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International Journal of Energy Economics and Policy | Vol 9 • Issue 4 • 2019 381

International Journal of Energy Economics and 
Policy

ISSN: 2146-4553

available at http: www.econjournals.com

International Journal of Energy Economics and Policy, 2019, 9(4), 381-387.

The Impact of Oil Factor on Azerbaijan Economy

Sugra Ingilab Humbatova1, Ragif Kh. Gasimov2, Natig Qadim‒Oglu Hajiyev3*

1Department of Economy and Managements, Azerbaijan State University of Economics (UNEC), Istiqlaliyyat Str. 6, Baku 1001, 
Azerbaijan, 2Department of Economy and Business Administration, Azerbaijan State University of Economics (UNEC), Baku 1001, 
Azerbaijan, 3Department of Regulation of Economy, Azerbaijan State University of Economics (UNEC), Istiqlaliyyat Str. 6,  
Baku 1001, Azerbaijan. *Email: n.qadjiev2012@yandex.ru

Received: 07 March 2019 Accepted: 14 May 2019 DOI: https://doi.org/10.32479/ijeep.8001

ABSTRACT

The paper examines the role of oil in the world economy and its impact on Azerbaijan economy. The reciprocal relations between factors in research 
were carried out by differential model of time series and times series have been checked whether they are unit root (Augmented Dickey-Fuller (ADF), 
Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) as a methodology of the research. The research focused on the econometric 
analysis through traditional methods and statistics like EVIEWS 9, GRETL, PASW Statistics. The results confirm that the formation of demand and 
supply didn’t happen in the world market during 15-20 years. The relationships between oil prices and demand and supply prove the novelty of the 
research. The impact of oil price fluctuations on “economic concept” was high in 2007-2009 and 2014-2016. The practical importance of the article 
is the employment of the income ‒ generated from “the contract of the century” ‒ for the human development.

Keywords: Economic Development, Oil Prices, Econometric Analysis, Functional Dependence, Macroeconomic Indicators 
JEL Classifications: E31, F31, Q41

1. INTRODUCTION

World Oil market is still in the centre of attentions in the modern 
world (Bataa et al., 2016; Liu et al., 2015). Thus, it is the energy 
driven factor of world economics, stock market, oil exporting and 
importing countries’ economics, exchange rate and etc. (Baffes, 
2007; Jadidzadeh and Serletis, 2017). Modern oil market is 
featured in developing dynamics and it is related to the increase 
of consumption and world production. During 2005-2015, 
oil consumption and production increased 12.1% and 11.9% 
respectively. Besides, there is unstability in the world oil market. 
This case was observed seriously in 2008 and 2014-2016.

Average oil price was 43.55 dollar/barrel in 2016. This is the 
lower indicator than in 2016. However, it was 38.1 dollar/barrel 
in 2004. Having low prices was related to geopolitical issues. War 
and conflicts in the Near East, economic sanctions to Russia and 

etc. For the purpose of making the balance in the world oil market, 
oil exporters embarked on negotiations to impose a quota on oil 
production in February 2016 and in December, they concluded the 
agreement with “freezing”. During that period, oil price had been 
50 dollars. Oil price had been 53-57 dollar/barrel at the beginning 
of December. Oil market affects on the world economy and other 
energy driven factors. Although it is expected to be reduced in 
the near future, oil governs the world economics and policy now.

2. LITERATURE REVIEW

Ghalayini (2011) researched the fluctuation of oil prices 
and concluded that price shocks affected macroeconomic 
indicators through different channels. Geopolicital doubts and 
certain market dissessions paved the way to mercenaries and 
speculative resources to turn out in the world oil market. In 
turn, it caused the increase of prices for a short period again 

This Journal is licensed under a Creative Commons Attribution 4.0 International License



Humbatova, et al.: The Impact of Oil Factor on Azerbaijan Economy

International Journal of Energy Economics and Policy | Vol 9 • Issue 4 • 2019382

Study Period and Country/Region Methodology Results of the study
Guglielmo et al. (2015) China, 1997M1-2014M2 VAR‒GARCH‒in‒mean 

model, VAR DCC‒GARCH‒
in‒mean model

Exchange rates in the stock market depend on the 
world oil price positively

Siok et al. (2015) China, 1980-2010 ARDL Model World oil prices influence on the main determinants 
of inflation in less dependent sectors of oil – on real 
exchange rates direct, on production expenditures of 
exporters indirect. It is recommended to reduce the 
effects of these shocks through exchange policy

Fatih and Fethi (2016) Turkey, 2004-2014 VAR model Oil prices influence to the production prices of oil 
twice more than retail ones

Seyhun and 
Demezca (2015) 

Turkey, 2001M2-2011M7 Kejriwal ‒ Perron (2009) test 
results, cointegration

Oil prices directly influence on exchange rates

Lanouar, et al. (2018) USA,1947Q2-2016Q4 AR (p)‒GARCH Oil prices impact little on economic development. 
The impact is non‒linear and not constant

Sakib (2014) Bangladesh Real business cycle (RBC) 
model, dynamic stochastic 
general equilibrium (DSGE) 
analysis. Impulse Response 
Functions (IRFs)

Oil prices are not the main factors of business cycle 
of economics

Nooreen et al. (1998) Pakistan, 1998M3-2005M12 GARCH, Granger causality 
test

There is no strong relations between oil prices and 
stock market. The reason for this is the utilisation of 
gas beside oil

Umut et al. (2013) Turkey, 1991M1-2010M2 Fully modified OLS (FM‒
OLS), dynamic OLS (DOLS) 
cointegration

There is a long‒term and direct relationship among 
exchange rates and oil prices as well as exchange 
rates of securities (stock market)

Osigwe and 
Arawomo (2015)

Nigeria The Granger causality test 
within an ECM framework 
was used to estimate the 
inter‒linkage among the 
variables

There is no relations between kerosene prices and 
consumption and economic development. There is 
a positive relation between energy consumption and 
economic development

Foudeh (2017) Kingdoms of Saudi Arabia, 
1995Q4-2015Q4

ARDL model Oil prices impact on GDP positively. Trade balance, 
budget, internal economics, gold‒currency reserves, 
foreign investment are directly related to oil prices. 
It directly impacts on the main determinant of 
economics – government expenditures

Theodosios and 
Dagoumas (2017)

Russian, 1995-2014 Two vector 
autoregressions (VARs) and 
VECM

Macroeconomic indicators such as ‒ industry 
production index, unemployment, GDP, government 
expenditures depend on oil factors like oil price and 
oil production but there is no evidence for Dutch 
disease

Elsiddig et al. (2016) Sudan, 2000Q1-2011Q2. ARDL, VAR model, Granger 
causality test

There is an adverse relation between oil prices 
and macroeconomic indicators of developed 
countries. However, it affects underdeveloped 
countries like Sudan directly. The reduction of 
oil prices makes the oil prices down, cut down 
current prices and government budget. But the 
increase of oil price doesn’t impact on budget 
resurgence (Granger causality). Oil prices affect the 
budget asymmetrically

(ADB, 2004). Another economists Hamilton (1983) and Bruno 
and Sachs (1982), who researched the fluctuation of oil prices, 
explained the impact of oil prices on economic development, 
unstability of financial growth and inflation in 1950-1979 in the 
United Kingdom. They came to a conclusion that variables were 
closely connected to each other. Thus, fluctuations affect large 
economies unconstructively. The increase of oil prices causes 
the increase of prices in economy and reduces employment and 
productivity (Dornbusch et al., 2001).

Besides, it has been concluded that oil price interconnects with 
the legislation of local authorities (Siddiqui. 2005). The increase 
of oil prices causes the inflation to go up and to reduce the profit 
generated from products and services and weakens economic 

development. Every government faces this problem when they 
want to increase oil prices (Nooreen et al., 2007).

Oil prices have a huge influence on the world economy but it 
is hard to determine because they are different for each country 
(Arezki and Blanchard, 2015, Barsky and Kilian, 2004). Michael 
and Menzie (2004) has concluded that impact of energy resources 
on economy is completely different from other resources. He 
claimed that inflation is directly influenced by oil prices. Besides, 
Turkish scholars Hakan and Taşçı (2002), Aydoğuş (1993), Olgun 
(1982) researched the influence of oil prices on Turkey, inflation 
and economic development. They concluded that salary and other 
factors such as profit, interest rate and rent must be regulated on 
the basis of oil prices and level of current prices.



Humbatova, et al.: The Impact of Oil Factor on Azerbaijan Economy

International Journal of Energy Economics and Policy | Vol 9 • Issue 4 • 2019 383

Study Period and Country/Region Methodology Results of the study
Aynur (2016) ОESD,1995-2013. Panel VECM, Granger 

causality tests, cointegration
There is cointegration among economic 
development, energy consumption, employment 
and capital. There is relations between economic 
development and energy consumption in a short 
period (VECM). However, there is no relations 
between economic development and energy 
consumption in a long term

Nasser et al. (2016) Oman 1980-2012 Simple macroeconomic 
model, Regression analysis

Oil sector influences on GDP and all sectors of 
economy positively. The strongest impact happens 
in gas sector but the weakest in agrarian sector. 
Economics is far away comprehensive development

Yusoff and 
Bekhet (2016)

Malaysian 1990-2013 Furthermore, on the basis 
of the standardised CGE 
model developed by Lofgren 
et al. (2002), an energy 
subsidies CGE (ESCGE) 
model was established. Model 
of the general balance (MGB) 
constant elasticity of 
substitution (CES)

Fuel and tax subsidies have a strong influence on 
energy consumption structure. Reduction in fuel and 
tax subsidies will reduce energy consumption and 
will stimulate alternative energy sources. This will 
cause the reduction of budget shortcoming and will 
increase GDP

Aziz and 
Dahalan (2015) 

ASEAN‒5 Malaysia 
Indonesia and Singapore 
Thailand and Philippines. 
1991-2012

Panel regressions analysis Oil price fluctuations impact on RSCA negatively in 
all technological processes 

Bass (2019) Russian, 2010-2017 VECM, Granger causality 
tests, cointegration

The influence of world oil price fluctuations on 
inflation and exchange rate in Russia has been 
examined. World oil price, inflation and exchange 
rate is in the cointegration. Oil price and exchange 
rate are one of the factors that cause inflation

Mourad and 
Ben-Salha (2019)

WORLD, 1990M1-2017M11 Linear and Nonlinear ARDL 
modeling

Oil price changes directly affect food prices in 
global scale

Kilian (2009) USA 1973M1-2007M12 The structural VAR model Cause effect reactions between macroeconomic 
indicators and oil price has been changed. Different 
reasons of oil price increase affect differently to 
economics

Ghalayini (2011) OPEC, G‒7, Russian, India 
and China, 2000Q1-2010Q1

Granger Causality tests The increase of oil price for oil exporting countries 
doesn’t influence on their economic development. 
However, the dependence of G‒7 countries on oil 
reduce their GDP a bit

Hamilton (1983) USA 1947-1981 Granger Causality tests The correlation between oil price and produced 
products is only statically compliance. There is no 
systematics case

Bruno and Sachs (1982) United Kingdom 1950-1979 Macroeconomics analysis Oil price shocks directly affect to production and its 
price and effectivity in a long term

Hakan and Taşçı (2002) Turkey1990 Input–output analysis If Salary, rent, profit are stable, the oil price increase 
cause inflation not much. However, oil price 
becomes string if it changes according to oil price 
and even causes hyperinflation

Mathew and 
Ngalawa (2017)

1980M1-2015M4 PSVAR Oil price shocks directly affect African oil exporting 
countries economy via currency‒credit system, 
unemployment, exchange rate and etc., It stimulates 
business. There is a correlation between GDP and 
oil price

Hamilton (2005) USA 1949Q2-1980Q4 OLS regression After second world war, 9/10 downsizing had been 
prior to oil prices in the USA

Kilian (2009) USA 1947-2005 The fitted value of the linear 
ordinary least squares (OLS) 
regressions

Oil shock impact is asymmetric

Michael and 
Menzie (2004) 

USA, United Kingdom, 
France, Germany and Japan. 
1980-2005

ARDL, Fillips curve European countries and Japan have been affected more 
than the USA by oil shocks since they are dependent 
on oil prices. Generally, it wasn’t strong impact

Hamilton (2010) USA 1949Q2-2001Q3 OLS to estimate the 
forecasting regression, 
impulse‒response functions.

This paper reviews some of the literature on the 
macroeconomic effects of oil price shocks with a 
particular focus on possible nonlinearities in the 
relation and recent new results obtained by Kilian 
and Vigfusson (2009)



Humbatova, et al.: The Impact of Oil Factor on Azerbaijan Economy

International Journal of Energy Economics and Policy | Vol 9 • Issue 4 • 2019384

3. MATERIALS AND METHODS

In the research, world GDP, world industry production, daily oil 
production and including oil price have been generated by internet 
resources. Azerbaijan macroeconomic indicators have been taken 
from Azerbaijan State Statistics Committee.

For econometric analysis, we have used simple and complex 
regressions:

  ŷ a bx   (1)

Simple regression function includes:

  0,7<|rxy|≤1,n≥6 (2)

  1 1 2 2ˆ m my a b x b x b x     (3)

Complex regression function includes:

 

 0, 3 1; , ,
, , 1, ; 6

k k l k lx y x x x y x y
r r min r r

k l k l m n m

  

    


 (4)

Simple linear regression function is used in terms of the simplicity 
of economic significance of the model.

So, the more the regression function is complex, the more complex 
is the parameter.

In case of the lack of information, having more regression 
parameters is statistically important or causes the low quality of 
the model by criteria. We have established models by using special 
econometric computer programs.

The smallest square method has been used for the calculation 
of linear regression function parameters. It is required to choose 
regression parameters carefully:

 

   
2

1 2

2

1 1

2 2

, , , ˆ, m i i
i

i i

i i m im

S a b b b y y

y a b x
min

b x b x

 

  
      









 (5)

The following parameters are required for the quality of regression 
model:
1. General importance is verified: F≥Fa;m,n–m–1
2. The importance of regression function parameters is verified: 

|t|≥t1–a;n–m–1)
3. The verification of the smallest square method.

However, since time series are mostly non‒stationary the 
employment of the ordinary least squares (OLS) method might 
cause fake linear dependency among variables. The probability 
is high among our time series, that’s why we can only mention 
three of them:
1. Let’s insert the lag order inputs (indicators) on the right side 

of yt=α+βxt equation.
  yt=α+βxt+γyt–1+δxt–1+ut (6)

Here, ut – is stasionary series whilext– is an exogen indicator.

We can establish this equation in 2 forms:
a. yt=α+γyt–1+β∆xt+(β+δ)xt–1+ut (7)

b. yt=α+γyt–1+(β+δ)xt–δ∆xt+ut (8)

In both cases, integrated yt~I(1) stands here. β zero – on the right 
side of the equation is the coefficient of ∆xt stationary variable. 
yt–1, xt–1~I(1), ut– is stationary series. Sims et al., (1990) indicated 
in his article that the employment of the ordinary least squares 
method is important for the coefficient of the equation. β is 
normal unless it is asympotic. The usual t– statisticts possesses 
asymptotic normal distribution N(0,1) in order to check H0:β=0 
hypoteses. Analogically, δ on the right side of the equation is the 
coefficient of ∆xt stationary variable yt–1, xt–1~I(1), ut– is stasionary 
series. That’s why δ is normal unless it is asympotic. The usual 
t– statisticts possesses asymptotic normal distribution N(0,1) in 
order to check H0:β=0 hypoteses.

2.  Prior to model assessment, lets differentiate series, in other 
word, lets analyse the model in series difference.

  ∆yt = α+β∆xt+ut (9)

In that case, we can see that in equation ut– is stasionary series. 
In this model, the assessment of the ordinary least squares for 
either α or β is normal unless it is asymptotic. If ut– white noise, 

Study Period and Country/Region Methodology Results of the study
Apergis and 
Miller (2009)

USA Austria, Canada, 
France, Germany, Italy, 
Japan, England

SVAR Oil price fluctuations don’t impact on stock market 
significantly

Basher and 
Sadorsky (2006) 

BRIC (Russian, Brazil, India, 
China)

CAPM Russian and Brazilian stock market is specifically 
active. India and China are witnessed the adverse 
effect. It is related with their strong influence to 
international economics

Filis et al. (2011) Canada, Mexico, Brazil, 
USA, Germany, Niderland 
1987M1-2009M12

They have revealed direct relationship in all cases. 
Besides, it has been determined that the correlation 
is increasing related to the market of developed 
countries

Li et al. (2012) China 2001M1-2005M10, 
2005M11-2007M06

Cointegration and causal 
analysis

The dependency between oil price and stock market 
was determined as direct and straight

GDP: Gross domestic product



Humbatova, et al.: The Impact of Oil Factor on Azerbaijan Economy

International Journal of Energy Economics and Policy | Vol 9 • Issue 4 • 2019 385

then both t– statisticts possess asymptotic normal distribution 
N(0,1).

3. Using autocorrelation regression model for assessment.
2

1,  , ~ . . . 0 )( ,t t t t t t ty x u u x u i i d N           (10)

In case of fake regression ˆ 1   (on probabbility). That’s why, 
in case T is higher, this method equals to primary differentiation 
method of series. We will employ the second method ‒ primary 
differentiation method of series.

4. EMPIRICAL RESULTS AND DISCUSSION

First of all, the stationary of time series has been checked and 
tested though commonly‒accepted three tests (ADF ‒ Augmented 
Dickey‒Fuller, PP ‒ Phillips‒Perron and KPSS ‒ Kwiatkowski‒
Phillips‒Schmidt‒Shin). Tests have been done through EVIEWS 
9 econometric program (Table 1).

Abbreviations
WGDP World gross domestic product, 

dollar
mln. dollar

WIP Industrial production, dollar mln. dollar
WPP World production, barrel mln.barrel per a day
WCP World consumption, barrel mln.barrel per a day
PB Oil prices $/Barrel
AZGDP Azerbaijan gross domestic 

product, manat
mln. manat

AZIFC Azerbaijan investment on fixed 
capital, manat

mln. manat

AZOP Azerbaijan oil production, ton mln. ton
AZETT Azerbaijan external a trade 

turnover, dollar
mln. dollar

AZIM Azerbaijan, import, dollar mln. dollar
AZEX Azerbaijan, export, dollar mln. dollar

ADF reveals that world GDP, world industry production, oil 
prices, world oil production (supply) and world oil consumption 
(demand) are in the 1st difference and stationary in three cases 
(constant; constant and linear trend; none). Only world GDP in 

the 1st difference is not stationary in one case (none). This result 
is suitable for the method. The results of PP is similar to ADF test, 
but is unclear a bit. Thus, world GDP and oil process are stationary 
(none) both in 1st difference and in simple case. KPSS test is also 
unclear. The above‒mentioned facts might be referred to time 
series tests of Azerbaijan macroeconomic indicators.

The coefficients of only two of the models (models 1 and 2) 
that reflect the impact of World GDP, world industry production, 
daily oil production (supply) and oil consumption (demand) on 
oil price are statistically significant (Table 2). In other words, 
world GDP and world industry production influence on world oil 
prices. It can be inferred that model 3 and 4 has no any impact 
of world daily oil production (supply) and oil consumption 
(demand) on world oil prices. Thus, the coefficients are not 
statistically significance. According to the Breusch‒Godfrey 
Serial Correlation LM Test, autocorrelation in models doesn’t 
exist. Autocorrelation exists only in model 3. So, we can infer 
that although world industry production plays a certain role in 
world oil price fluctuation and world GDP, daily oil production 
(supply) and oil consumption (demand) don’t impact on world 
oil price. As mentioned in the beginning of the research, 
non‒economic factors play a role in oil price fluctuations 
(up and down).

The models (model 5-8) reflecting the dependency of investment 
on fixed assets on oil price and oil production in Azerbaijan 
happens the adverse process (Table 3). So, model 5 and 7 either 
constant or oil price coefficient is statically significance. Generally, 
model is significant and adequate. However, model 6 and 8 (models 
that reflect the dependency of oil price on Azerbaijan GDP and 
investment on fixed capital) are not statistically significance 
(reflecting oil production coefficient in Azerbaijan) and generally, 
models are not adequate. It gives an evidence once more that 
Azerbaijan’s GDP and investment on fixed capital depends 
entirely on the oil price and does not depend on the volume of oil 
production in Azerbaijan (mainly in the short‒term). According to 
the Breusch‒Godfrey Serial Correlation LM Test, autocorrelation 
has been active in model 5.

Table 1: The unit root test results (1st difference)
Variables Constant Constant and trend None

ADF PP KPSS ADF PP KPSS ADF PP
PB ‒4.10*** ‒4.10*** 0.23 ‒4.33** ‒4.50** 0.16** ‒4.16*** ‒4.16***
WGDP ‒3.71** ‒3.67** 0.13 ‒3.32* ‒3.59* 0.12* ‒0.75 ‒2.423**
WIP ‒3.76** ‒3.73** 0.15 ‒3.71** ‒3.67* 0.12* ‒3.12*** ‒3.12***
WCP ‒3.95*** ‒4.00*** 0.12 ‒3.82** ‒3.83** 0.11 ‒2.35** ‒2.30**
WPP ‒5.33*** ‒5.58** 0.09 ‒5.15*** ‒5.49*** 0.09 ‒3.05*** ‒3.05***
PB ‒3.52** ‒3.48** 0.17 ‒3.64* ‒4.24** 0.17 ‒3.59*** ‒3.56***
AZGDP ‒3.33** ‒3.26** 0.12 ‒3.22 ‒3.09 0.11 ‒2.08** ‒1.97**
AZIFC ‒3.61** ‒3.59** 0.11 ‒3.53* ‒3.51* 0.10 ‒2.63** ‒2.63**
AZOP ‒1.77 ‒1.76 0.25 ‒2.08 ‒2.06 0.09 ‒1.77* ‒1.77*
AZETT ‒6.47*** ‒6.78*** 0.19 ‒6.51*** ‒7.29*** 0.23*** ‒6.59*** ‒6.85***
AZEX ‒6.69*** ‒7.02*** 0.16 ‒6.71*** ‒7.70*** 0.09 ‒6.88*** ‒7.23***
AZIM ‒4.48*** ‒4.48*** 0.16 ‒4.67*** ‒4.69* 0.11 ‒3.83*** ‒3.86***
ADF denotes the Augmented Dickey‒Fuller single root system respectively. The maximum lag order is 3. The optimum lag order is selected based on the Shwarz criterion automatically; 
***, ** and *indicate rejection of the null hypotheses at the 1%, 5% and 10% significance levels respectively. The critical values are taken from MacKinnon (Mackinnon, 1996). 
PP Phillips‒Perron is single root system. The optimum lag order in PP test is selected based on the Newey‒West criterion automatically; ***, ** and *indicate rejection of the null 
hypotheses at the 1%, 5% and 10% significance levels respectively. The critical values are taken from MacKinnon (Mackinnon, 1996). KPSS denotes Kwiatkowski‒Phillips‒Schmidt‒
Shin (Kwiatkowski et al., 1992) single root system. The optimum lag order in KPSS test is selected based on the Newey‒West criterion automatically; ***, ** and *indicate rejection of 
the null hypotheses at the 1%, 5% and 10% significance levels respectively. The critical values are taken from Kwiatkowski‒Phillips‒Schmidt‒Shin [90]. Assessment period: 1999-2017*



Humbatova, et al.: The Impact of Oil Factor on Azerbaijan Economy

International Journal of Energy Economics and Policy | Vol 9 • Issue 4 • 2019386

The macroeconomic indicators of the Azerbaijani manat and the 
models (models 9, 11 and 13) expressed in figures from the models 
reflecting the influence of oil prices on macroeconomic indicators 
in Azerbaijan (model 9-14) are statistically significant and the 
models are adequate. However, these indicators are expressed in 
models that are dependent on oil prices (models 9,11 and 13) but 
macroeconomic indicators are statistically significant, and the 
constants are negligible. Thus, the results of these models (model 
9-14) once again prove that the relationship between oil prices and 
many macroeconomic indicators is different in oil exporting and 
oil importing countries.

5. CONCLUSION

The reasoning of models either economic or mathematical 
point of view can closely be related to the relative proximity 
of the economic growth rate with oil production and price rate. 
Unlike world economic situation, as noted above, there is no 
absolute dependency close to between world oil production and 
consumption as well as the relative dependency among world 
oil production, consumption and world GDP and in general, 
dependency between oil price and these factors (world oil 

production, consumption and world GDP), especially in the last 
decade. That’s why our Azerbaijan also witnesses the reverse 
processes. Although economic growth and demand act as an 
important factor in the world oil price, it can be inferred that 
the economic growth observed in Azerbaijan, one of the world’s 
smallest exporter of oil production in the world, is largely 
dependent on oil production and oil prices.

REFERENCES

ADB. (2004), Asian Development Outlook 2004. Philippines: Asian 
Development Bank.

Apergis, N., Miller, M.M. (2009), Do structural oil market shocks affect 
stock prices? Energy Economics, 31(4), 569-575.

Arezki, R., Blanchard, O. (2015), The Oil Price Slump: Seven Key 
Questions. CEPR Discussion Paper, VOX CEPR Policy Portal.

Aydoğuş, O. (1993), Cost Price Relationship, Price Settings in Sectors 
and Inflation in Turkish Economy. 3rd Izmir Economic Conference, 
3, 4-7 June 1992. Ankara: SPO.

Aynur, P. (2016), Which energy growth hypothesis is valid in OECD 
countries? Evidence from panel granger causality. International 
Journal of Energy Economics and Policy, 6(1), 28-34.

Aziz, M.I.A., Dahalan, J. (2015), The impacts of oil price fluctuations on 

Table 2: The dependency of oil price on the world GDP, industry production, demand and supply for oil
Variables Model 1 Model 2 Model 3 Model 4

∆PB ∆PB ∆PB ∆PB
∆WGP 0.005***
∆WCP 3.389
∆WIP 0.016***
∆WPP 1.292
C ‒10.949*** ‒6.10*** ‒1.302 1.028
R2 0.776 0.847 0.033 0.009
Adj. R2 0.763 0.837 ‒0.023 ‒0.049
F‒st. 59.002 93.717 0.592 0.158
Pr.(F‒st.) 0.000001 0.000000 0.452 0.696
D ‒W st. 1.633 1.999 1.992 2.038
Breusch‒Godfrey serial correlation LM test
F‒st. 0.229 0.099 0.043 0.084
Obs* R2 0.555 0.247 0.110 0.211
Pr. F (2,15) 0.800 0.906 0.957 0.919
Pr. Chi-square (2) 0.757 0.883 0.946 0.899
*P<0.05; **P<0,01; ***P<0.001 GDP: Gross domestic product

Table 3: The influence of oil price and oil production on GDP and investment on fixed capital
Variables Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model 11 Model 12 Model 13 Model

∆AZGDP ∆AZGDP ∆AZIFC ∆AZIFC ∆AZIM ∆AZIM ∆AZEX ∆AZEX ∆AZETT ∆AZETT
∆PB 203.78*** 55.95*** 35.67** 563.02*** 598.67***
∆AZOP 0.03 ‒0.10 ‒0.03 ‒0.47 ‒0.50
C 3399.77*** 3808.22** 845.30*** 1114.83 350.87 477.60 ‒538.58 1375.83 ‒187.69 1848.57
R2 0.77 0.0006 0.62 0.09 0.42 0.01 0.59 0.02 0.61 0.02
Adj. R2 0.69 ‒0.07 0.59 0.03 0.39 ‒0.05 0.56 ‒0.05 0.59 ‒0.03
F‒st. 37.77 0.009 24.08 1.55 12.03 0.25 22.51 0.34 24.93 0.35
Pr.(F‒st.) 0.00001 0.92 0.0001 0.23 0.003 0.61 0.0002 0.56 0.0001 0.55
D ‒W st. 0.69 1.77 1.41 1.77 3.22 2.15 2.66 2.80 2.69 2.77
Breusch‒Godfrey serial correlation LM test
F‒st. 8.97 0.69 0.77 0.25 4.97 0.25 1.36 1.50 1.52 1.25
Obs* R2 9.86 1.59 1.77 0.61 7.47 0.63 2.92 3.17 3.22 2.77
Pr. F (2,15) 0.003 0.52 0.49 0.77 0.02 0.77 0.29 0.25 0.29 0.31
Pr. 
Chi-square (2)

0.007 0.45 0.42 0.77 0.02 0.77 0.231 0.20 0.19 0.25

*P<0.05; **P<0,01; ***P<0.001. GDP: Gross domestic product



Humbatova, et al.: The Impact of Oil Factor on Azerbaijan Economy

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