Statistical Analysis of Oil Exploration and Macroeconomic Factors in Saudi Arabia

This article presents a statistical analysis of the relationship between oil exploration and macroeconomic factors in Saudi Arabia. The article explores the causality, stationarity, co-integration, VECM, and VAR models of the five factors of interest. The article also presents hypotheses and assumptions made during the data analysis. The data includes unemployment data, yearly oil volume exports, Saudi Arabian GDP score and growth, internal and external oil price. The article concludes with the results of the statistical tests and distribution statistics.

Methodology
In order to answer our statistical questions, we collected a number of data-sets from
secondary sources. We will therefore be able to test our statistical hypothesis
following our data analysis.
For each of the five factors of interest, we will conduct:
i. Causality tests (Granger causality test)
To determine whether oil exploration causes other macroeconomic factors and vice
verse.
During the season, the average annual output of oil is almost 80 million barrels, which
is 18% in the first phase and during the third period, the volume is about 20%
compared to the second phase. However, during the fourth phase it is reduced by 27%
compared to the third period. The slump is due to the fact that economy has been
shown to reduce the imbalance in the economy, which has had a great effect on gross
domestic product, which has focused almost 14 percent in the fourth quarter.
Although gross domestic product has grown from $ 32.94 billion. The average dollar
for almost $ 40.77 billion. The US dollar in most of the three quarters was almost
25% more than the first time.
ii. Stationarity tests (Unit-root tests)
To test the nature of the macroeconomic factors
iii. Co-integration tests
To test for a long-run equilibrium relation between Oil exploration and other
macroeconomic variables such as economy growth, I.e. does Oil mining and export
affect the GDP in the long run, say 20 years?

iv. Vector Error Correction Model (VECM)
To determine the relationship between Oil exploration and other macroeconomic
variables
v. VAR Model
For macroeconomic data description and performing of causal inference between oil
exploration and other macroeconomic factors.
We will then analyze the test statistics and draw conclusions that will be used to test
our hypotheses.
Data
Based on the above fact, an indication of an oil-dependent economy, said that the
characteristics of choosing a GDP per capita and government revenues would cost the
total value of exports and imports closely linked to changes in oil and oil prices.
Between 2008 and 2019, the average of the relationship between the benchmark
development index of oil prices and economic development outlook was 0.8
Our data includes: Unemployment data from Saudi Arabia, Yearly Oil volume
exports, Saudi Arabian GDP score and growth, internal and external oil price
Economic development depends heavily on the final export value. The cost of
creating the index has identified the relationship between changes in the value of oil
exports on the one hand, and changes in the value of gross domestic product, which is
gross domestic product by government revenues and exports, have reached a gain of
about 0.9,

Hypotheses:
Unemployment rate against Oil exploration
H0: Oil exploration has no effect on unemployment rates in Saudi Arabia
H1: Oil exploration has a significant effect on unemployment rates in Saudi Arabia
H1 There is an important link between oil exports and economic growth in the
economy.
H2: The economy is heavily dependent on oil exports for most of its revenue. H3:
Any increase in oil prices has always led to a positive growth in the economy.
H4: Global crude oil prices have a direct impact on GDP. H5: Oil investment is
promised in terms of oil revenues.
H6: Oil and gas trade is affected by oil revenues.
H7: Increasing oil exports will lead to increased government budgets. This will lead to
increased spending and spending in many sectors such as the social economy
GDP and Oil exploration
H0: Oil exploration has led to growth of the Saudi Arabian GDP
H1: The Saudi Arabian GDP growth has not been affected by the oil exploration in the
country
Effect of Oil exploration on the Stock Market
H0: Oil exportation from Saudi Arabia has significant effect on the stock market
H1: Oil exportation has no significant effect on the stock market
Oil prices in Saudi Arabia and the Oil production in Saudi Arabia

H0: The oil Industry prices have considerable influence on the Saudi Arabian oil
mining and exportation
H1: The oil Industry prices do not have influence over oil exploration and exportation
activities in Saudi Arabia
Oil exploration and Inflation
H0: Oil production and exportation in Saudi Arabia has significant influence on
inflation
H1: Oil production and exportation in Saudi Arabia has no significant effect on
inflation rates in Saudi Arabia
Additionally, we will explore the distribution of the five macroeconomic factors from
the previous years to 2017. Therefore, apart from the aforementioned statistical tests,
we will explore the distribution statistics of the macroeconomic factors in the Saudi
Arabian economy.
Assumptions
During our data analysis, we make several assumptions. Which include:
i. Oil exports from Saudi Arabia are the major foreign exchange earner for the
country
ii. Oil exploration has a direct effect ona number of macro-economic factors such as
employment rate, inflation rates, and the GDP growth of Saudi Arabia.
Additionally we assume that Oil exploration is affected by macro-economic
factors such as the price of Oil in the Oil mining industry
iii. The sample size period is representative of the whole population

Results
Distribution statistics
Unemployment rate
Variable Obs Mean Std. Dev. Min Max
year 19 2008 5.627314 1999 2017
Unemployment rate 19 5.488495 .5076645 4.3451 6.254306
The average unemployment rate of Saudi Arabia from 1999 to 2017 is 5.4495
Stand. Dev. Of unemployment rate = 0.570496
Graph for distribution of unemployment in Saudi Arabia

4.5 5 5.5 6 6.5
Unemployment
2000 2005 2010 2015 2020
Year
Another format maybe?

GDP growth rate owing income from Oil production in Saudi Arabia
Variable Obs Mean Std. Dev. Min Max
Year 20 2009.5 5.91608 2000 2019
Saudi Arabia GDP
growth
20 2.00288 6.362119 -9.5449 17.409

-10 0 10 20
GDP growth
2000 2005 2010 2015 2020
Year
Gross Domestic Product growth trend
Saudi Arabia Oil export Volume
Variable Obs Mean Std. Dev. Min Max
year 20 2009 5.91608 2000 2019
Oil export 20 9129390 915089.5 7090000 1.05e+07

7000000 8000000 9000000 10000000 11000000
Export volume
2000 2005 2010 2015 2020
Year
Saudi Arabia Oil exports
Average oil prices
Variable
Ob
s
Mean Std. Dev. Min Max
year 12 2013.5 3.605551 2008 2019
priceperba
~l
12
80.7166
4
17.37719
37.611
4
106.2002

40 60 80 100 120
Price
2005 2010 2015 2020
Year
price per barrel
Average oil prices
Distribution of oil exports for a given price
Variable Obs Mean Std. Dev. Min Max
year 12 2013.5 3.605551 2008 2019
priceperba~l 12 80.71664 17.37719 37.6114 106.2002
exports 12 9568983 744367.9 8170000 1.05e+07
Mean of export = 9312546
Std. Dev. Of exports = 750876.5
Max. Price per barrel = 101.12

80000008500000900000095000001000000010500000
Exports
40 60 80 100 120
Price per barrel
Exports
Effect of Oil industry prices on Oil exploration in Saudi Arabia
Variable
Ob
s
Mean Std. Dev. Min Max
year 12 2013.5 3.605551 2008 2019
Price per barrel 12
80.7166
4
17.37719
37.611
4
106.2002
exports 12
956898
3
744367.9
81700
00
1.05e+07
Industry price 12
55.8488
8
7.612934
48.862
2
72.1502

80000008500000900000095000001000000010500000Exports
50 55 60 65 70
Industry price
Exports
Relationship between Industry oil prices and Oil exportation from Saudi Arabia
??????

The economy is mainly supported by the leasing of oil and depending on the
availability of crude oil, Cambodia has the capacity to maintain GDP growth,
government spending and productivity growth.
Industrial Production
The tangible link between oil between hiring and economic development is the reason
for the need for an initiative to maintain a stable economic policy framework.
Statistical tests
Vector Auto Regression Model
i. Exports and Oil Industry prices
Government revenues have shown a strong fluctuation of the price of oil and the
value of the export contract. Percentage of fluctuations to percent change in oil
price per barrel or oil price result reached 0.98 or 0.96. Government spending is
largely dependent on the change in the export's crude oil price (the price of a
0.93% elasticity is roughly), while a significant decline is due to a change in the
treaty (0.77%).

_cons 33.02225 27.17587 1.22 0.224 -20.24147 86.28598
L2. -.6159164 .2956203 -2.08 0.037 -1.195321 -.0365113
L1. .7286996 .2637373 2.76 0.006 .211784 1.245615
industryprice
L2. -2.54e-07 3.64e-06 -0.07 0.944 -7.39e-06 6.88e-06
L1. 2.04e-06 3.94e-06 0.52 0.604 -5.68e-06 9.77e-06
exports
industryprice
_cons 3308170 1227536 2.69 0.007 902244.4 5714096
L2. -7728.261 13353.19 -0.58 0.563 -33900.02 18443.5
L1. 18925.81 11913.03 1.59 0.112 -4423.299 42274.93
industryprice
L2. -.3129358 .1643719 -1.90 0.057 -.6350989 .0092273
L1. .9178522 .1780614 5.15 0.000 .5688583 1.266846
exports
exports
Coef. Std. Err. z P>|z| [95% Conf. Interval]
industryprice 5 8.04095 0.4602 8.52526 0.0741
exports 5 363210 0.8227 46.40388 0.0000
Equation Parms RMSE R-sq chi2 P>chi2
Det(Sigma_ml) = 2.05e+12 SBIC = 36.32866
FPE = 1.85e+13 HQIC = 35.69414
Log likelihood = -170.1304 AIC = 36.02607
Sample: 2010 - 2019 No. of obs = 10
Vector autoregression
-200000
0
200000
400000
-200000
0
200000
400000
0 2 4 6 8 0 2 4 6 8
varbasic, exports, exports varbasic, exports, industryprice
varbasic, industryprice, exports varbasic, industryprice, industryprice
95% CI orthogonalized irf
step
Graphs by irfname, impulse variable, and response variable

ii. Unemployment and Oil exports
_cons -249528.5 1941302 -0.13 0.898 -4054411 3555354
L2. -.0776181 .2177003 -0.36 0.721 -.5043028 .3490666
L1. .77372 .2327377 3.32 0.001 .3175624 1.229878
oilexport
L2. -700400.3 468356.4 -1.50 0.135 -1618362 217561.5
L1. 1248114 471966.5 2.64 0.008 323076.6 2173151
unemploymentrate
oilexport
_cons 3.437823 .7114599 4.83 0.000 2.043388 4.832259
L2. -8.03e-08 7.98e-08 -1.01 0.314 -2.37e-07 7.61e-08
L1. 1.82e-07 8.53e-08 2.13 0.033 1.49e-08 3.49e-07
oilexport
L2. -.5304352 .171646 -3.09 0.002 -.8668552 -.1940152
L1. .7569482 .172969 4.38 0.000 .4179351 1.095961
unemploymentrate
unemploymentrate
Coef. Std. Err. z P>|z| [95% Conf. Interval]
oilexport 5 560056 0.6975 36.88992 0.0000
unemploymentrate 5 .205253 0.6111 25.14439 0.0000
Equation Parms RMSE R-sq chi2 P>chi2
Det(Sigma_ml) = 5.75e+09 SBIC = 29.88138
FPE = 2.10e+10 HQIC = 29.42324
Log likelihood = -225.1881 AIC = 29.39852
Sample: 2002 - 2017 No. of obs = 16
Vector autoregression

-500000
0
500000
-500000
0
500000
0 5 0 5
varbasic, oilexport, oilexport varbasic, oilexport, unemploymentrate
varbasic, unemploymentrate, oilexport varbasic, unemploymentrate, unemploymentrate
95% CI orthogonalized irf
step
Graphs by irfname, impulse variable, and response variable

Vector Error Correction model
Unemployment and Oil exports
_cons -4.33432 . . . . .
oilexport -1.19e-07 8.99e-08 -1.32 0.187 -2.95e-07 5.75e-08
unemploymentrate 1 . . . . .
_ce1
beta Coef. Std. Err. z P>|z| [95% Conf. Interval]
Johansen normalization restriction imposed
Identification: beta is exactly identified
_ce1 1 1.744961 0.1865
Equation Parms chi2 P>chi2
Cointegrating equations
_cons 1.99e-07 164848.8 0.00 1.000 -323097.8 323097.8
LD. -.0460953 .2419138 -0.19 0.849 -.5202376 .4280471
oilexport
LD. 1015466 498373.6 2.04 0.042 38671.41 1992260
unemploymentrate
L1. 363097 457825.6 0.79 0.428 -534224.7 1260419
_ce1
D_oilexport
_cons .1732813 .0571874 3.03 0.002 .061196 .2853666
LD. 8.54e-08 8.39e-08 1.02 0.309 -7.91e-08 2.50e-07
oilexport
LD. .5173846 .1728899 2.99 0.003 .1785265 .8562426
unemploymentrate
L1. -.7658398 .1588235 -4.82 0.000 -1.077128 -.4545515
_ce1
D_unemploymentrate
Coef. Std. Err. z P>|z| [95% Conf. Interval]
D_oilexport 4 566900 0.3791 7.325934 0.1196
D_unemployment~e 4 .196662 0.7051 28.6881 0.0000
Equation Parms RMSE R-sq chi2 P>chi2
Det(Sigma_ml) = 6.45e+09 SBIC = 29.82263
Log likelihood = -226.1044 HQIC = 29.4103
AIC = 29.38805
Sample: 2002 - 2017 No. of obs = 16
Vector error-correction model

APPENDIX
This is a high reliance on economy. The crude oil export value, especially from the
final export price, is well monitored by measuring the variability of the observed
trends in exchange for the value of the attacked oil exports or the exchange rate of the
contract for exports related to oil exports. In total, the percentage change in the value
of a barrel of oil, respectively, the export value of output will shift the contract of
change in gross domestic product of 0.75% and 0.73%. Changes in GDP per capita to
changes in the development of oil prices and contract prices, which are not as high as
the development of GDP, but not significant (0.663% and 0.669% as compared to the
percentage change in oil prices and oil prices). This shows a high degree of
correlation between earnings on the one hand and the development of oil on the other.