hence, in the last subsection of analysis the results for direct and indirect effects which were obtained using impulse responses will be decomposed. The time series plots of real GDP growth rates are described in Appendix 1 and it is seen that there is no trend for any of the time series plots for selected countries. Hence, this fact was taken into account while conducting stationarity tests, so both ADF and KPSS test were used without including trend. Additionally, the summary statistics for real GDP growth rates are shown in Table 2. Table 2 Summary statistics for real GDP growth rates of selected countries. Mean Real GDP growth rates of Russia Real GDP growth rates of Germany Real GDP growth rates of Netherlands Real GDP growth rates of France Real GDP growth rates of Poland Real GDP growth rates of Lithuania Real GDP growth rates of Latvia

Median Minimum Maximum

0.90483

1.3208

-5.4482

4.0962

Std. Dev. 1.7224

0.32958

0.3000

-4.1000

2.0000

0.8598

2.6088

-2.0273

8.7489

0.46620

0.6000

-2.1000

2.0000

0.72996 1.5658

-0.94528

1.5444

0.38169

0.5000

-1.7000

1.2000

0.52923 1.3866

-1.5698

4.2328

1.0563

1.2000

-3.1000

6.1000

1.0645

1.0077

0.46589

8.5182

1.1296

1.5000

-13.100

4.2000

2.1022

1.8611

-4.4384

27.734

1.0944

1.3000

-9.4000

5.4000

2.4662

2.2536

-1.8252

5.1549

40

C.V.

Skewness

1.9036

-1.3660

Ex. kurtosis 2.308

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania 3.1 Stationarity tests As it was mentioned earlier in methodological approach section the ADF and KPSS tests were applied to examine the stationarity of the specific data. The results of ADF and KPSS tests for real GDP growth rates are shown in Table 3 and 4, respectively. Table 3 The results of ADF test without constant and with constant for real GDP growth rates of selected countries. Variables/Tests

ADF without constant (p-value)

ADF with constant (p-value)

Real GDP growth rates of Russia Real GDP growth rates of Germany Real GDP growth rates of Netherlands Real GDP growth rates of France Real GDP growth rates of Poland Real GDP growth rates of Lithuania Real GDP growth rates of Latvia

0.02819

0.000839

4.79E-07

7.30E-06

0.0009287

0.00264

0.02398

0.04801

0.05908

6.89E-07

0.01577

0.001802

0.01611

0.05619

As it can be noticed from the Table 3 above, the real GDP growth rates are stationary according to the results of ADF test both without constant and with constant. The lag order for the ADF test was chosen as 4. The null hypothesis in ADF test is that data is not stationary, so based on the results of the conducted test we should reject the null hypothesis of non-stationarity.

41

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Table 4 The results of KPSS test without trend for real GDP growth rates of selected countries. Variables/Tests

KPSS without trend (10% : 0.350)




0.170092

0.170092

0.170092

0.0468944

0.0468944

0.0468944

0.627583

0.627583

0.627583

0.383323

0.383323

0.383323

0.233094

0.233094

0.233094

0.188429

0.188429

0.188429

0.211726

0.211726

0.211726

Additionally, the KPSS test was applied to test the stationarity of the real GDP growth rates of selected countries and the results of this test also proved that the data is stationarity. The lag truncation parameter was chosen as 4. As it is described in the Table 4 the test statistics obtained without including a trend are lower than the critical values. The null hypothesis in KPSS test is that the data is stationary. Accordingly, the null hypothesis of stationarity can not be rejected as the data is stationary. The results of ADF and KPSS tests for real and nominal oil price measures are presented in Table 5 and 6, respectively.

42

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Table 5 The results of ADF test without constant and with constant for real and nominal oil price growth rates. Variables/Tests

ADF without const. (p-value)

ADF with const. (p-value)

Real oil price growth rates (EU GDP deflator)

3.99e-005

7.933e-005

Real oil price growth rates (Lithuanian GDP deflator)

2.387e-005

0.0001

Nominal oil price growth rates (USD)

6.118e-005

3.245e-005

Nominal oil price growth rates (Euro)

6.405e-005

7.641e-005

As it can be seen from the Table 3 above the oil price growth rates are stationary according to the results of ADF test without constant and with constant. The lag order for the ADF test was chosen as 4. The null hypothesis in ADF test is that data is not stationary, so based on the results of the conducted test we should reject the null hypothesis of non-stationarity. Table 6 The results of KPSS test without trend for real and nominal oil price growth rates. Variables/Tests





0.0356349

0.0356349

0.0356349

Real oil price growth

0.0819068

0.0819068

0.0819068

43

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania rates (Lithuanian GDP deflator) Nominal oil price growth rates (USD)

0.0682525

0.0682525

0.0682525

Nominal oil price growth rates (Euro)

0.0335612

0.0335612

0.0335612

Additionally, the KPSS test was applied to test stationarity of the oil price growth rates and the results of this test also proved that the data is stationarity in all significance levels. The lag truncation parameter was chosen as 4. As it is described in the Table 4 the test statistics obtained both by including trend and without a trend are lower than the critical values in all significance levels. The null hypothesis in KPSS test is that the data is stationary. Accordingly the null hypothesis of stationarity can not be rejected as the data is stationary. 3.2 Autocorrelation tests for the real GDP growth rates As it was stated by Marno Verbeek, an autocorrelation function (ACF) is a very significant tool which helps to understand how the observations relate with each other and how a time series data changes as time passes. By analyzing the results of ACF or correlograms a researcher can understand how long and how strong the previous values of series are connected to its current values. In other words, ACF can show how long and how strong a shock in other variable can affect and be spread through the values of the considered variable (Verbeek, 2004). Appropriately, the Appendix 2 captures the correlograms that were obtained for the real GDP growth rates. According to the results for Lithuanian real GDP growth rates, it can be seen that the first three autocorrelation coefficients are significant and die out slowly, but after the 3rd lag the significance dissapears. The results for GDP growth rates of France showed the same pattern and had first three autocorrelation coefficients significant. For Latvia and 44

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Netherlands the autocorrelation coefficients were significant for the first four lags and for Russia for the first two lags. Real GDP growth rates of Poland did not have any significant autocorrelation coefficients, while autocorrelation coefficients of Germany showed significance in 1st and 8th lags. Accordingly, the partial autocorrelations (PACF) are also provided in Appendix 2. 3.3 Estimated VAR model As it was mentioned earlier in methodological approach section, a slightly different form of the VAR model that was depeloped and used by Abeysinghe (2001), which does not capture the changing trade matrices was used in current analysis. Appropriately, the weighted average of export shares multiplied by the GDP growth rates of selected countries were not considered as endogenous variables and were estimated implicitly. The VAR model with an exogenous variable will be described with the help of the steps provided below. 3.3.1 Variables According to the theoretical background provided earlier it is known that the frist step while estimating a VAR model is to choose how many and which variables should be appropriately included in the model. Real GDP growth rates of selected countries are the endogenous variables in the model which were chosen according to the methodology used by previous analysis (Korhonen & Ledyaeva, 2008) as a measure of economic growth. The data is seasonally adjusted quarterly data of gross domestic product in market prices which shows the percentage change on previous period (quarter). The only exogenous variable in the model is the oil price measure. In this case four different oil price measures were considered and one of them (real oil price measure deflated by EU GDP deflator) was chosen for the final VAR model. Two of these oil price measures use real oil price growth rates, while the other two are nominal oil price growth rates in US dollars

45

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania (USD) and Euros. The first real oil price measure was calculated by using quarterly exchange rates between USD and Euro and converting them into Euro. Later the nominal oil prices in Euros were deflated by quarterly average European Union GDP deflator numbers in order to estimate the real oil prices. The second oil price measure was estimated by deflating the nominal oil prices by quarterly Lithuanian GDP deflator numbers. As it was highlighted in previous paragraph the real oil price measure deflated by average EU GDP deflator was chosen as the most appropriate measure for the exogenous variable for the final VAR model. One of the reasons behind this choice was that the results from the other VAR models which used the other oil price measures were not as accurate as from this one. Due to the fact that, though the main goal of this study is to assess the direct and indirect effects for Lithuania, while evaluating the results from the estimated models the appropriatness and accurateness of the results for all the countries included in the model are considered as main criteria. Hence, for instance real oil price measure deflated by Lithuanian GDP deflator was not used as core oil price measure though the results for Lithuania were better while for the other countries it did not meet the expectations. 3.3.2 Appropriate lag length selection The second step while estimating a VAR model is chosing, the most appropriate and accurate lag length. Table 7 represents the results of lag length selection for the VAR model. Table 7 The results of lag length selection for the estimated VAR model (real oil price measure deflated by EU GDP deflator, as an exogenous variable). lags 1 2 3

loglik -558.71086 -500.48829 -458.24117

p(LR) 0 0.00122

AIC 18.558533 18.283233* 18.484811 46

BIC 20.631602* 21.968689 23.782655

HQC 19.378852* 19.741577 20.581181

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania 4

-420.02098

0.00731

18.806596

25.716827

21.540992

As it is evident from Table 7, two criteria that are taken into consideration in this analysis proposed different lag length for the model. Thus, in order to ensure which lag length can provide more efficient and trustful results both suggestions were considered. As it was expected lag order 1 suggested by BIC criterion provided more reasonable results. Moreover, according to the theory, as the sample size of analysis is not large the lower lag length should be preferred in order to obtain more precise results and avoid the loss of information. Due to the fact that, provided the sample size is not large and higher lag length is chosen then the significance levels of the estimated coefficients tend to decrease. In addition, it would be worth to mention the fact that in all other cases while the rest three oil price measures were used, criteria suggested the same lag length as in the table above (AIC – lag order 2 and BIC – lag order 1). The results of above mentioned lag length selections are presented in Appendix 3. 3.3.3 Results of VAR model In order to evaluate the results of the VAR model the significances of the parameters and the logical economic interpretation should be analyzed. Table 8 The results of equation for Lithuanian real GDP growth from estimated VAR model (Equation 6: Real GDP growth rates of Lithuania).

const Real GDP growth rates of Russia (lag 1) Real GDP growth rates

coefficient 0.0965106 0.232956

std. error 0.332243 0.141043

t-ratio 0.2905 1.652

p-value 0.7724 0.1037

0.0638218

0.38421

0.1661

0.8686

47

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania of Germany (lag 1) Real GDP growth rates of Netherlands (lag 1)

0.0627595

0.441305

0.1422

0.8874

Real GDP growth rates of France (lag 1)

1.26321

0.62351

2.026

0.0471 **

Real GDP growth rates of Poland (lag 1)

0.303627

0.214723

1.414

0.1624

Real GDP growth rates of Lithuania (lag 1)

-0.204925

0.145805

-1.405

0.165

Real GDP growth rates of Latvia (lag 1)

0.249497

0.113146

2.205

0.0312 **


-2.92829

1.45387

-2.014

0.0484 **

According to the results described in Table 8, in this equation for Lithuanian real GDP growth rate, the p-value for oil price measure which equals to 0.0484 shows that this variable is significant. The negative sign in front of the parameter (-2.92829) states that there is a negative correlation between the oil price growth rates and real GDP growth rate of Lithuania, which is quite logical because Lithuania as it‘s known is an oil-importing country. Other coefficients that showed actually stronger significances related to Lithuanian GDP growth rate were the 1st lags of GDP growth rates for France and Latvia. According to the pvalues of the coefficients for the 1st lags of Russian GDP growth rate it was also close to be significant. Moreover, as it can be noticed from Tabe 9 the R-squared for Lithuanian equation, which is a coefficient of determination equals to 0.380272. Table 9 The results of equation for Lithuanian real GDP growth from estimated VAR model (Equation 6: Real GDP growth rates of Lithuania). 48

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Mean dependent var Sum squared resid R-squared F(8, 61) rho

1.117143 191.2353 0.380272 4.678784 -0.00448

S.D. dependent var S.E. of regression Adjusted R-squared P-value(F) Durbin-Watson

2.11475 1.770595 0.298996 0.000169 1.998912

Table 10 F-tests of zero restrictions from the equation for Lithuanian real GDP growth. All lags of real GDP growth rates of Russia All lags of real GDP growth rates of Germany All lags of real GDP growth rates of Netherlands All lags of real GDP growth rates of France All lags of real GDP growth rates of Poland All lags of real GDP growth rates of Lithuania All lags of real GDP growth rates of Latvia

F(1, 61) = 2.7280 [0.1037] F(1, 61) = 0.027593 [0.8686] F(1, 61) = 0.020225 [0.8874] F(1, 61) = 4.1045 [0.0471] F(1, 61) = 1.9995 [0.1624] F(1, 61) = 1.9754 [0.1650] F(1, 61) = 4.8624 [0.0312]

Table 10 presents the joint significances of the variables and as there is only one lag included in the model the p-values are the same as in the Table 8. The results for the other countries are presented in Appendix 4. According to them, the oil price measure as an exogenous variable showed the significance (p-value = 0.0228 ) only in the case of Russian equation, being almost significant for France (p-value = 0.1248) and insignificant in all other cases. However, it have to mentioned that a VAR model is a statistical analysis tool and it can not fully capture all the economic logics behind the information that is set in the model. Moreover, there can be some other information in the data that is used. Taking into consideration these aspects, it can be stated that the insignificances of the oil price measure does not necessarily mean that the results of the model are not trustful. 3.3.3.1 Granger causality test According to Brooks (2008) the tests that are mentioned in previous subsection can be described also as causality tests, which were firstly introduced by Granger (1969). In other 49

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania words, Granger causality test tries to answer the question if the changes in one variable can cause changes in other variable. For instance, if α1 causes α2, the lags of α1 should be significant in the equation for α2. However, the word „causality“ here does not mean that one variable causes the other one, but it is just a correlation between then. Accordingly, in order to analyze the Granger causality between the oil price measure and real GDP growth rates of each country p-values which stand for real oil price growth rates (EU GDP deflator) in each equation will be considered. The p-values from each equation are presented in Table 11. Table 11 The p-values of the real oil price measure (EU GDP deflator) from each equation.


p-value (real oil price measure – EU GDP deflator) 0.0228 ** 0.4845 0.8673 0.1248 0.6839 0.0484 ** 0.2689

According to the Table above it can be stated that the oil price measure Granger causes the real GDP growth rates of Russia and Lithuania. However, it does not Granger cause the real GDP growth rates of other countries included in the model.

50

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania 3.3.3.2 Autocorrelation tests of residuals According to Brooks (2008) a model should have some criteria in order to be accepted, so that a researcher can be confident that the results of the model are precise. One of this criteria is that the residuals of the model should be white noise. As it is known a white noise process has constant mean and variance, while also the autocorrelation for this process is accepted to be equal to zero, except at lag zero. On the other hand, if the time series plots of residuals are fluctuating closely to zero, going up and down it means the residuals are white noise.

Figure 2. Time series plots of residuals of estimated VAR model. (uhat1 – 7 stand for equations of Russia, Germany, Netherlands, France, Poland, Lithuania and Latvia; respectively).

51

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania The Figure 2 above describes the time series plots of the residuals. In order to analyze the autocorrelations for the residuals, the correlograms are presented in Appendix 5. As it can be seen from the correlograms, the residuals almost for all equations do not cross the blue line and stay close to zero, except for the 2nd, 4th and 7th equations; being the equations for real GDP growth rates of Germany, France and Latvia, respectively. 3.4 Estimated direct and indirect effects via impulse responses One of the key points of the methodological approach, which provides the estimation of direct and indirect effects of an oil price shock on GDP growth of Lithuania are the impulse responses. According to Brooks (2008), the causality tests provide an opportunity to estimate which variables in the VAR model significantly affects the other variables included in the model. However, these results do not provide a researcher with information on how long and in which direction these influences are happening. Hence, impulse responses play a crucial role in estimating the responses of variables to the shocks that occur in other variables included in the model. So, exactly this feature of impulse responses was used in order to estimate both direct and indirect effects of oil price shocks on economic growth of the countries included in the model. After the model and its parameters were estimated the impulse responses were calculated adding a 50% increase to the oil price measure. In order to calculate the impulse responses a new variable is added which contains the same oil price growth rates but its sample size is wider than the original oil price measure (real oil price measure deflated by EU GDP deflator), because it contains 20 more observations (quarters). It is named a „Baseline Oil Price Measure“ (OPbaseline_EU). Further another new variable is defined and named „Oil Price Shock Measure“ (OPshock_EU). This variable is equaled to the baseline oil price measure, but only one quarter differs as a 50% oil price increase is added. Firstly, the VAR model is estimated with the baseline oil price measure as an exogenous variable. Then the 52

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania forecasting is done and the impulse responses are estimated. Secondly, the other VAR model is estimated with oil price shock measure and again the impulse responses are estimated by doing the forecasting. The difference between the estimated impulse responses from the VAR model with oil price shock measure and from the VAR model with baseline oil price measure gives us the total impulse responses to a 50% increase in oil price growth rates. The direct effects are calculated by using the estimated parameters for Lithuanian GDP growth equation. Afterwards the difference between the total effects and direct effects is calculated which is equal to the indirect effects. Figure 3 provides a graphical illustration of direct, indirect and total effects of a 50% increase in oil price growth rates on real GDP growth of Lithuania. In addition, Tabe 12 shows the impulse responses to a 50% increase in oil price growth rates.

Lithuania: Impulse responses to a 50% increase in oil price growth rates 1,5000

Impulse responses

1,0000 0,5000 0,0000 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20

Total Direct

-0,5000

Indirect -1,0000 -1,5000 -2,0000

Lags

Figure 3. Total, direct and indirect impact of a 50% increase in oil price growth rates on real GDP growth of Lithuania. 53

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Table 12 Direct, indirect and total effects of a 50% increase in oil price growth rates on real GDP growth rate of Lithuania. Lags 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Total effects -1.4000 1.2000 0.6000 0.4000 0.3000 0.2000 0.1000 0.0000 0.1000 0.1000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Direct effects -1.4641 0.3000 -0.0615 0.0126 -0.0026 0.0005 -0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Indirect effects 0.0641 0.9000 0.6615 0.3874 0.3026 0.1995 0.1001 0.0000 0.1000 0.1000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

According to Figure 3 and Table 12, in the 1st quarter after the shock occurs Lithuania experiences negative direct, however positive indirect effects from the 50% increase in oil price growth rates, the total effect of an oil price shock for Lithuania after the 1st quarter is negative too. The indirect effects for Lithuanian GDP growth remain positive till the 11th quarter, when it starts to die out. So for the last 10 quarters the indirect effects are zero. On the other hand the direct effects show fluctuation becoming positive in the 2nd quarter and again go to negative in the 3rd one. This fluctuation continues until the 8th quarter when the direct effects fade out and equal to zero for the rest of the period.

54

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania On the contrary; direct, indirect and total impacts of a 50% increase in oil price growth rates on cumulative real GDP growth rate of Lithuania are described in Figure 4 and Table 13.

Lithuania: Cumulative impulse responses to a 50% increase in oil price growth rates 3,500

Cumulative impulse responses

3,000 2,500 2,000 1,500

1,000

Total_CUM

0,500

Direct_CUM

0,000

Indirect_CUM

-0,500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

-1,000 -1,500 -2,000

Lags

Figure 4. Total, direct and indirect impact of a 50% increase in oil price growth rates on cumulative real GDP growth of Lithuania. Based on the results presented in Figure 4 and Table 13, cumulative indirect effects of oil price shock on real GDP growth of Lithuania are positive through all twenty quarters. Indirect effects increase effectively in first five quarters, however start to stabilize after sixth quarter and stays constant through the rest of period. On the contrary, expectedly cumulative direct effects have a negative pattern and do not show sharp changes. Total cumulative effects of oil price shock is neagtive in the 1st and the 2nd quarters because of the high negative direct effects and relatively lower positive indirect effects in first 2 quarters. Nevertheless, cumulative total effects are positive starting from the 3rd quarter until the end of the period. 55

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Table 13 Direct, indirect and total effects of a 50% increase in oil price growth rates on cumulative real GDP growth rate of Lithuania. Lags 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Total cumulative effects -1.4000 -0.2000 0.4000 0.8000 1.1000 1.3000 1.4000 1.4000 1.5000 1.6000 1.6000 1.6000 1.6000 1.6000 1.6000 1.6000 1.6000 1.6000 1.6000 1.6000

Direct cumulative effects -1.4641 -1.1641 -1.2256 -1.2130 -1.2156 -1.2150 -1.2152 -1.2151 -1.2151 -1.2151 -1.2151 -1.2151 -1.2151 -1.2151 -1.2151 -1.2151 -1.2151 -1.2151 -1.2151 -1.2151

Indirect cumulative effects 0.0641 0.9641 1.6256 2.0130 2.3156 2.5150 2.6152 2.6151 2.7151 2.8151 2.8151 2.8151 2.8151 2.8151 2.8151 2.8151 2.8151 2.8151 2.8151 2.8151

Moreover, Table 14 presents the short run and long run cumulative effects of an oil price shock. Short run effects are the cumulative sums of four quarters of impulse responses, while the long run effects are the twenty quarter sums of impulse responses. Table 14 Cumulative impact of a 50% increase in oil price growth rates on real GDP growth rate of Lithuania. Country

Effect

Direct 56

Impact through trading

Total

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania impact

partners (indirect)

impact

Short run (4 quarters)

-1.2130

2.0130

0.8000

Long run (20 quarters)

-1.2151

2.8151

1.6000

Lithuania

As it was expected, Lithuania as an oil-importing country experinences negative direct effects from an oil price shock both in short term and long term period. Additionally, the negative direct effects for Lithuania were -1.2130 and -1.2151 percentages in short run and long run, respectively. However, as it is seen from the Table, the indirect effects from a 50% increase in oil price growth rates are positive. Furthermore, it can be stated that a 50% positive oil price shock indirectly causes 2.0130 and 2.8151 percentage increases in real GDP growth of Lithuania after four quarters and twenty quarters, respectively. Another interesting fact is that the negative direct effects are mitigated by the positive indirect effects which are affecting the economy through the trade linkages. As a result, both short run and long run total effects of a 50% oil price shock on cumulative real GDP growth of Lithuania are positive. The cumulative total impact is 0.8 and 1.6 percentage point increases after four quarters and twenty quarters, respectively. As the main goal of this research was to estimate the direct and indirect effects of an oil price shock on economic growth of specifically Lithuania, moreover, to analyze if possible positive indirect effects through trade relationships can mitigate the expected negative direct effects, the result for the other countries listed into the model are not presented. However, it has to be mentioned that the results estimated for Lithuania not depending on which oil price measure was used, showed the same pattern and performed consistently. It means that with different oil price measure the GDP growth of Lithuania had a positive indirect and a negative direct impact from a 50% increase in oil price growth rates. In all cases these indirect effects

57

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania through trade linkages mitigated relatively lower direct effects, so both in short run and long run the total effects of an oil price increase on economic growth of Lithuania were positive. 3.5 Robustness of empirical results As it was mentioned in previous section the results for Lithuanian real GDP growth remained consistent not depending on what oil price measure was considered as an exogenous variable in VAR model. So in order to show the robustness of results for Lithuania, the current section will provide the graphical illustrations of cumulative direct, indirect and total effects of a 50% increase in oil price growth rates on real GDP growth of Lithuania from the models which have had other oil price measures (real oil price growth rates deflated by Lithuanian GDP deflator, nominal oil price growth rates in USD and nominal oil price growth rates in Euro) as an exogenous variable.

Lithuania: Cumulative impulse responses to a 50% increase in oil price growth rates Cumulative impulse responses

3,500 3,000 2,500 2,000 1,500

Total_CUM

1,000

Direct_CUM

0,500

Indirect_CUM

0,000 -0,500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

-1,000 -1,500

Lags

Figure 5. Total, direct and indirect impact of a 50% increase in oil price growth rates on cumulative real GDP growth of Lithuania (Exogenous variable: real oil price growth rates deflated by Lithuanian GDP deflator). 58

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Figure 5 describes the estimated cumulative direct, indirect and total effects of an oil price shock on real GDP growth of Lithuania using the real oil price measure deflated by Lithuanian GDP deflator. As it is evident from the table, total effects of the oil price shock for Lithuania are positive in both short and long run in this case as well.


3,000 2,500 2,000 1,500 1,000

Total_CUM

0,500

Direct_CUM

0,000

Indirect_CUM

-0,500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

-1,000 -1,500

Lags

Figure 6. Total, direct and indirect impact of a 50% increase in oil price growth rates on cumulative real GDP growth of Lithuania (Exogenous variable: nominal oil price growth rates in USD). Figure 6 presents the estimated cumulative impulse responses to a 50% increase in oil price growth rates in case of Lithuanian real GDP growth. In this scenario the exogenous variable included into the model is the nominal oil measure in USD. However, as it can be noticed from Figure 6, the pattern of these results do not differ from the previous two described in Figure 4 and Figure 5.

59



3,000 2,500 2,000 1,500 1,000 Total_CUM

0,500

Direct_CUM

0,000 -0,500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Indirect_CUM

-1,000 -1,500

-2,000

Lags

Figure 7. Total, direct and indirect impact of a 50% increase in oil price growth rates on cumulative real GDP growth of Lithuania (Exogenous variable: nominal oil price growth rates in Euro). The results from the last model which used nominal oil price growth rates in Euros as an exogenous variable are described in Figure 7. It is evident that the results for Lithuanian real GDP growth show similar pattern in this case too. In other words, both in short and long run cumulative positive indirect effects mitigate cumulative negative direct effects, so that the total effects become positive as in all previous cases. Based on the results provided in this section, the results obtained for Lithuania are consistent with all four oil price measures that were considered. As the results from all four VAR models with different oil price measures as exogenous variables, confirmed the fact that positive indirect effects which come through trade linkages outperform the negative direct effects of the oil price shock on real GDP growth of Lithuania. Hence, the robustness of empirical results provided earlier can be affirmed. 60


4. DISCUSSION In this section of the thesis significant research findings is presented and discussed. Further the linkages between the significant findings of analysis and the literature reviewed earlier are described. And lastly, the limitations of the analysis are estimated and discussed, while also implications for further research are suggested. 4.1 Main Research Findings The results of empirical analysis suggested that an oil price shock affected the real GDP growth rate of Lithuania both directly and indirectly. Appropriately, cumulative impulse responses to a 50% increase in oil price growth rates were calculated for both short run and long run. Both in short run and long run period negative direct effects were mitigated by positive indirect effects that influences the GDP growth of Lithuania through its trade linkages. According to the results described in Table 12, its an evident fact that after 4 quarters when an oil price shock occurs the total cumulative real GDP growth of Lithuania equals to 0.8%. However, the total cumulative effects are higher in a long term period (after 20 quarters) and equals to 1.6%. Hence, based on the results of the empirical analysis the consideration of the hypotheses provided in Introduction part should be as following: Hypothesis: H0: There are no essential direct and indirect effects of oil price shocks on economic growth of Lithuania. HA: There are reasonable direct and indirect effects of oil price shocks on economic growth of Lithuania.

61

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania In this case, the null hypothesis which states that there is no essential direct and indirect effects of oil price shocks on economic growth of Lithuania, can not be accepted and it is rejected. Due to the fact that, an oil price increase showed quite reasonable effects both in short term and long term periods on real GDP growth of Lithuania. Additional Hypothesis: H0: Positive indirect effects do not have any influence on negative direct effects of an oil price shock on economic growth of Lithuania. HA: Positive indirect effects mitigate the negative direct effects of an oil price shock on economic growth of Lithuania. On the other hand, there is another additional hypothesis that have to be considered as well based on the estimated direct and indirect effects via impulse responses. As in the first case, the null hypothesis which states that positive indirect effects do not have any influence on positive direct effects, is rejected. For the reason that estimated indirect effects were positive and they mitigated relatively lower negative direct effects. 4.2 The Linkage of Main Findings to the Literature Reviewed The synthesis between the reviewed literature and the empirical results of current analysis can be considered in two ways. As some studies were implemented on the effects of oil price shocks on economic growth of oil-importing countries not considering the trade linkages, while others took this aspect into account. Hence, such several studies which were conducted by Rebeca Jiménez-Rodríguez and Marcelo Sánchez (2004), Knut Anton Mork, Øystein Olsen and Hans Terje Mysen (1994), Lutz Killian (2008b), Emanuel Anoruo and Uchenna Elike (2009), Alireza Keikha, Ahmadali Keikha and Mohsen Mehrara (2012), Tiru K. Jayaraman and Evan Lau (2011), Afia Malik (2008) and Evangelia Papapetrou (2009) mainly

62

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania suggested that an oil-importing country generally suffers from an oil price shock. In other words, a sudden increase in oil prices causes an economic slowdown in the economy of an oil-importing and negatively affects its GDP growth. However, through the example of Lithuania which was considered in this thesis this view was not supported, but rather it was estimated that Lithuanian real GDP growth is experiencing a total positive impact as a result of a 50% increase in oil price growth rates in short and long term period. This result is based on the trade linkages with main trading partners, which are considered in current study. On the other hand, the results of current empirical analysis for Lithuania as an oil-importing country, can be compared with the results of such studies which were realized by: Tilak Abeysinghe (2001), Iikka Korhonen and Svetlana Ledyaeva (2008), Berument, Ceylan and Dogan (2010); and Rasmussen and Roitman (2011). Both in the researches by Tilak Abeysinghe (2001) and, Korhonen and Ledyaeva (2008) it was stated that some oil-importing countries included in the analysis tend to experience positive indirect effects from their main trading partners, especially from the one which is an oil-exporting country. For instance, according to the results of the study by Abeysinghe (2001), Singapore initially experiences slightly positive indirect effects of oil price increases. These positive indirect effects originate from the trade linkages with such oil-exporting countries as Malaysia and Indonesia who are main trading partners of Singapore. However, these positive indirect effects do not last long for Singapore, moreover, direct and indirect effects of high oil prices are both negative for the other oil-importing countries considered in that analysis. Another essential comparison might be the results of the study by Korhonen and Ledyaeva (2008), which state that indirect effects for such oil-importing countries as UK, USA, Switzerland and Finland are positive. However, because of relatively high negative direct effects, total effects of an oil price shock are negative for these countries both in short and long run. On the other hand, indirect effects are negative for such oil-importing countries as Japan and China. Another interesting fact is that 63

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania such oil-importing countries as Italy, Belgium and France had positive direct effects of an oil price shock, though they are oil-importing countries. In current analysis, direct effects of an oil price shock for Lithuania are negative and indirect effects are positive. However, total effects of an oil price shock on Lithuanian real GDP growth are positive as well, because compared to some countries from previous studies mentioned above negative direct effects for Lithuanian GDP growth are mitigated by positive indirect effects both in short and long run. Moreover, the standpoint which was suggested by Berument, Ceylan and Dogan (2010); and Rasmussen and Roitman (2011) that “the oil-importing economies can actually weaken and mitigate the direct negative effects from the oil price shocks by the indirect positive effects that may come through the intensive trade linkages with an oil-exporting economy”, was also approved through the example of Lithuania in this research. 4.3 Limitations and Implications for Further Research This research includes some limitations that can be listed as following: 1) The period of study covers only around 18 years, starting from the 2nd quarter of year 1995 until the last quarter of 2012. As a quarterly data is used, the sample size consists of only 71 observations, which is actually accepted to be a low number for time series analysis. However, the short time scope factor is explained by the fact that some countries included in the model are former Soviet Union countries and data for them is available only from the beginning of 1995. 2) Another reason to be considered as a limitation for this study is the fact of recession periods which took place during the study period. However, the dummy variables for the economic recession in Russia in 1998 and the financial crisis of 2008 were not

64

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania added into the model. For instance, as it was done in the model used by Korhonen and Ledyaeva (2008) for the equation of Russian real GDP growth. 3) As the methodological approach used in this study is a simple VAR model with an exogenous variable, but not a structural VAR model used in previous analysis by Abeysinghe (2001) and, Korhonen and Ledyaeva (2008), it does not include changing trade matrices which may help to estimate not the fixed but changing impulse responses at any time as trading pattern changes over time. So that, the model used in this study provides an estimation for the trade relationships only in implicit way. As it was mentioned in earlier sections of the thesis, weighted averages of bilateral export shares are considered implicitly within the estimated parameters of the VAR model. 4) According to the results of empirical analysis, it is an obvious fact that Lithuania experiences positive indirect effects which come from its trade relationships with main trading partners. However, the methodological approach used in this analysis do not allow to estimate in which proportions each country does its contribution to this indirect effects. Intuitively, it can be stated that the biggest portion of this positive indirect effects of an oil price increase to the real GDP growth of Lithuania, comes from its main trading partner which is Russia and is a net oil-exporting country. Nonetheless, the exact and precise matrix of indirect effects between countries included in the model is not estimated. Based on the limitations highlighted above, one of the implications for further research which can include other list of countries that will not have former Sovet Union countries in it, is to use a longer time scope. As it‘s known, longer the study period is, more accurate the results of analysis are. On the other hand, some improvements can be done in the model used or the exact model developed by Abeysinghe (2001) might be implemented. So that the trade matrices can be directly considered and changing impulse 65

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania responses can be estimated. Moreover, a matix of indirect effects between countries which provides individual effects from each country in the model, can be determined.

66


CONCLUSIONS The main goal of the Thesis was to identify direct and indirect effects of an oil price shock on economic growth of Lithuania, at the same time considering its trade linkages with main trading partners. Additionally, to verify if possible positive indirect effects through the trade linkages can mitigate expected negative direct effects of an oil price shock. In order to estimate the above mentioned effects, the variables were selected and analyzed through the chosen period of the study. The reviewed literature provided different views on how oil price shocks affect the economic growth of oil-importing countries. While some of the previous studies stated that the sudden oil price increases have negative effects on economic growth of oil-importing countries, others presented a view that an oil price shock may affect an economy also indirectly through the trade linkages with main trading partners. Moreover, those oil-importing countries that have a net oil-exporter as their main trading partner might have stronger positive indirect effects. A VAR model with an exogenous variable was chosen, which allowed to estimate both direct and indirect effects of an oil price shock. However, it differed from the original model developed by Abeysinghe (2001) and used in the analysis by Korhonen and Ledyaeva (2008), which provided an estimation of changing impulse responses because of the changing trade weights. The study period was set starting from the 2nd quarter of 1995 till the 4th quarter of 2012. Quarterly data on real GDP growth series and quarterly oil price growth rates were used to conduct the current analysis. The results of empirical analysis showed that the cumulative direct effects of a 50% increase in oil price growth rates on real GDP growth of Lithuania were negative (-1.2130 and -1.2151 percentage changes in short run and long run, respectively). However, the cumulative indirect 67

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania effects through the trade linkages were positive (2.0130 and 2.8151 percentage changes in short run and long run, respectively) and mitigated relatively lower direct effects. In other words, estimated cumulative total effects on real GDP growth of Lithuania equaled to 0.8 and 1.6 percentage point increases in short run, and long run, respectively. The robustness of empirical results was affirmed by presenting the results from other VAR models with different oil price measures as exogenous variables. Accordingly, the estimated results for Lithuania were consistent in all mentioned cases. Overall, the selected methodology did not allow to estimate changing impulse responses and to identify the contribution of each country into the positive indirect effects on Lithuanian real GDP growth. Therefore, an improved model can be used and longer study period can be considered for a further research. However, though the trade weights between the selected countries were assumed to be constant, the main goals of current analysis were achieved and, both direct and indirect effects of an oil price shock on economic growth of Lithuania were estimated.

68


References Abeysinghe, T. (2001). Estimation of direct and indirect impact of oil price on growth. Economics Letters, 73(2), 147-153. Abeysinghe, T., & Forbes, K. (2005). Trade Linkages and Output-Multiplier Effects: A Structural VAR Approach with a Focus on Asia. Review Of International Economics, 13(2), 356-375. Amuzegar, J. (2009). OPEC’s Adaptation to Market Changes. Middle East Institute Viewpoints: The 1979 “Oil Shock:” Legacy, Lessons, and Lasting Reverberations, 9-12. Anorou, E., & Elike, U. (2009). An Empirical Investigation into the Impact of High Oil Prices on Economic Growth of Oil-Importing African Countries. International Journal Of Economic Perspectives, 3(2), 121-129. Arora, V., & Vamvakidis, A. (2005). How Much Do Trading Partners Mailer for Economic Growth?. IMF Staff Papers, 52(1), 24-40. Benedictow, A, Fjærtoft, D & Løfsnæs, O. (2010). Oil dependency of the Russian economy: An econometric analysis. Statistics Norway, Research Department, Discussion Paper, 617. Berument, M., Ceylan, N., & Dogan, N. (2010). The Impact of Oil Price Shocks on the Economic Growth of Selected MENA Countries. Energy Journal, 31(1), 149176. Bjørnland, H. C. (2000). VAR Models in Macroeconmic Research. Documents 2000/14, Statistics Norway.

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Blanchard, O. J. & Gali, J. (2007). The Macroeconomic Effects of Oil Shocks: Why are the 2000s So Different from the 1970s?. NBER Working Papers, 13368. Borges, A. M. (1986). Applied General Equilibrium Models: An Assessment of Their Usefulness for Policy Analysis. OECD Economic Studies, (7), 7-43. Brooks, C. (2008). Introductory Econometrics for Finance. Second edition. Cambridge: Cambridge University Press. Chuku, C. A. (2012). Linear and asymmetric impacts of oil price shocks in an oilimporting and -exporting economy: the case of Nigeria. OPEC Energy Review, 36(4), 413-443. doi:10.1111/j.1753-0237.2012.00220.x Ciscar, J. C., Russ, P., Parousos, L. & Stroblos, N. (2004). Vulnerability of the EU Economy to Oil Shocks: a General Equilibrium Analysis with the GEM-E3 Model. Institute for Prospective Technological Studies. Corden, W., & Neary, J. (1982). Booming Sector and De-industrialisation in a Small Open Economy. Economic Journal, 92(368), 825-848. Darby, M. R. (1984). The U.S. Productivity Slowdown: A Case of Statistical Myopia. American Economic Review, 74(3), 301. Dybczak, K., Voňka, D. & van der Windt, N. (2008). The Effect of Oil Price Shocks on the Czech Economy, Czech National Bank, Working Paper Series, 5. El Anshasy, A. A. (2009). Oil prices and economic growth in oil-exporting countries. Retrieved from: http://www.usaee.org/usaee2009/submissions/OnlineProceedings/ElAnshasy_oil Prices_growth.pdf

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Ghalayini, L. (2011). The Interaction between Oil Price and Economic Growth. Middle Eastern Finance and Economics, 13. Granger, C. J. (1969). Investigating Causal Relations by Econometric Models and Crossspectral Methods. Econometrica, 37(3), 424-438. Hamilton, J. D. (1983). Oil and the Macroeconomy since World War II. Journal Of Political Economy, 91(2), 228-248. Hamilton, J. D. (2003). What is an oil shock?. Journal Of Econometrics, 113(2), 363. doi:10.1016/S0304-4076(02)00207-5 Hamilton, J. D. (2005). Oil and the Macroeconomy. Retrieved from: http://econweb.ucsd.edu/~jhamilton/JDH_palgrave_oil.pdf Hamilton, J. D. (2009). Causes and Consequences of the Oil Shock of 2007-08. Brookings Papers On Economic Activity, (1), 215-283. Hamilton, J. D. (2011). Historical Oil Shocks. NBER Working Paper Series, 16790. Hamilton, J. D. (2012). Oil prices, exhaustible resources, and economic growth. NBER Working Paper No. w17759. Inklaar, R., Jong-A-Pin, R., & de Haan, J. (2008). Trade and business cycle synchronization in OECD countries—A re-examination. European Economic Review, 52(4), 646-666. doi:10.1016/j.euroecorev.2007.05.003 Issawi, C. (1978). The 1973 oil crisis and after. Journal Of Post Keynesian Economics, 1(2), 3. Ito, K. (2012). The impact of oil price volatility on the macroeconomy in Russia. Annals Of Regional Science, 48(3), 695-702. doi:10.1007/s00168-010-0417-1. 71

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Jayaraman, T. K., & Lau E. (2012). Oil Price and Economic Growth in Small Pacific Island Countries. Modern Economy, 2, 153-162. Jiménez-Rodríguez, R., & Sánchez, M. (2004). Oil price shocks and real GDP growth: empirical evidence for some OECD countries. European Central Bank, Working Paper Series, 362. Keikha, A., Keikha, A. & Mehrara, M. (2012). Institutional quality, economic growth and fluctuations of oil prices in oil dependent countries: A panel cointegration approach. Modern economy, 3, 218-222. Kilian, L. (2008a). The Economic Effects of Energy Price Shocks. Journal Of Economic Literature, 46(4), 871-909. doi:10.1257/jel.46.4.871 Kilian, L. (2008b). A Comparison of the Effects of Exogenous Oil Supply Shocks on Output and Inflation in the G7 Countries. Journal Of The European Economic Association, 6(1), 78-121. Korhonen, I., & Ledyaeva, S. (2010). Trade linkages and macroeconomic effects of the price of oil. Energy Economics, 32(4), 848-856. doi:10.1016/j.eneco.2009.11.005 Lee, K., Ni, S. & Ratti, R. A. (1995). Oil shocks and the macroeconomy: The role of price variability. Energy Journal, 16(4), 39. Malik, A. (2008). How Pakistan is coping with the Challenge of High Oil Prices. MPRA Paper, 8256. Marquez, J. (1984). Oil price effects in theory and practice. International Finance Discussion Papers, 237.

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Mendoza, O., & Vera, D. (2010). The asymmetric effects of oil shocks on an oilexporting economy. Cuadernos de Economía, 47(135), 3-13. Mork, K. (1989). Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results. Journal Of Political Economy, 97(3), 740. Mork, K., Olsen, O. & Mysen, H. T. (1994). Macroeconomic responses to oil price increases and decreases in seven OECD countries. Energy Journal, 15(4), 19. Moshiri, S. & Banihashem, A. (2012). Asymmetric Effects of Oil Price Shocks on Economic Growth of Oil-Exporting Countries. Retrieved from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2006763 Oyeyemi, A. M. (2013). The Growth Implications of Oil Price Shock in Nigeria, Journal of Emerging Trends in Economics and Management Sciences, 4(3), 343-349. Papapetrou, E. (2009). Oil price asymmetric shocks and economic activity: the case of Greece. Retrieved from: http://www.aaee.at/2009IAEE/uploads/fullpaper_iaee09/P_489_Papapetrou_Evangelia_4-Sep2009,%2015:13.pdf Rasmussen, T. N., & Roitman, A. (2011). Oil Shocks in a Global Perspective: Are they Really that Bad?. IMF Working Papers, 11/194. Rautava, J. (2002). The role of oil prices and the real exchange rate in Russia’s economy. BOFIT Discussion Papers, 3. Robays, I. V. (2012). Macroeconomic Uncertainty and the Impact of Oil Shocks. European Central Bank, Working Paper Series, 1479.

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Schneider, M. (2004). The impact of oil price changes on growth and inflation. Monetary Policy and the Economy, 2, 27-36. Segal, P. (2007). Why Do Oil Price Shocks No Longer Shock?. Oxford: Oxford Institute for Energy Studies. Sill, K. (2007). The Macroeconomics of Oil Shocks. Business Review, Q1, 21-31. Sims, C. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1-48. Stock, J. H., & Watson, M. W. (2010). Introduction to Econometrics. Fourth Edition. Mason, OH: South-Western Cengage Learning. Tang, W., Wu, L., & Zhang, Z. (2009). Oil price shocks and their short- and long-term effects on the Chinese economy. East-West Center Working Paper, Economics Series, 102. Tobin, J. (1980). Stabilization Policy Ten Years After. Brookings Papers On Economic Activity, (2), 19-71. Umar, G., & Kilishi, A. (2010). Oil Price Shocks and the Nigeria Economy: A Variance Autoregressive (VAR) Model. International Journal Of Business & Management, 5(8), 39-49. Verbeek, M. (2004). A Guide to Modern Econometrics. Second Edition. West Sussex: John Wiley & Sons Ltd.

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Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Appendix 1 Time series plots of real GDP growth rates of selected countries. 1) Time series plot for real GDP growth rates of Russia.

2) Time series plot for real GDP growth rates of Germany.

3) Time series plot for real GDP growth rates of Netherlands.

75


4) Time series plot for real GDP growth rates of France.

5) Time series plot for real GDP growth rates of Poland.

6) Time series plot for real GDP growth rates of Lithuania.

76


7) Time series plot for real GDP growth rates of Latvia.

77

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Appendix 2 Correlograms obtained for the real GDP growth rates of selected countries.

78


79

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Appendix 3 1) The results of lag length selection (real oil price measure deflated by Lithuanian GDP deflator, as an exogenous variable). lags 1 2 3 4

loglik -558.35233 -499.96398 -457.99275 -420.17937

p(LR) 0 0.00139 0.00865

AIC 18.547831 18.267581* 18.477396 18.811325

BIC 20.620900* 21.953038 23.775239 25.721555

HQC 19.368149* 19.725926 20.573765 21.54572

2) The results of lag length selection (nominal oil price measure in US dollars, as an exogenous variable). lags 1 2 3 4

loglik -560.99642 -502.66071 -459.68574 -420.91673

p(LR) 0 0.00087 0.0058

AIC 18.626759 18.348081* 18.527933 18.833335

BIC 20.699828* 22.033537 23.825776 25.743566

HQC 19.447077* 19.806425 20.624303 21.567731

3) The results of lag length selection (nominal oil price measure in Euros, as an exogenous variable). lags 1 2 3 4

loglik -558.62464 -500.27672 -458.13755 -419.92783

p(LR) 0 0.00129 0.00734

AIC 18.555959 18.276917* 18.481718 18.803816

80

BIC 20.629029* 21.962373 23.779561 25.714046

HQC 19.376278* 19.735261 20.578088 21.538211

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Appendix 4 The results of the estimated VAR model (real oil price measure deflated by EU GDP deflator as an exogenous variable). VAR system, lag order 1 OLS estimates, observations 1995:3-2012:4 (T = 70) Log-likelihood = -587.34982 Determinant of covariance matrix = 0.045788791 AIC = 18.5814 BIC = 20.6051 HQC = 19.3852 Portmanteau test: LB(17) = 788.521, df = 784 [0.4480] 1) Equation 1: Real GDP growth rates of Russia coefficient 0.486493 0.402181

std. error 0.24362 0.103421

t-ratio 1.997 3.889

p-value 0.0503 * 0.0003 ***

0.421426

0.281725

1.496

0.1398

Real GDP growth rates of Netherlands (lag 1)

-0.271089

0.32359

-0.8378

0.4054


0.680009

0.457193

1.487

0.1421


-0.32027

0.157447

-2.034

0.0463 **


-0.127733

0.106913

-1.195

0.2368


0.15921

0.0829653

1.919

0.0597 *


2.4897

1.06606

2.335

0.0228 **

const Real GDP growth rates of Russia (lag 1) Real GDP growth rates of Germany (lag 1)

81


0.906131 102.8209 0.504858 7.774633 -0.041603

1.734808 1.298302 0.439922 3.95E-07 2.07384


F-tests of zero restrictions: All lags of real GDP growth rates of Russia All lags of real GDP growth rates of Germany All lags of real GDP growth rates of Netherlands All lags of real GDP growth rates of France All lags of real GDP growth rates of Poland All lags of real GDP growth rates of Lithuania All lags of real GDP growth rates of Latvia

F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) =

15.123 [0.0003] 2.2376 [0.1398] 0.70183 [0.4054] 2.2122 [0.1421] 4.1377 [0.0463] 1.4274 [0.2368] 3.6825 [0.0597]

2) Equation 2: Real GDP growth rates of Germany coefficient -0.187659 0.128182

std. error 0.127889 0.0542909

t-ratio -1.467 2.361

p-value 0.1474 0.0214 **

0.0538269

0.147892

0.364

0.7171


-0.075792

0.169869

-0.4462

0.657


0.862849

0.240004

3.595

0.0006 ***


0.174302

0.0826519

2.109

0.0391 **


-0.087926

0.056124

-1.567

0.1224


-0.0254385

0.0435527

-0.5841

0.5613


0.393594

0.559631

0.7033

0.4845


82


0.320000 28.33468 0.447581 6.177924 0.029808

0.862185 0.681545 0.375132 7.96E-06 1.935183



F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) =

5.5744 [0.0214] 0.13247 [0.7171] 0.19908 [0.6570] 12.925 [0.0006] 4.4473 [0.0391] 2.4544 [0.1224] 0.34116 [0.5613]

3) Equation 3: Real GDP growth rates of Netherlands coefficient -0.0159524 0.00986508

std. error 0.100318 0.0425868

t-ratio -0.159 0.2316

p-value 0.8742 0.8176

-0.118398

0.116009

-1.021

0.3115


0.283649

0.133248

2.129

0.0373 **


0.813447

0.188263

4.321

5.83e-05 ***


0.0740405

0.0648336

1.142

0.2579


-0.0298087

0.0440247

-0.6771

0.5009


0.0106335

0.0341635

0.3113

0.7567


-0.0736724

0.438985

-0.1678

0.8673


83


0.461429 17.43464 0.531149 8.638154 -0.017164


0.734115 0.534615 0.46966 8.63E-08 2.004468


F(1, 61) = 0.053660 [0.8176] F(1, 61) = 1.0416 [0.3115] F(1, 61) = 4.5315 [0.0373] F(1, 61) = 18.669 [0.0001] F(1, 61) = 1.3042 [0.2579] F(1, 61) = 0.45845 [0.5009] F(1, 61) = 0.096878 [0.7567]

4) Equation 4: Real GDP growth rates of France coefficient 0.0694732 -0.000615808

std. error 0.0767707 0.0325905

t-ratio 0.9049 -0.0189

p-value 0.3691 0.985

-0.110735

0.0887784

-1.247

0.217


0.176557

0.101971

1.731

0.0884 *


0.541611

0.144073

3.759

0.0004 ***


0.0419101

0.0496154

0.8447

0.4016


-0.0576503

0.0336909

-1.711

0.0921 *


0.0517943

0.0261444

1.981

0.0521 *


0.522808

0.335943

1.556

0.1248


84


0.381429 10.21048 0.479213 7.016299 -0.124198


0.533051 0.409127 0.410913 1.59E-06 2.242551

F-tests of zero restrictions: All lags of real GDP growth rates of Russia All lags of real GDP growth rates of Germany All lags of real GDP growth rates of Netherlands All lags of real GDP growth rates of France All lags of real GDP growth rates of Poland All lags of real GDP growth rates of Lithuania All lags of real GDP growth rates of Latvia 5)

F(1, 61) = 0.00035703 [0.9850] F(1, 61) = 1.5558 [0.2170] F(1, 61) = 2.9979 [0.0884] F(1, 61) = 14.132 [0.0004] F(1, 61) = 0.71352 [0.4016] F(1, 61) = 2.9280 [0.0921] F(1, 61) = 3.9247 [0.0521]

Equation 5: Real GDP growth rates of Poland coefficient 0.947655 0.193236

std. error 0.187986 0.0798033

t-ratio 5.041 2.421

p-value 4.42e-06 *** 0.0184 **

-0.242785

0.217389

-1.117

0.2684


0.662552

0.249693

2.653

0.0101 **


-0.129615

0.352787

-0.3674

0.7146


-0.198792

0.121492

-1.636

0.1069


-0.0814302

0.0824978

-0.9871

0.3275


0.0458061

0.064019

0.7155

0.477


-0.33652

0.822613

-0.4091

0.6839


85


1.045714 61.22176 0.222617 2.183556 0.071394

1.068344 1.001816 0.120666 0.041096 1.807823



6)

F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) = F(1, 61) =

5.8632 [0.0184] 1.2473 [0.2684] 7.0409 [0.0101] 0.13499 [0.7146] 2.6773 [0.1069] 0.97428 [0.3275] 0.51195 [0.4770]

Equation 7: Real GDP growth rates of Latvia coefficient -0.277759 0.29587

std. error 0.403115 0.171129

t-ratio -0.689 1.729

p-value 0.4934 0.0889 *

-0.223375

0.466166

-0.4792

0.6335


0.351624

0.53544

0.6567

0.5138


1.52036

0.756512

2.010

0.0489 **


0.17054

0.260525

0.6546

0.5152


0.168655

0.176907

0.9533

0.3442


-0.0223425

0.137282

-0.1627

0.8713


1.96837

1.76400

1.116

0.2689


86


1.100000 281.5222 0.338529 3.902332 -0.022641


2.483569 2.148283 0.251778 0.0009 2.033296


87

F(1, 61) = 2.9892 [0.0889] F(1, 61) = 0.22961 [0.6335] F(1, 61) = 0.43126 [0.5138] F(1, 61) = 4.0389 [0.0489] F(1, 61) = 0.42850 [0.5152] F(1, 61) = 0.90887 [0.3442] F(1, 61) = 0.026487 [0.8713]

Direct and indirect effects of oil price shocks on economic growth: case of Lithuania Appendix 5 Correlograms for the residuals of equations from the estimated VAR model.

88


89