relationship between price and rate of production of crude
TRANSCRIPT
International Journal of Scientific & Engineering Research Volume 11, Issue 3, March-2020 322 ISSN 2229-5518
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Relationship Between Price and Rate of Production of Crude Oil in Nigeria.
Ogunyemi Ephraim Oluwole, Ajibade Bright. Abstract — It is expected that significant relationship should exist between price and production of crude oil. However, production may not
necessarily determine price or production may not have significant relationship with price due to some factors. Irrespective of the relationship
between the key factors, production can be studied independently as well as fluctuation in price of crude oil to determine the future occurrence. In
this study, the effect of production was investigated on price of crude oil using scientific approach. Time series data were collected on two
variables from 1999 to 2019. Charts were used to aid visual impression and numerical analyses were carried out for decision making. Correlation,
Regression and Trend analysis were used in the study to model the possible relationship. It was discovered that the two variables; price and
production rate are independent, therefore trend analysis was used to determine the future value. It was observed that the price of crude oil may
be lower than the present value, all things been equal. The rate of production of crude oil is quite promising as future values are predicted to be
higher than the present value.
Keywords— Correlation, Mean Square Error, Nonlinear Model, Price, Production, P-value, Regression, Trend.
—————————— ——————————
1.1 Introduction
According to Organization for Economic Co-operation and
Development (OECD) [4], Crude oil production is defined
as the quantities of oil extracted from the reservoir after the
removal of inert matter or impurities [3] (Nwanze, 2007). It
includes crude oil, natural gas liquids (NGLs) and
additives. This indicator is measured in thousand ton of oil
equivalent. Crude oil is a complex mixture of naturally
occurring hydrocarbon with impurities, of colour ranging
from yellow to black and of variable density and viscosity.
NGLs are the liquid or liquefied hydrocarbons produced in
the process of purification and stabilization of natural gas.
With oil on high demand as global commodity, comes the
possibility that major fluctuations in price can have a
significant economic impact. The two primary factors that
impact the price of oil are: supply and demand, market
sentiment.The concept of supply and demand is fairly
straightforward. As demand increases the price should go
up. As demand decreases the price should go down.Not
quite. The price of oil is actually set in the oil future market.
An oil future contract is a binding agreement that gives one
the right to purchase oil by the barrel at a predefined price
on a predefined date in the future. Under a futures contract,
both the buyer and the seller are obligated to fulfill their
side of the transaction on the specified date.
Basic supply and demand theory states that the more of a
product is produced, the more cheaply it should sell, all
things being equal. The reason more was produced in the
first place is because it became more economically efficient
to do so.
Despite Nigeria’s huge oil wealth, Nigeria has remained
one of the poorest in the world. In addition, the insurgency
in the North, Niger-Delta Avengers in the South,
kidnappings for ransomed and the rampaging Fulani
herdsmen have all compounded Nigeria’s problem in no
small measure. The problems with Nigerian economy have
been traced to failure of successive governments to use oil
revenue and excess crude oil income effectively in the
development of other sectors of the economy[10] (Alley et
al, 2014).
The economy has been bedeviled by sustained
underdevelopment evidenced by poor human
developmental and economic indices including poor
income distribution, militancy and oil violence in the Niger
Delta, endemic corruption, unemployment, relative poverty
[11] (Nwezeaku, 2010). Nigeria’s extreme reliance on the
crude oil market has triggered structural difficulties for the
economy, as earnings from crude oil fluctuate along with
market trends [1] (Aigbedion and Iyayi, 2007). Crude oil
became the dominant resource in the mid-1970s.
On-shore oil exploration accounts for about 65% of total
production, which is located mainly in the swampy areas of
the Niger Delta, while the remaining 35% represents
offshore production and involves drilling for oil in the deep
waters of the continental shelf. The massive increase in oil
revenue as an aftermath of the Middle - East war of 1973
created unprecedented, unexpected and unplanned wealth
for Nigeria, and then began the dramatic shift of policies
from a holistic approach to benchmarking them against the
State of the oil sector [6] (Oladipo and Fabayo, 2012).
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In 2000, oil and gas exports accounted for more than 98% of
export earnings and about 83% of federal government
revenue. Nigeria's proven oil reserves are estimated to be
35 billion barrels; natural gas reserves are well over 100
trillion cuft [2] (Gbadebo, 2008). Nigeria is a member of the
Organization of Petroleum Exporting Countries (OPEC),
and in mid 2001, its crude oil production was averaging
around 2.2 million barrels per day [2] (Gbadebo, 2008).
Due to the contribution of crude oil to the GDP of the
country, there is need for proper study of production rate
as well as price of the product in the past, present and
possibly (predict the) future for proper planning.
2.1 Methodology
The following techniques are used in the study; histogram,
scatter plot, correlation analysis, regression analysis, trend
analysis (linear and quadratic models).
Diagrams generally aid visual expression and communicate
easily with the readers especially those with weak
numerical knowledge. Histogram can be used to determine
the fit of the data. It can also be used to determine the
characteristics of the variables of interest.
Histogram with highest bar at the centre suggested
normally distributed data. It can also show positively
skewed or negatively skewed data.
Scatter plot can be used to determine nature of relationship
between variables and can be used to determine
appropriate model for variables. Scatter plot can show
positive, negative or no relationship between variables. It
has the strength to show linearity between variables. It this
study, it was used to determine the best model for trend
analysis.
Correlation analysis can be parametric or non-parametric,
depending on the type of variables. For two independent or
two dependent variables, the correlation approach is non-
parametric which can be referred to as Spearman Rank
Correlation. For independent and dependent variables, the
correlation approach is parametric correlation which is
product moment correlation. The value of correlation lies
between negative and positive one. Correlation value of
zero implies spurious correlation between variables. The
higher the correlation value, the stronger the bond between
the variables of interest and the lower the correlation value,
the weaker the bond between the variables. Positive
correlation implies direct relationship between the
variables and negative correlation implies inverse
relationship between the variables.
Regression analysis can be used to determine the
mathematical relationship between or among variables.
Regression analysis that involves two variables;
independent and dependent variable is referred to as
simple regression analysis and regression analysis with
more than one independent variable is referred to as
multiple regressions. In this study, simple regression was
used.
Regression analysis can also be linear or nonlinear which
can be shown using scatter plot. In this study, non-linear
and linear regression was used.
Trend analysis is commonly used in time series analysis.
Time series analysis involves time dependent data;
collection of data over a period of time. The data used for
the study are time dependent as the data were collected
over a specified period of time. Trend analysis can be used
to determine the future occurrence of a variable using time
as independent variable. In this study, both price and
production rate were predicted using trend analysis.
3.1 Data Analysis
Based on the aim of the study, the following charts are
presented;
2400200016001200800400
90
80
70
60
50
40
30
20
10
0
PRODUCTION RATE (Thousands)
Fre
qu
en
cy
Histogram of PRODUCTION RATE (Thousands)
Figure 1: Histogram of production rate of crude oil
From the chart, it can be observed that production of the
crude oil increases as time increases the highest was
recorded at the end of the series. This implies production of
the product experiences gradual increase.
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12010080604020
50
40
30
20
10
0
PRICE($)
Fre
qu
en
cy
Histogram of PRICE($)
Figure 2: Histogram of price of crude oil
The chart shows significant fluctuation in the price of the
product. The pattern can be best explained using quadratic
function due to the shape of the diagram. The fluctuation in
price of the product does not correspond with the increase
in production. Therefore, insignificant relationship between
production and price of crude oil is suspected.
Descriptive Statistics: PRODUCTION RATE
(Thousands), PRICE($)
Variable Mean SE Mean StDevMinimum
Maximum SkewnessKurtosis
PRODUCTION RATE 2277.7 16.2 250.9 200.0 2695.0 -
2.40 18.25
PRICE($) 62.66 1.99 30.93 16.80 132.72 0.42 -
0.95
The descriptive statistics of the variables is as shown above.
The average production of crude oil is 2277.7 with standard
error of 16.2. Highest recorded production is 2695unit. For
the period under consideration, average price of the
product is 62.66unit. The lowest recorded price of the
product is 16.8 and the highest recorded price is 132.72unit.
140120100806040200
3000
2500
2000
1500
1000
500
0
PRICE($)
PR
OD
UC
TIO
N R
ATE (
Th
ou
sa
nd
s)
Scatterplot of PRODUCTION RATE (Thousands) vs PRICE($)
Figure 3: Scatter diagram of the variables
The chart shows irregular pattern which can be interpreted
as insignificant relationship between the variables.
Although, the direction of the diagram indicates positive
relationship but weak based on the cluster of the points.
To confirm the assertion, there is need for further test such
as correlation analysis and regression analysis.
Correlations: PRODUCTION RATE (Thousands),
PRICE($)
Pearson correlation of PRODUCTION RATE (Thousands)
and PRICE($) = 0.287
P-Value = 0.000
The correlation value of 0.287 can be interpreted as weak
positive relationship between the variables. This implies
production of the product has insignificant positive effect
on the price. Since the P-value is less than 0.05, there is need
for regression analysis to ascertain the level of the
relationship using coefficient of determination.
Regression Analysis: PRODUCTION RATE (Thousands)
versus PRICE($)
The regression equation is
PRODUCTION RATE (Thousands) = 2132 + 2.33 PRICE($)
Predictor Coef SECoef T P
Constant 2131.67 35.10 60.73 0.000
PRICE($) 2.3298 0.5025 4.64 0.000
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R-Sq = 8.3% R-Sq(adj) = 7.9%
Analysis of Variance
Source DF SS MS F P
Regression 1 1246441 1246441 21.49 0.000
Residual Error 239 13859188 57988
Total 240 15105629
Regression analysis shows (confirms) positive relationship
between the variables. The rate of change of price with
respect to production is 2.33unit. This implies a unit
increase in price of the product will lead to 2.33unit
increase in production.
Coefficient of determination of the model is 8.3% which
implies price is responsible for only 8.3% of the fluctuation
in the production of crude oil.
Using T-test, the parameters in the model are significant
since the P-value of the parameters are less than 0.05. With
coefficient of determination less than 50%, it is not
advisable to predict production of crude oil using price or
vice versal. Therefore, trend analysis is necessary.
Trend Analysis for PRODUCTION RATE (Thousands)
Data PRODUCTION RATE (Thousands)
Length 241
NMissing 0
Fitted Trend Equation
Yt = 2030.4 + 7.502*t - 0.03390*t**2
Accuracy Measures
MAPE 9.4
MAD 138.8
MSD 38751.0
Trend Analysis Plot for PRODUCTION RATE
(Thousands)
24019214496481
3000
2500
2000
1500
1000
500
0
24019214496481
140
120
100
80
60
40
20
0
PRODUCTION RATE (Thousands)
Index
PRICE($)
Time Series Plot of PRODUCTION RATE (Thousands), PRICE($)
Figure 4: Time Series Plot of the variables
240216192168144120967248241
3000
2500
2000
1500
1000
500
0
Index
PR
OD
UC
TIO
N R
ATE (
Th
ou
sa
nd
s)
MAPE 9.4
MAD 138.8
MSD 38751.0
Accuracy Measures
Actual
Fits
Forecasts
Variable
Trend Analysis Plot for PRODUCTION RATE (Thousands)Quadratic Trend Model
Yt = 2030.4 + 7.502*t - 0.03390*t**2
Figure 5: Trend Analysis of Production of Crude Oil
Trend Analysis for PRODUCTION RATE (Thousands)
Data PRODUCTION RATE (Thousands)
Length 241
NMissing 0
Fitted Trend Equation
Yt = 2362.7 - 0.702599*t
Accuracy Measures
MAPE 12.3
MAD 192.3
MSD 60289.7
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Forecasts
Period Forecast
242 2192.64
243 2191.93
Trend Analysis Plot for PRODUCTION RATE
(Thousands)
240216192168144120967248241
3000
2500
2000
1500
1000
500
0
Index
PR
OD
UC
TIO
N R
ATE (
Th
ou
sa
nd
s)
MAPE 12.3
MAD 192.3
MSD 60289.7
Accuracy Measures
Actual
Fits
Forecasts
Variable
Trend Analysis Plot for PRODUCTION RATE (Thousands)Linear Trend Model
Yt = 2362.7 - 0.702599*t
Figure 6: Trend Analysis of Production of Crude Oil Using
Linear model
Summary of the trend analysis models for production
Accuracy Measures Quadratic
Linear
MAPE 9.4 12.3
MAD 138.8 192.3
MSD 38751.0 60289.7
Considering the values, it can be observed that accuracy
measures for quadratic model are better than that of linear
model. Therefore, the model that best explain the trend of
production of crude oil is quadratic model. Using the
model, the future values are 1860.36 and 1851.42.
Using the same approach for price of the product, the
output is shown below;
Trend Analysis for PRICE($)
Data PRICE($)
Length 241
NMissing 0
Fitted Trend Equation
Yt = -3.83 + 1.1675*t - 0.003839*t**2
Accuracy Measures
MAPE 34.149
MAD 17.207
MSD 401.333
Forecasts
Period Forecast
242 53.8867
243 53.1923
244 52.4902
245 51.7805
246 51.0630
247 50.3379
248 49.6051
249 48.8646
250 48.1165
251 47.3606
252 46.5971
253 45.8259
Trend Analysis Plot for PRICE($)
2502252001751501251007550251
140
120
100
80
60
40
20
0
Index
PR
ICE($
)
MAPE 34.149
MAD 17.207
MSD 401.333
Accuracy Measures
Actual
Fits
Forecasts
Variable
Trend Analysis Plot for PRICE($)Quadratic Trend Model
Yt = -3.83 + 1.1675*t - 0.003839*t**2
Figure 7: Trend Analysis of Price of Crude Oil Using
Quadratic model
The prediction of future price of crude oil shows the
possibility having prices lower than the present price. It
shows that the price of the product can be as low as 45unit
considering the present situation and the available data.
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Based on the shape of the diagram, the best model is
quadratic model to capture the fluctuation of the variable.
4.1 Summary and Conclusion
In the location considered, price of crude oil is not a
determinant factor for the volume of crude produced in the
region. The production of crude has experienced significant
increase based on the available data and there is possibility
of more increase. Price of the product highly fluctuates
throughout the period. It was as low as 16unit but later
increased up to 133unit.
In modeling, scatter plot is necessary to determine the best
model that can best explain the variables of interest. Based
on this fact, scatter plot was constructed to determine the
best model for trend analysis of the variables. It was found
that quadratic model best explain the variables compare
with linear model.
Correlation analysis was used to determine the strength
and nature of relationship between the variables which was
found to be positively weak as the value was below 0.5.
Regression analysis also confirms the weakness of the
relationship between price of crude oil and the rate of
production as the value was 8.3%. The results of both
correlation and regression analysis led to further analysis;
trend analysis, to determine the future values of the
variables.
A warning signal was discovered as the prediction of price
of crude oil resulted to a very low figure; 40unit. For a
nation that sole depend on crude oil for its survival, drastic
measures must be taken a boost the GDP of such nation.
Diversification of economy may be needed to strengthen
the economy of the country.
Future value of production of crude oil in the region shows
promising value as the future values are higher than the
present value. This implies more production of crude oil is
expected but this depends on government policies and the
policies of the regulatory bodies.
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