• About Us +
• Editorial Board +
• For Contributors +
• Journal Search +
Journal Search Engine
ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.21 No.1 pp.110-118
DOI : https://doi.org/10.7232/iems.2022.21.1.110

# Impact of Oil Price Fluctuations on Economic Growth, Financial Development, and Exchange Rate in Iraq: Econometric Approach

Hayder Abbas Drebee*, Nor Azam Adual Razak
University of Al-Qadisiyah, Iraq
University Utara Malaysia, Malaysia
*Corresponding Author, E-mail: hayder.drebee@qu.edu.iq
July 14, 2021 November 26, 2021 January 10, 2022

## ABSTRACT

Oil is the main source of energy, which plays a pivotal role in the global economy. Therefore, its price fluctuations have a significant impact on the economy of the countries that trade oil. The objective of this study is to investigate the effects of oil price fluctuations on economic growth, financial development and exchange rate in Iraq. To achieve this objective, IRFs and VDCs, which are estimated from VAR, have been used. This study found that great fluctuations in economic growth, financial development, and exchange rate are due to oil price fluctuations. It is a reflection of oil resource mismanagement besides the rampant corruption in the state administrations additional to allocating most of financial resources to the war efforts when Iraq fought against terrorism. Therefore, it is necessary for the decision makers to adopt a certain strategy based on reducing the dependence of the Iraqi economy on crude oil. Prompting financial sector by developing well-regulated economic policies of economic diversity is needed as well. It could be done institutionally by regulating frameworks to build confidence in the economy and reducing the dominance of the public sector.

## 1. INTRODUCTION

Oil is the main energy source that plays a pivotal role in the global economy. Although many institutions and companies have switched to alternative sources of renewable energies such as wind power, nuclear power, and solar power, the demand for crude oil in the global economy has not dwindled so far. Therefore, oil price fluctuations have a significant effect on the economy of oil exporting and importing countries. Moreover, oil is a major source of income in oil exporting countries and a major input to the production in oil importing countries. Because of this mutual importance, oil price exhibits more fluctuations than the prices of other commodities (Dehn, 2000). The oil market has experienced successive fluctuations since the 1970s to the present day (see Figure 1).

Over the past several decades, researchers have been interested in studying the effects of oil price fluctuations on economic activities, identifying mechanisms by which to reduce these effects, and proposing effective monetary and fiscal policies to prevent negative impacts of such fluctuations (Bernanke, 2004;Devlin and Lewin, 2004;Hamilton, 1983). Most studies have found that oil price fluctuations are important sources that have exacerbated the economic conditions in most countries. However, most studies have focused on the economies of oilimporting countries. Therefore, their results are valid for these countries only. This issue is quite different for oilexporting countries such as Iraq, where oil is the only lung that breathes the budget of Iraq. In 2011, for example, oil exports accounted for 95% of the revenues of the Iraqi government and more than 70% of GDP.

On the other hand, the expenditure of oil revenues on the construction of infrastructure and the revival of productive sectors such as agriculture and tourism are characteristics of the economy. In other words, the fiscal and monetary policies of the oil-exporting countries, including Iraq, depend mainly on the oil revenues. Therefore, if preventive measures are not taken by decision makers to mitigate the fluctuations in the oil price, it would affect the Iraqi economy. Although the government does not face problems in financing its projects when the oil price is increasing, it needs to follow certain strategies to enhance manufacturing, agriculture, and tourism sectors. Otherwise, the economy would be vulnerable to oil price fluctuations. In the case of low oil price, the government sector cannot reduce its expenses directly and thereby ended up increasing the fiscal deficit and debt and hampering project implementation.

The fluctuations in the oil price play a big role in the economy of both oil exporting and importing countries. Therefore, their impact on various economic activities is one of the issues that have been discussed in many studies since the mid-1970s when oil price has increased in 1973. Most of these studies have been conducted in developed countries, especially in the United States (Jiménez- Rodríguez and Sánchez, 2004). It is quite surprising to learn that there were few studies on the effects of the oil price fluctuations on the economy of the oil-exporting countries themselves (Berument et al., 2010).

Most studies have dealt with the impact of oil price fluctuations on GDP (Berument et al., 2010). Other studies have dealt with the impact of those fluctuations on GDP and money supply (Algahtani, 2016), financial markets (Abdalla, 2013), exchange and interest rates (Eryiğit, 2012), national output, inflation, or unemployment and the proportion of exports to imports (Ayşen and Mehmet, 2014). On the other hand, the impact of those fluctuations on the financial development has not been dealt with in the previous studies in both developed and developing countries. Therefore, this study has attempted to determine the impact of oil price fluctuations on the financial development in an oil-dependent economy, such as Iraq, which is ranked the third in the list of oil exporting countries, in addition to the impact of those fluctuations on economic growth and the exchange rate.

In light of this, this study aims to examine the impact of the oil price fluctuations on the Iraqi economy (economic growth, Iraqi Dinar exchange rate against the US dollar and financial development), and enrich the library by a new study on the effects of the oil price fluctuations on the economic activity in oil-exporting countries. To achieve this objective, the Impulse Response Function (IRFs) and variance decompositions (VDCs) were used, which were estimated from the Vector Auto Regressive Model (VAR). The importance of this study lies in addressing one of the important topics in the Iraqi economy, which relies mainly on oil in addition to providing practical studies for decision makers to make their decision on a scientific basis.

The rest of this study is divided into four sections: the second section deals with a series of previous studies that have dealt with the impact of oil price fluctuations on various economic activities. The third part describes the data and statistical methods that have been employed and discussed the empirical results that have been obtained. Finally, fourth section provides a summary of the findings and recommendations of the study.

## 2. LITERATURE REVIEW

There are many studies that have dealt with the impact of oil price fluctuations on the economic variables since 1970 (Hamilton, 1983;Gisser and Goodwin, 1986;Mork, 1989;Naccache, 2010). The results of these studies differ from one country to another depending on whether they are developed or developing, or oil exporting or importing. A majority of these studies are concerned with the impact of these fluctuations on the economic activities of oil-importing countries. One of the leading studies in this field is the study of Hamilton (1983), which concluded that the oil price shock was the cause of all economic recessions in America for the period of 1949 to 1973. This study also found that changes in oil price would lead to changes in unemployment and GNP.

However, Hooker (1996) has criticized Hamilton (1983) by pointing out that oil price shocks do not cause most of the US economic changes by using quarterly data during the period 1973-1994. In his defense, Hamilton (1996) explained that the impact of oil price fluctuations on economic variables should be measured by comparing its price of the current year with its price in the past year, but not with the previous quarter year itself. Burbridge and Harrison (1984) found that the negative impact of the oil price shock on industrial production in Canada, Germany, Japan, and America is consistent with Gisser and Goodwin (1986) study, which is the effect of oil price fluctuations on the US macro economy before and after 1973 is different.

Using GARCH model to study oil price shock on GNP during the 1949-1992, Lee et al. (1995) found that Mork (1989) method of separating positive and negative effects does not reveal the strong impact of the oil price shock on GNP during the study periods. Since oil price fluctuated in nature, companies and economic agencies expect rising in oil price for the short term. To assess the impact of the oil price shock on GDP in the OECD countries, a sample of seven OECD countries was taken by Jiménez-Rodríguez and Sánchez (2004). Using VAR, this study found that increasing, rather than decreasing, oil price has a greater effect on the GDP growth. This confirms that the fluctuations in oil prices have a significant negative impact on economic activity in the oil-importing countries, but unclear impact for the oil-exporting countries.

Although most of the studies on the impact of oil price fluctuations on economic activities are conducted in the developed countries and oil-importing countries, there are studies that have dealt with this issue in the oilexporting countries and developing countries. Berumen et al. (2010) studied the impact of oil price fluctuations on GDP growth in several of the Middle East and North Africa countries during the period 1952-2004 by using VAR and IRF. The study has concluded that oil price fluctuations do not affect the GDP in the oil-importing countries, while oil-importing countries have been significantly affected.

Eltony and Al-Awadi (2001) examined the impact of the oil price shock on the Kuwaiti economy from 1998 to 1984 using the VAR model. This study concluded that the oil price shock is the main cause of fluctuations in the economic activities. There is a one-direction causal relationship between oil prices and revenues, and between governments spending to other variables. Berument and Ceylan (2004) used structural VAR (SVAR) model and IRF to determine the impact of oil price shocks on GDP in 15 MENA countries that exported crude oil or imported petroleum products during the period 1970-2003. The study has found the impact of oil price shocks in the countries of Algeria, Iran, Iraq, Jordan, Kuwait, Oman, Qatar, Syria, Tunisia, and the United Arab Emirates. On the other hand, no significant effect of oil price fluctuations on their real GDP was found in Bahrain, Egypt, Lebanon, Morocco and Yemen. VAR model was used in Nigeria to determine the impact of oil price shocks on GDP, inflation, exchange rate and money supply using the quarterly data from 1970 to 2003 by Olomola and Adejumo (2006). They concluded that the shock affects the exchange rate and long-term money supply only.

Similar studies have been carried out in Indonesia and Iran by Ward and Siregar (2001) and Farzanegan and Markwardt (2009), respectively. The study of Ftiti et al. (2016) concluded that an oil price shock influenced economic growth in the short and long run. The effect is more in the medium run than in the short run in four OPEC countries (United Arab Emirates, Kuwait, Saudi Arabia and Venezuela) during the period 2000-2010 using co-integration test. The study of Rahma et al. (2016) concluded that the decrease in the oil price has a significant impact on GDP growth in Sudan. The unemployment rate there is positively affected by decreasing oil prices by applying VAR during the period 2000-2014.

In Saudi Arabia, several studies have been conducted including the study of Algahtani (2016) which has indicated a positive relationship between oil price and Saudi Arabian domestic product in the long run by applying VAR and VECM during the period 1970-2020.

### 3.1 Data and Statistical Analysis

The objective of this study is to investigate the impact of oil price fluctuations on economic growth, financial development and Iraqi Dinar exchange rate against the US Dollar. GDP per capita is used as a proxy for economic growth, ratio of broad money supply to GDP (M2 / GDP) is used as a proxy for financial development, and Iraqi Dinar exchange rate against the US dollar during the period 1980-2020 is used to measure the exchange rate.

We employed IRFs to identify the effect of oil price fluctuations on the variables of the study with the determination of the periodic intervals whose effect remains on those variables. We employed VDCs to determine the relative importance of oil price fluctuations in the interpretation of model variables. VDCs are employed by using the annual data from 1980 to 2020, which is obtained from the International Monetary Fund Statistics. The variables are used in natural logarithms. Table 1 shows the description of the variables of the model.

Table 1 shows that the crude oil price varied widely: the lowest value is $12.28 per barrel, while the highest is$109.45 per barrel with an average of 39.818 and a stan-dard deviation of 30.345. Gross domestic product (GDP) also varied widely from 807.8 to 8024886, and Iraqi Dinar exchange rate against the US. Dollar varied widely as well. The lowest exchange rate is that each Iraqi Dinar is equivalent to the US $0.3, while the highest value is 1972 Iraqi Dinars per US$1 with an average of 878 and a standard deviation of 759.

### 3.2 Unit Root Test

It is well known that most of the time series data are characterized as nonstationary (Greene, 2000;Nelson and Polsser, 1982) and this renders the regression results to be spurious (Granger and Newblod, 1974;Drebee et al., 2014; 2018). For this reason, the stationarity of data had been determined prior to estimating the impact of crude oil price fluctuation on the variables of the study. To achieve this, the stationarity of each variable will be determined by using the Augmented Dickey Fuller (ADF) test, which is affected by the existence of intercept or intercept and trend or neither of both. Therefore, this test was conducted twice, first by estimating the regression that contains an intercept and trend and, second, by estimating the regression with an intercept only.

ADF is considered to be one of the unit root tests which uses the following equations:

where Δ refers to the first difference of the time series, Yt ; δ the parameter of the sloping variable, t the time trend, εt the error term, p number of optimal delay periods, which is determined by several criteria, the most important of which are Akaike information criterion (AIC) and Schwartz Bayesian criterion (SBC). Optimal delay periods are used to address the autocorrelation problem. Table 2 shows the results of the ADF test at levels and first differences. It shows that all of the study variables are non-stationary at levels, but there are stationary at the first differences.

### 3.3 Co-integration Test

Conducting a co-integration test requires that all of the time series be integrated of the same order. Since the unit root test confirmed that all variables are integrated of the same order, the second step is to test the existence of co-integration among those variables. In other words, testing the long-run relationship among variables is needed. To achieve this objective, the Johansen and Juselius test is used (Johansen and Juselius, 1990), which is considered one of the most important tests of the cointegration in the case of more than two variables. It treats all variables of the model as internal variables, and the following equation illustrates this test:

where the effects of the long-run model variables are referred by the symbol Π in the equation above, while the rank of the matrix is indicated by the symbol r, which specifies the number of co-integration vectors of the model.

To test the existence of co-integration among the variables of a model and to find the number of vectors of co-integration, Johansen and Juselius (1990) and Johansen (1988) will be used. These methods propose two tests: the first is the Trace Test (λtrace ), and its statistics are calculated as follows:

The null hypothesis to be tested in this test is

$H 0 : r ≤ n vs. H 1 : r > n .$

A second test is the Maximum Eigenvalue Test (λmax ), and its statistics are calculated as follows:

The null hypothesis is:

$H 0 : r ≤ r 0 Vs. H 1 : r > r 0 + 1$

If the calculated values are greater than the corresponding values in Johansen and Juselius (1990) the null hypothesis would be rejected. This indicates the presence of the long-run equilibrium relationship among the variables. In other words, there are a co-integration among these variables. Table 3 shows the results of applying the two tests for the co-integration of the variables in the model.

It is noted from Table 3 that the value calculated for both tests is greater than the critical value of their statistics at a significant level of 5%. Thus, the null hypothesis is rejected. Therefore, there is a co-integration (long-run equilibrium relationship) among the study variables.

### 3.4 Impulse Response Function (IRF) and Variance Decomposition (VDC)

Impulse Response Function (IRF) is used to measure the current and future values in model variables that have occurred in response to the fluctuations of a variable within VAR or VECM.

In other words, that function shows the effects of shock in one of the variables on the other, while the VDC measures the extent to which crude oil price is responsible for interpreting the variance of forecasting errors of economic growth, financial development, and exchange rate.

VDC determines the relative contribution of oil price fluctuations to the interpretation of changes in these variables.

IRF and VDC were obtained by converting the following equation,

The current values of each internal variable are interpreted according to the previous values of this variable and the other variables, to the following equation:

$Δ Y t = α 0 + A 0 ε t + A 1 ε t − 1 + A 2 ε t − 2 + A 3 ε t − 3 + ........... = α 0 + ∑ i = 0 ∝ A i ε t − i$

where y refers to the total number of variables in the model (n × 1) , n is the number of variables in the model, α0 is the constant vector; T is the number of observations in the regression, p is the optimal delay period, Ai represents the matrix of the basic model coefficients (n × n), and ε is the random error vector (n ×1).

Response of variables study to a shock in the crude oil price can be obtained by one standard deviation in time (t) in the short run on the variables in the time period t+d (d represents a specified time horizon) by the following equation:

$A i j , d = ∂ y i . , t + d ∂ ε j , t$

The response of long-run model variables can be obtained by collecting pulse responses from 0 to d, i.e.:

$∑ i = 0 d A j = ∂ y , t + d ∂ w , t$

Variance Decomposition is calculated by the fluctuations to which y.j is exposed as follows:

The relative importance of variable j is obtained by interpreting the change in variable i by the following equation:

where k represents the economic shock, and the number of shocks is represented by n.

It is noteworthy that many studies support the use of a vector error correction model (VECM) when there is a co-integration among variables. However, Engle and Yoo (1987) show that a VAR is superior (in terms of forecast variance) to a VECM in short horizons. Besides that, the advantages of VAR are demonstrated from Naka and Tufte (1997) by studying IRFs in co-integrated systems. According to their analysis, if there is a co-integration among variables, the VECM estimates are relatively poor compared to VAR.

Figure 2 shows the effect of oil price fluctuations on economic growth, financial development and Iraqi Dinar exchange rate against the US. Dollar in short and long runs. These fluctuations have had a positive effect on the per capita GDP of Iraq in the first, second, third, fourth, and twenty-fifth periods. This means that the increase in oil price in these periods leads to the increases in that average. This effect is negative in the fifth to the twentyfourth time periods. In other words, the fluctuations of oil price have a positive effect in both short and long runs.

It is worth mentioning that the highest response in economic growth to oil price fluctuations is in the first period followed by the fourth one and then the third. In contrast, the lowest response is in the tenth time period followed by the ninth one and then the eleventh period. The positive value of response coefficient in economic growth results from oil price fluctuation between the rising and declining during the periods of study.

The fluctuations in crude oil price on (M2 / GDP) have a negative impact on both short and long runs. The negative impacts on the financial development in all periods except the 6th to the 14th periods were shown. The highest response for (M2 / GDP) to oil price fluctuations is in the sixth period followed by the 15th and 21st periods respectively. In contrast, the lowest response is in the 9th period then the 24th and the 19th periods.

The oil price fluctuations have positively affected Iraqi Dinar exchange rate against the US Dollar in all periods except the fourth to 14th periods. This indicates that the oil price fluctuations have positively affected the exchange rate of both short and long run. The highest period in response to these fluctuations is the 17th one followed by the first one and then the 16th. The 8th, 9th and 10th periods had the lowest response of exchange rate to oil price fluctuation.

Based on the previous results, it can be seen that economic growth, financial development and Iraqi Dinar exchange rate against the US. Dollars fluctuated between rising and declining due to crude oil price fluctuation during the study period. This reflects the mismanagement of oil resources and allocation of most resources for wars and the fight against terrorism and the economic blockade in the last decade of the past century in addition to the prevalent corruption in the state administrations.

While the analysis using IRF is qualitative one, the analysis using VDC is a quantitative one. Table 4 shows the significance of all estimated values of variance of predicted variance of oil price fluctuations in explaining the change in economic growth, financial development and the rate of exchange of the Iraqi dinar against the US dollar. Each value is at least twice the estimated standard error (Kouass et al., 1977;Wheeler, 1999) except for the first period of financial development and the second, third, and fourth periods of Iraqi Dinar exchange rate against the US dollar. This reflects the weakness of the relative contribution of fluctuations in crude oil price in the interpretation of the change in financial development and Iraqi Dinar exchange rate against the US dollar during these periods. The estimated values of the variance of explanatory error for oil price fluctuations are statistically insignificant. The relative importance of oil price fluctuations on economic growth, financial development and exchange rate has been very pronounced in the study periods except for the first period of financial development and the second, third, and fourth periods of the exchange rate.

As shown in Table 4, the percentage of variance of the explanatory error or the relative importance of oil price fluctuations in the interpretation of the change in financial development is the highest in all periods. It compared to the relative importance of oil price fluctuations for other variables except the 7th to 11th periods where the relative importance of oil price fluctuations in the explaining of change in the exchange rate is largest in these periods. The variance of the explanatory error of oil price fluctuations in explaining the change in economic growth is the highest in the first period only as compared to the variance of explanatory error of other variables.

The relative importance of oil price fluctuations in the explaining of change in economic growth has decreased from the first to the sixth period. It decreased from 14.246% in the first period to 3.038% in the sixth period. It increased from 6.378% in the 7th period to 24.790% at end of the table.

The highest value of the relative importance of oil price fluctuations is explaining the change in economic growth is in the 13th period. On the other hand, the lowest value of that importance is in the sixth period. The relative importance of oil price fluctuations in the explaining of the change in financial development has fluctuated significantly between rising and declining in the study period. This importance has increased by 5400% from the first to the second period, but it decreased till the seventh period by 63% then increased until the 18th period by 174% followed by slight fluctuation until the end of the table. The highest value of that importance was in the 18th period, while the lowest value was in the first period.

The relative importance of oil price fluctuations in the explaining of the change in the Iraqi Dinar exchange rate against the US. Dollar fluctuated very significantly between the rising and declining. This importance decreased by 539% from the first to the sixth period and then increased from the seventh to the 11th period by 128%. This followed by a slight fluctuation going to the end of the table period. The highest value of that importance takes place in the 17th period, while the lowest value is in the third period.

## 4. CONCLUSION

This study aimed to examine the impact of crude oil price fluctuations on economic growth, financial development and Iraqi Dinar exchange rate against the US. Dollar. GDP per capita is used as a proxy for economic growth, while the ratio of broad money supply to GDP (M2/GDP) is used as a proxy for financial development during the period 1980-2020. Thus, this study presented the first experimental assessment of the fluctuations effect of the price of crude oil on those variables, especially financial development in ongoing developing economies that depend mainly on oil, such as Iraq. To achieve this objective, IRF and VDA have been used.

It concluded that there is a long-run equilibrium relationship among the variables. It also finds that the GDP per capita, ratio of broad money supply to GDP, and exchange rate are affected by the fluctuations in the oil price in both short and long run. These fluctuations had positive effects on the first four and last periods of economic growth, while the negative effects had occurred in the other periods. The highest response of economic growth to oil price fluctuations was in the first period, while the fourth period had the lowest responsive.

The impact of those fluctuations on financial development is negative across the study periods except the sixth to the 14th periods. The sixth period had the highest value, in which there was a response to the financial development of oil price fluctuations. The lowest one was the 9th period. The exchange rate response due to these fluctuations was positive both in short and long run. This response was positive in all periods except for periods of fourth to 14th. The highest response to these fluctuations was in the 7th period, while the lowest response was in the 8th period.

Based on the previous results, huge fluctuations occur in response to economic growth, financial development and exchange rate due to oil price fluctuations during the study period. It reflects oil resource mismanagement in addition to rampant corruption in the state administrations apart from allocating most of the financial resources for wars and combatting terrorism.

The results indicate that the significant contribution of oil price fluctuations in explaining the change in per capita GDP, a ratio of broad money supply to GDP, and exchange rate. This applies to all periods except the first period of financial development and second, third and the fourth periods for the exchange rate. Variance of the explanatory error of these fluctuations in the interpretation of the change in financial development is the highest as compared with the same variance in the other variables except the 7th to the 11th periods. The relative contribution of these fluctuations in explaining exchange rate was the highest in these periods. In contrast, variance of explanatory error of economic growth is the highest in the first period as compared with the same variance of the other variables.

These results have indicated the limited role of fiscal policy in influencing economic growth that was partly explained by the lack of sophisticated financial markets in Iraq.

Based on the above results, it is noted that crude oil price is the main engine of financial development, economic growth and the exchange rate. It highlights the importance of the effects of the fluctuations of oil price on economic activities in Iraq. Therefore, it is necessary for the decision makers to adopt a certain strategy based on reducing the dependence of the Iraqi economy on crude oil. Prompting the financial sector throughout the development of a well-regulated economic policy has to be involved in this strategy as well. It could be done institutionally by regulating frameworks to build confidence in the economy and reduce the dominance of the public sector. Moreover, fiscal policy could be used more efficiently to stabilize the Iraqi economy.

## Figure

Oil price in the U.S. dollars per barrel from 1980 to 2020.

The Impulse Response Function (IRF) to fluctuations in oil price.

## Table

Description of the Study Variables

The Results of the ADF Stationary test at levels and first differences.

The Results of the Johansen and Juselius test for Co-integration

Relative importance of oil price fluctuations in the interpretation of study variables.

## REFERENCES

1. Abdalla, S. Z. S. (2013), Modelling the impact of oil price fluctuations on the stock returns in an emerging market: The case of Saudi Arabia, Interdisciplinary, Journal of Research in Business, 2(10), 10-20.
2. Algahtani, G. (2016), The effects of oil price shocks on economic activity in Saudi Arabia: Econometric approach, International Journal of Business and Management, 11(8), 124-133.
3. Ayşen, E. and Mehmet, M. (2014), The effects of oil price on Turkish economic growth, Proceedings of the Proceedings of the 12th International Academic Conference, Selcuk University, Turkey, 360-370.
4. Bernanke, B. S. (2004), Oil and the Economy. Remarks at the Distinguished Series, Darton College, Albany, Georgia.
5. Berument, H. and Ceylan, N. (2004), The impact of oil price shocks on the economic growth of the selected MENA countries, Working Paper, Bilkent University.
6. Berument, H. , Ceylan, N. B. , and Dogan, N. (2010), The impact of oil price shocks on the economic growth of selected MENA1 countries, The Energy Journal, 31(1), 149-176.
7. Burbridge, J. and Harrison, A. (1984), Testing for the effects of oil-price rises using vector autoregressions, International Economic Review, 25(1), 459-484.
8. Dehn, J. (2000), The effects on growth of commodity price uncertainty and shocks, Policy Research Working Paper, World Bank, Development Research Group.
9. Devlin, J. and Lewin, M. (2004), Managing Oil Booms and Busts in Developing Countries, Draft Chapter for Managing Volatility and Crises, A Practitioner’s Guide.
10. Drebee, H. A. and Razak, N. A. A. (2018), Measuring the efficiency of colleges at the University of Al-Qadisiyah-Iraq: A data envelopment analysis approach, Jurnal Ekonomi Malaysia, 52(3), 163-179.
11. Drebee, H. A. , Razak, N. A. A. , and Karim, M. Z. A. (2014), Is there an overlapping market between national car producers in Malaysia? Jurnal Ekonomi Malaysia, 48(1), 75-85.
12. Eltony, M. N. and Al-Awadi, M. (2001), Oil price fluctuations and their impact on the macroeconomic variables of Kuwait: A case study using a VAR model, International Journal of Energy Research, 25(11), 939-959.
13. Engle, R. F. and Yoo, B. S. (1987), Forecasting and testing in co-integrated systems, Journal of Econometrics, 35(1), 143-159.
14. Eryiğit, M. (2012), The dynamical relationship between oil price shocks and selected macroeconomic variables in Turkey, Economic Research-Ekonomska Istraživanja,25(2), 263-276.
15. Farzanegan, M. R. and Markwardt, G. (2009), The effects of oil price shocks on the Iranian economy, Energy Economics, 31(1), 134-151.
16. Ftiti, Z., K , Guesmi, F. T. , Teulon, F. , and Chouachi, S. (2016), Relationship between crude oil prices and economic growth in selected OPEC countries, Journal of Applied Business Research, 32(1), 11-22.
17. Gisser, M. and Goodwin, H. (1986), Crude oil and the macro economy: Tests of some popular notions, Journal of Money, Credit and Banking, 18(1), 95-103.
18. Granger, C. W. and Newbold, P. (1974), Spurious Regression in Econometrics, Journal of Econometrics, 2, 111-120.
19. Greene, W. H. (2000), Econometric analysis (4th Ed.), Upper Saddle River, N J: Prentice Hall.
20. Hamilton, J. D. (1983), Oil and the Macro economy since World War II, Journal of Political Economy, 92(2), 228-248.
21. Hamilton, J. D. (1996), This is what happened to the oil price-macro economy relationship, Journal of Monetary Economics, 38(2), 215-220.
22. Hooker, M. A. (1996), What happened to the oil price-macro economy relationship? Journal of Monetary Economics, 38(2), 195-213.
23. Jiménez-Rodríguez, R. and Sánchez, M. (2004), Oil price shocks and real GDP growth: Empirical evidence for some OECD countries, Applied Economics, 37(2), 201-228.
24. Johansen, S. (1988), Statistical analysis of cointegration vectors, Journal of Economic Dynamics and Control, 12(2-3), 231-254.
25. Johansen, S. and Juselius, K. (1990), Maximum likelihood estimation and inference on cointegration - With application to the demand for money, Oxford Bulletin of Economics and Statistics, 52(2), 169-210.
26. Kouassi, E. , Decaluwe, B. , Kapombe, M. , and Colyer, D. (1997), Temporal causality and the dynamic interactions between terms of trade and current account deficits in co-integrated VAR processes: Further evidence from Ivorian time series, Applied Economics, 31(1), 89-96.
27. Lee, K. , Ni, S. , and Raati, R. A. (1995), Oil shocks and the macro economy: The role of price variability, Energy Journal, 16(4), 39-56.
28. Mork, K. A. (1989), Oil and the macro economy when prices go up and down: An extension of Hamilton’s results, Journal of Political Economy, 97(3), 740-744.
29. Naccache, T. (2010), Slow oil shocks and the “weakening of the oil price-macro economy relationship”, Energy Policy, 38(5), 2340-2345.
30. Naka, A. and Tufte, D. (1997), Examining impulse response functions in cointegrated systems, Applied Economics, 29(12), 1593-1603.
31. Nelson, C. R. and Plosser, C. R. (1982), Trends and random walks in macroeconomic time series. Some evidence and implications, Journal of Monetary Economics, 10(2), 139-162.
32. Olomola, P. A. and Adejumo, A. V. (2006), Oil price shocks and macroeconomic activities in Nigeria, International Research Journal of Finance and Economics, 3, 28-34.
33. Rahma, E. , Perera, N. , and Tan, K. (2016), Oil price shocks and theirs consequences on Sudan GDP growth and unemployment rates, Proceedings of the 21st International Academic Conferences, Miami, 9-2.
34. Ward, B. and Siregar, H. (2001), The role of aggregate demand shocks in explaining Indonesian macroeconomic fluctuations, Commerce Division, Lincoln University, Discussion Paper No. 86.
35. Wheeler, M. (1999), The macroeconomic impacts of government debt: An empirical analysis of the 1980s and 1990s, Atlantic Economic Journal, 27(3), 273-284.
 Do not open for a day Close