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CHAPTER 1INTRODUCTION
1
CHAPTER 1: INTRODUCTION
1.1 BACKGROUND OF COMPANY
1PETRONAS, short for Petroliam Nasional Berhad, is a Malaysian owned oil and
gas company that was founded on August 17, 1974. Wholly owned by the Government,
the corporation is vested with the entire oil and gas resources in Malaysia and is entrusted
with the responsibility of developing and adding value to these resources. Petronas is
ranked among Fortune Global 500's largest corporations in the world.
Since its incorporation Petronas has grown to be an integrated international oil and
gas company with business interests in 31 countries. As of the end of March 2005, the
Petronas Group comprised 103 wholly owned subsidiaries, 19 partly-owned outfits and 57
associated companies. Together, these companies make the Petronas Group, which is
involved in various oil and gas based activities. On 11 March 2007, the Financial Times
identified Petronas as one of the "new seven sisters": the most influential and mainly
state-owned national oil and gas companies from countries outside the OECD.
The Group is engaged in a wide spectrum of petroleum activities, including
upstream exploration and production of oil and gas to downstream oil refining; marketing
and distribution of petroleum products; trading; gas processing and liquefaction; gas
transmission pipeline network operations; marketing of liquefied natural gas;
petrochemical manufacturing and marketing; shipping; automotive engineering; and
property investment.
1Petronas Malaysia: Corporate News 2005 about PETRONAS
2
2Over the years, PETRONAS have been able to leverage on our business
integration, value-adding and globalization strategy to chart a steady and healthy growth
trend in their financial performance.
In the financial year ended 31 March 2007, PETRONAS charted record Group
revenue of US$51.0 billion, which represents a 14.9 per cent increase from the previous
year’s revenue of US$44.4 billion. Of the total revenue, 76.6 per cent is derived from our
international operations and exports. Manufacturing activities accounted for 55.9 per cent
of the total revenue as the Group continued to create and add value to oil and gas
resources. On the back of the higher revenue, our Group net income grew by 13.2 per
cent to US$12.9 billion from US$11.4 billion in the previous year.
Strong business growth and performance record has enabled PETRONAS to
make significant contributions to the economic and social well-being of Malaysia, as well
as that of our host countries and their people. In Malaysia, PETRONAS has been catalytic
to the nation’s economic growth through our value-adding activities and development of
industry infrastructure and related facilities. PETRONAS has also made direct payments
to both Federal and State Governments in terms of dividends, taxes, export duties and
royalties amounting to US$13.4 billion in the financial year ended 31 March 2007.
2 PETRONAS Group Sustainability Reports 2007
3
1.2 BACKGROUND OF STUDY
This study highlighted on the sales performances of PETRONAS. It explores the
factors that influence to sales performance of PETRONAS and it will be price and
production of oil and gas in Malaysia. 3Malaysia has a well-developed oil and gas sector
and a growing petrochemicals industry. The groundwork for the development of the Oil
and Gas Industry of Malaysia was laid down when the Malaysian Parliament passed the
Petroleum Development Act in 1974. The industry has hence developed to become one of
the most important economic sectors of Malaysia.
PETRONAS performance is not only sales but also profit, asset, income,
production, export and many more. But this study only highlighted to sales performances
only.
The energy sector has also taken advantage of the engineering and technological
advances and has become a sector of great interest to engineers and entrepreneurs. The
country's oil & gas industry has developed from mere production of crude for export to
value-added downstream production of commodity and engineering plastics,
petrochemicals and fertilizers.
The Global O&G Sector has received lot of attention with the recent increases in
crude oil prices, reduction in Government subsidies causing an increase in consumer
price and a parallel increase in discovery of new oil fields. More so for a country like
Malaysia which has the world's 27th largest crude oil reserves at an estimated 3.6 billion
barrels and the 12th largest natural gas reserve of 85.8 trillion cubic feet and is the world's
single largest producer of liquefied natural gas.
3OIL & GAS the High Energy Sectors
4
The oil & gas industry is multidisciplinary in nature and constantly creates
opportunities for professionals from different academic backgrounds and specializations.
The main activities under this sector are Exploration & Production, EPIC (Engineering,
Procurement, Installation, Construction & Commissioning), Inspection, Refinery,
Petrochemical and Retail.
From this research, we can investigate how the price of oil and gas in Malaysia
relate to sales performance of PETRONAS. Besides that, the growth in production of oil
and gas is also important where we can find either the production is relate or not to the
sales of PETRONAS.
The final result will show which factor that influenced to sales performance of
PETRONAS whether price or production of oil and gas in Malaysia.
5
1.3 PROBLEM STATEMENT
Malaysia is important to world energy markets because of its huge oil and natural
gas resorts. Malaysia's oil production occurs offshore and primarily near Peninsular
Malaysia. Production however also takes place offshore of Sabah (East Malaysia) and
Sarawak. Current oil reserves are estimated at approximately 3 Billion barrels with a
declining tendency, due to the lack of major new oil discoveries in the last years.
The rationale of this study is because of to find whether increasing in price and
production growth of oil and gas in Malaysia related to PETRONAS sales performance or
not. This study only take year 2003 till 2007 because that year are the most critical year of
increasing price and production growth of oil and gas in Malaysia. So this study want to
explore whether there are relation between PETRONAS sales performance between 2003
till 2007 with increasing price and production growth of oil and gas in Malaysia or not.
PETRONAS is the state oil and gas company. Other main producers include Esso
Production Malaysia Inc followed by Sabah Shell Petroleum Company and Sweden's
Ludin Oil. Main importers of Malaysian oil are Japan, Thailand, South Korea and
Singapore.
Malaysia's natural gas production has been rising steadily in recent years. In 2000
Malaysia accounted for approximately 15% of total liquefied natural gas exports and is
estimated to contain a 75 trillion cubic feet natural gas resort. Malaysia mainly exports
natural gas to Japan, South Korea and Taiwan. Major natural gas fields include Bedong,
Bintang, Damar, Jerneh, Laho, Lawit, Noring, Pilong, Resak, Telok and Tujoh.
As a result of the energy crisis, our leaders at that time took a brave and bold
decision to take full control of our indigenous oil and gas resources. This decision resulted
in the enactment of the Petroleum Development Act in 1974, which vested the Federal
Government via PETRONAS, with the entire rights, ownership and privileges on the
6
nation’s oil and gas resources. PETRONAS was created as a vehicle to execute, oversee
as well as chart the course for petroleum development of the nation.
4The pace of petroleum development picked up significantly following the formation
of PETRONAS in 1974. Then, Malaysia's oil production was 80,000 barrels per day (bpd).
Today, our country’s oil output has grown almost ten-fold to about 750,000 bpd.
From negligible gas production in 1974, Malaysia is today the third largest exporter
of LNG in the world. Petroleum also transformed our rural landscape and brought about
much socio-economic changes. Sleepy fishing villages such as Kertih and Bintulu
transformed into world-class petroleum and LNG complexes exporting petroleum
products, petrochemicals products and LNG all over the world. Local economies
prospered in tandem.
Today the oil and gas sector is a key component in our country’s economy. Oil and
gas is a major contributor to the Federal Government's revenue. The sector is the largest
recipient of FDI and one of our biggest sources of foreign exchange. The value added
created by the oil and gas sector is more than six times that of electronics and electrical
sector.
Given the major contribution of the oil and gas sector to our economic health, and
the fact that the petroleum industry is a truly global industry, we should be ever mindful of
global business and geopolitical challenges. Today’s world is inherently uncertain and
unstable. We should also remember that Malaysia is not a big oil and gas producer. A
single giant oil field in the Middle East can produce more oil than all the oil fields in
Peninsular and East Malaysia combined. And yet, we have managed our petroleum
resources rather well.
4LAUNCH OF THE MALAYSIAN OIL & GAS SERVICES COUNCIL (MOGSC) - UCAPAN TIMBALAN PERDANA MENTERI
7
The Malaysian oil and gas industry has been very active as the Government,
through PETRONAS, continues to drive efforts to further develop the industry amidst an
increasingly challenging and complex environment. This has resulted in expanded
opportunities for participation by the service providers and supporting industries. Indeed,
the heightened activities from the vibrant industry have substantially contributed towards
the overall growth and development of the Malaysian economy.
As we move forward, greater emphasis will need to be placed on capacity and
capability building to ensure Malaysian companies continue to succeed in this increasingly
challenging environment. While PETRONAS and its Production Sharing Contractors have
encouraged and supported the participation of local contractors at home, you must be
prepared to upgrade your capabilities, knowledge and skills towards playing a bigger role
in adding value to the nation, and rise to the challenge of competing outside your home
market.
Outside Malaysia, PETRONAS has to compete head to head against other oil
giants and National Oil Companies. Malaysian companies in the oil and gas sector must
continue to conduct your business according to the highest ethical standards both here
and abroad.
Ultimately, there is a great opportunity for Malaysia to distinguish and brand
ourselves as a leading and preferred international provider for oil & gas services, backed
by a strong National Oil Company with a global footprint spanning across more than 30
countries. This vision of “PETRONAS Incorporated” is indeed an ambitious one as it will
require the unwavering commitment to excellence, quality, value and high performance on
the part of everyone – PETRONAS, PSC contractors, and oil and gas services players.
But with energy, perseverance and determination, and with the remarkable progress
achieved to date, I am confident that Malaysian oil and gas service providers will be able
to rise to this challenge.
8
1.4 RESEARCH QUESTION
1. What are the factors that will strongly influence PETRONAS sales performance?
2. Is the increasing in price of oil in Malaysia strongly influence PETRONAS sales
performance?
3. Is the production growth of oil in Malaysia strongly influence PETRONAS sales
performance?
4. Is the increasing in price of gas in Malaysia strongly influence PETRONAS sales
performance?
5. Is the production growth of gas in Malaysia strongly influence PETRONAS sales
performance?
1.5 RESEARCH OBJECTIVES
1. To explore the factors those strongly influence PETRONAS sales performance.
2. To find either the increasing in price of oil in Malaysia can strongly influence
PETRONAS sales performance.
3. To investigate either the production growth of oil in Malaysia can strongly influence
PETRONAS sales performance.
4. To find either the increasing in price of gas Malaysia can strongly influence
PETRONAS sales performance.
5. To investigate either the production growth of gas in Malaysia can strongly influence
PETRONAS sales performance.
9
1.6 THEORETICAL FRAMEWORK
In this study, the dependent variable will be the sales performance of PETRONAS. The
variables that will influence the sales performances of PETRONAS are the production of
petroleum and the price competitiveness.
Figure 1.1Schematic diagram for Theoretical Framework
From the figure 1.1, it shows the relationship between dependent variable and
independent variables.
1.6.1 Dependent Variables
According to Zikmud (2000), a dependent variable is a criterion that is to be predicted or
explained. Based on the topic research, the dependent variable is PETRONAS sales
performances.
1.6.2 Independent Variables
According to Zikmund (2000), independent variable is a variable that is expected to
influence the dependent variable. In this study, the independent variables that will
influence the dependent variable are price and production of oil and gas in Malaysia.
10
SALES PERFORMANCES OF
PETRONAS
PRICE OF OIL AND GAS IN MALAYSIA
PRODUCTION OF OIL AND GAS IN MALAYSIA
INDEPENDENT VARIABLES DEPENDENT VARIABLES
1.7 HYPOTHESIS
From this study, there are two independent variables had been found and it comes to the
hypothesis. The PETRONAS sales performance will be influenced or not being influenced
by growth production of petroleum and also the price of petroleum.
1.7.1 Hypothesis 1
H1: PETRONAS sales performance is influenced by the production of oil in Malaysia.
H0: PETRONAS sales performance is not influenced by the production of oil in Malaysia.
1.7.2 Hypothesis 2
H1: PETRONAS sales performance is influenced by the price of oil in Malaysia.
H0: PETRONAS sales performance is not influenced by the price of oil in Malaysia.
1.7.3 Hypothesis 3
H1: PETRONAS sales performance is influenced by the production of gas in Malaysia.
H0: PETRONAS sales performance is not influenced by the production of gas in Malaysia.
1.7.4 Hypothesis 4
H1: PETRONAS sales performance is influenced by the price of gas in Malaysia.
H0: PETRONAS sales performance is not influenced by the price of gas in Malaysia.
11
1.8 SIGNIFICANCE OF STUDY
This study is beneficial for:
1. Professional Organization: This study can be applied also into others professional
association that involved in petroleum business and industry or for those parties who
seeking for the market in Malaysia and will help them to identify how actually the
sales performances for PETRONAS. They will recognize each factor which affects the
sales performances and from there, they will analyze the market and find which
aspects will improve the sales performances of PETRONAS.
2. Others Organizations: This study can also be used and applied in other
organizations that seeking for information regarding petroleum industry in Malaysia.
From this study, they will know the overview of the petroleum industry and focusing
on sales performances. This is also significant for those organizations that looking to
enter the business in the petroleum industry. It will be the investor and etc.
3. The researcher: This research will help other researcher who doing a research that
related to this topic as their additional references. Basically to those who wants to do
an economic research such as international trade, imports and export or research
regarding the petroleum industry.
4. Universiti Teknologi Mara (UITM): UITM can also use it as reference for several
parties such as professors, lecturer and students as well for future use in related field
of study. The most recommended for those economic students, business students,
and etc.
12
1.9 SCOPE OF STUDY
This study highlighted on the sales performances of PETRONAS. It explores the factors
that influence to sales performance of PETRONAS and it will be price and production of
oil and gas in Malaysia. 60 samples are will be taken by monthly in 5 years for analysis.
This paper covers the period from 2003 until 2007 will be studied. The data are, taken
from 5 years started from 2003 up to 2007. Data will be taken from the financial statement
of PETRONAS and Jabatan Perangkaan. The most important sources of information for
evaluating the sales performance of PETRONAS is it financial statement. The data will
also be gathered from Data Stream v4.0 Advance application that subscribed by UITM
Terengganu library.
1.10 LIMITATION OF STUDY
1. Time Constraint
This research has to be completed within 3 months only. There fore, there are lacks of
information in gaining data since the duration is too short. Within the 3 months, the
student also involved with industrial training and it is quite difficult to spend time with both
commitments. Students also need to finish some projects given by the company that
attached for industrial training.
13
2. Inexperienced
The researcher should have an experience and skill to conduct the research. This is
important to ensure that every part of the research can be done without any hassle. It is
also to make sure that all of the sources of information are accurate, updated and reliable.
3. Private and confidential information
There are some information that are reluctant to be given by the organization since it’s
involve the weaknesses and the threat of the company which may affects their business
strategies. In addition, there are procedures to be followed in order to get information from
PETRONAS.
4. Limited resources and information
This study is limited to the availability to get the information since we are using secondary
data as my primary sources. There will be heavily depending on the journals, statistical
reports and books. There are certain reports and statistics that are not updated. The
information from the internet is mostly regarding the overview of the PETRONAS industry
and not specific to the sales performance.
1.11 DEFINITION OF TERMS
1. Performance
How well or badly you do something; how well or badly something works; the country
economic performance.
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2. Sales
Sales are the quantity or amount sold. It is also the activity or profession of selling.
3. Price
Price is the amount of money that we have to pay for something.
4. Production
Production is the action or process of producing or being produced. It is also the amount
of something produced.
5. Oil
Oil is a viscous liquid derived from petroleum, used especially as a fuel or lubricant. Oil is
also means any of various viscous liquids which are insoluble in water but soluble in
organic solvents and are obtained from animals or plants.
6. Natural Gas
Natural gas is a gaseous fossil fuel consisting primarily of methane. It is found in oil fields
(associated) either dissolved or isolated in natural gas fields (non associated), and in coal
beds (as coalbed methane).
7. Capacity
The maximum amount that something can contain or produce.
8. Volume
The amount or quantity of something, especially when great.
15
9. Industry
Industry is an economic activity concerned with the processing of raw materials and
manufacture of goods in factories. Also can defined as a group of economic
establishments all of which are primarily engaged in the same kind of activity or in
producing the same kind of activity or in producing the same kind of product.
10. Economy
Economy is the state of a country or region in terms of the production and consumption of
goods and services and the supply of money.
11. Macroeconomy
A large-scale economic system.
12. Macroeconomics
The part of economics concerned with large-scale or general economic factors, such as
interest rates and national productivity.
13. Exploratory
Exploratory is inquiring into or discuss in detail. It is also evaluate a new option or
possibility.
14. Regression
Statistics a measure of the relation between the mean value of one variable and
corresponding values of other variables.
16
15. Analysis
The part of mathematics concerned with the theory of functions and the use of limits,
continuity, and the operations of calculus.
16. Interpret
Perform (a creative work) in a way that conveys one’s understanding of the creator’s
ideas, understand as having a particular meaning or significance.
17
CHAPTER 2LITERATURE REVIEW
18
CHAPTER 2: LITERATURE REVIEW
Literature review is the documentation of a comprehensive review of the published and
unpublished work from secondary sources of data in the areas of specific interest to the
researcher, according to Sekaran (2003).
2.1 ECONOMIC GROWTH
According to Sachs J.D, et.al (1997), Similarly, China’s underdeveloped legal
system will be more of a drag on the economy as the complexity of economic life
increases, unless legal reform especially regarding private property rights can keep pace
with economic growth. Continuing corruption and misuse of state assets will further
undermine the public support for the existing political institutions. In the 1995 ranking by
Transparency International of the seriousness of corruption within 41 countries, china
ranked second in the extend of corruption. Such problems will play out against a backdrop
of continuing serious pressures on the state, arising from low tax revenues and financial
losses of the state owned enterprises.
2.2 PETRONAS
PETRONAS, short for Petroliam Nasional Berhad, is a Malaysian owned oil and
gas company that was founded on August 17, 1974. Wholly owned by the Government,
the corporation is vested with the entire oil and gas resources in Malaysia and is entrusted
with the responsibility of developing and adding value to these resources. Petronas is
ranked among Fortune Global 500's largest corporations in the world. Since its
incorporation Petronas has grown to be an integrated international oil and gas company
19
with business interests in 31 countries. As of the end of March 2005, the Petronas Group
comprised 103 wholly owned subsidiaries, 19 partly-owned outfits and 57 associated
companies. Together, these companies make the Petronas Group, which is involved in
various oil and gas based activities. (Petronas Malaysia: Corporate News 2005)
Over the years, PETRONAS have been able to leverage on our business
integration, value-adding and globalization strategy to chart a steady and healthy growth
trend in their financial performance. In the financial year ended 31 March 2007,
PETRONAS charted record Group revenue of US$51.0 billion, which represents a 14.9
per cent increase from the previous year’s revenue of US$44.4 billion. Of the total
revenue, 76.6 per cent is derived from our international operations and exports.
Manufacturing activities accounted for 55.9 per cent of the total revenue as the Group
continued to create and add value to oil and gas resources. On the back of the higher
revenue, our Group net income grew by 13.2 per cent to US$12.9 billion from US$11.4
billion in the previous year. Strong business growth and performance record has enabled
PETRONAS to make significant contributions to the economic and social well-being of
Malaysia, as well as that of our host countries and their people. In Malaysia, PETRONAS
has been catalytic to the nation’s economic growth through our value-adding activities and
development of industry infrastructure and related facilities. PETRONAS has also made
direct payments to both Federal and State Governments in terms of dividends, taxes,
export duties and royalties amounting to US$13.4 billion in the financial year ended 31
March 2007. (PETRONAS Group Sustainability Reports 2007)
20
2.3 OIL AND GAS INDUSTRY
According to Samad bin Solbai (2005), Thirty years ago the Malaysian Parliament
passed the Petroleum Development Act (1974) and laid down the groundwork for the
development of the oil & gas industry in the country. Since then the industry has
developed to become one of our most important economic sectors. It is also a sector
which has taken advantage of the most demanding, challenging and exciting engineering
and technological advances and therefore should be of great interest to engineers. The
country’s oil & gas industry has developed from mere production of crude for export to
value-added downstream production of commodity and engineering plastics,
petrochemicals and fertilizers. Local engineers have numerous opportunities to contribute
to the various facets of the industry, from front-end engineering design of oil production
facilities to the design and construction of chemical plants. The oil & gas industry is
multidisciplinary in nature. The input and contribution of every discipline of engineering
have significant roles to play in the industry. Due to its unique requirements, the industry
has developed standards and practices almost at par with the high standards of
requirements in aeronautics.
According to Razmahwata bin Mohamad Razalli (2005), The Oil & Gas (O&G)
industry has seen no small amount of attention during recent months. One item attracting
attention is crude prices rising above USD50 per barrel (0.159m3) and the simultaneous
rise of petrol prices due to reduction in government subsidies. News of discoveries of new
potentially producing fields has increased interest in O&G related stocks, whether in
suppliers to the industry or oil refineries. To encourage and maintain this level of interest,
IEM held a symposium in July 2004, attempting to put forward a forum where people
21
outside the O&G industry could be exposed to issues within the industry. As a follow-up,
this article attempts to present a basic picture of the oil and gas industry in Malaysia.
22
The global outlook series on Oil and Gas provides a collection of statistical
anecdotes, market briefs, and concise summaries of research findings. Get an aerial view
of the global oil and gas industry, the spike in consumption, the depletion of reserves and
other factors triggering the tell tale signs of ever rising prices. The emerging global
scenario is crisply crystallized with an exclusive coverage of Crude Oil, Liquefied Natural
Gas, OPEC Oil, and Non-OPEC Oil. The discussion on all these segments is annotated
with over 174 information rich tables. A one-pager summarized outlook maps the direction
in which the industry is heading. Also provided is a recapitulation of recent mergers,
acquisitions, and other noteworthy strategic corporate developments. The US market is
elaborately discussed, and illustrated with quantitative analysis, and research findings.
Seasoned with 69 tables which present numerical data on production capacity, revenues,
and reserves of leading companies in major market segments, this section provides the
reader a macro level understanding of the Industry. Other parameters evaluated include,
among others, gas purchasing patterns. Other regional markets briefly summarized and
annotated with tables include – Europe, Russia, UK, Iran, Iraq, Saudi Arabia, United Arab
Emirates (UAE), Asia, Australia, China, Indonesia, Brazil, Venezuela, Algeria, and Nigeria,
among few others. The purpose of the abstracted regional market discussion is to provide
the reader a prelude to these markets. Also included is an indexed, easy-to-refer, fact-
finder directory listing the addresses, and contact details of 1115 companies worldwide.
(Oil and Gas Industry, 2006)
23
2.4 PERFORMANCE
According to Pain .N, et.al (1997), they have sought to investigate the time series
relationship between manufacturing exports and foreign direct investment for a number of
OECD economies. Their results suggest that export performance is significantly affected
by changes in the location of production, even after allowing for the impact of changes in
relative prices and quality on export demand. They find evidence of heterogeneity in the
linkages between investment and exports across countries, as might be expected given
the diverse motivations that are known to drive investment decisions. On balance our
evidence points to a small negative impact of outward investment on home country export
performance, offset by a corresponding positive impact from inward investment on host
country export performance. That evidence is consistent with the majority of the findings
from the small number of existing time series studies, but is contrary to the findings from
earlier cross sectional studies using data in the 1970s. One possible reason for this is that
the trade and investment relationship has evolved over time. They report some
preliminary evidence consistent with this hypothesis, which shows that the negative
relationship between outward investment and export performance has strengthened over
time.
According to White D.S, et.al (1998), the conclusions drawn from the study are
subject to the traditional limitations with any US-based, self-administered, mail survey.
One additional limitation is that, given the nature of the research, educated managers
from larger service firms may have responded in disproportionate numbers. None of the
performance measures may capture the complete construct domain. More positively,
however, the findings reported there reduce the number of variables that future
researchers may wish to examine, and therefore allow for multi-item instruments. While
24
limits to the generalizability of the results of this study exist, the findings do offer insights
into the components of four export performance measures and provide a better
understanding of the differences between manufacturing and service industries. First, their
study indicates that the different export performance measures, which were thought to
have high convergent validity, capture different sets of variables. Thus, some may
contend that the study raises more questions than it answers in regard to export
performance measurement in the service industries context. Future researchers may wish
to clarify this issue by delineating the components of export performance measures more
narrowly, or by testing export performance measures within a more homogenous service
setting. Either of that directions would be a significant contribution to the literature in
helping to develop better service industry export performance measures.
25
2.5 PRODUCTION OF OIL AND GAS
According to John S, et.al (1996), significant oil and gas production is found in every
state of the United States except Maine, Vermont, New Hampshire, and Idaho. In most
states the mineral estate is the dominant estate, leaving the surface estate subservient to
oil and gas activities. That can have significant effects on agricultural activities and the
future development potential of the land's highest and best use, particularly for property
located on the urban fringe with development potential. The short- and long-term value
implications of the drilling, production, transportation, and transmission of oil and gas off
property is further complicated by changes in land title (e.g., leases, easements) and the
likelihood of environmental contamination. As a conclusion in his study, oil and gas
activities are a major disruption of the surface and have significant value implications for
surface estate owners. Many landowners and appraisers are not fully aware of the full
impact of oil and gas exploration and production activities to a property's present and
future market value. The first step is to become more aware of the oil and gas well
development procedures and processes.
According to Youngquist W et.al (1999), the peak of world oil production, followed
by an irreversible decline, will be a watershed in human history. Production data from 42
countries representing 98% of world oil production are used rather than reserve estimates.
They believe the former is a more reliable indicator of the future for most oil-producing
regions, with the exception, to some extent, of the OPEC nations which, at times, observe
production quotas. In addition, they recognize that regional and global economic cycles
occasionally change demand for oil, so production figures are not always a current
indication of oil-field potentials. However, for the longer term, production is a useful
measure of true oil-field potential. A judgmental factor also is applied based on the
26
structure, stratigraphy, thermal maturity of oil basins, and volumes of sediments in
potential oil basins yet to be fully explored. Combining these factors with the oil production
numerical data, they have arrived at 2007 for the time of world oil production peak.
Alternative fossil fuel sources which might replace conventional oil (defined as oil from
wells using only primary and secondary recovery methods) cannot come on stream early
enough or in sufficient quantity to significantly affect the peak time. They will merely
augment the far end of the world production curve. They estimates do include recent
technological developments in both exploration and production, but these also seem to be
a minor factor in establishing the peak. Replacement of oil, to the degree this can be
done, by renewable energy sources, such as solar, wind, hydro, or tidal require much time
and capital to bring on stream in significant quantity, and only limited world progress has
been made in these sources. They likewise do not seem to move the peak significantly.
They do recognize, however, given all possible variables, it is likely that our date of 2007
may be wrong. The question is how far wrong? They believe it is reasonably close and on-
going studies will narrow whatever error exists. Importantly, the peak of oil production will
occur within the lifetimes of most people living today.
According to Edwards J.D (1997), predictions of production rates and ultimate
recovery of crude oil are needed for intelligent planning and timely action to ensure the
continuous flow of energy required by the world's increasing population and expanding
economies. Crude oil will be able to supply increasing demand until peak world production
is reached. The energy gap caused by declining conventional oil production must then be
filled by expanding production of coal, natural gas, unconventional oil from tar sands,
heavy oil and oil shales, nuclear and hydroelectric power, and renewable energy sources
(solar, wind, and geothermal). Declining oil production forecasts are based on current
estimated ultimate recoverable conventional crude oil resources of 329 billion barrels for
27
the United States and close to 3 trillion barrels for the world. Peak world crude oil
production is forecast to occur in 2020 at 90 million barrels per day. Conventional crude oil
production in the United States is forecast to terminate by about 2090, and world
production will be close to exhaustion by 2100.
According White D, et.al (1994), creating growth and restoring value will require a
fundamental commitment to "new game" strategies. THE NON-GOVERNMENTAL OIL
AND GAS business has witnessed a huge erosion of value in recent years. Between 1980
and 1993, a representative sample of 103 worldwide oil and gas companies destroyed a
total of nearly US$300 billion in shareholder wealth, compared with he risk adjusted
returns available in their respective countries. If extrapolated to all non-governmental oil
and gas companies, the total loss worldwide would run to more than US$400 billion --
more than the entire GDP of all but 11 countries -- principally in the upstream (exploration
and production) segment. To be sure, leading firms have taken action to improve their
returns and have even met with some success in the last couple of years. But these
actions have not been sufficient to lay the basis for vibrant future growth. These
companies continue to face a substantial cost/price squeeze, exacerbated both by
increasing competition for access to the attractive areas that remain and by a stagnant or
declining resource base. Conventional solutions, therefore, simply will not work.
Meaningful growth is impossible without new game strategies founded on a no-nonsense
exploitation of market, political, and technological discontinuities.
28
2.6 PRICE OIL AND GAS
Salomon Smith Barney, formerly Salomon Brothers Inc., has recently published its
16th annual Survey and analysis of 1998 worldwide oil and gas exploration and
production expenditures. His report, featuring replies from 202 oil and gas companies,
indicates 1998 spending will increase 10.9% over 1997. This marks the third consecutive
year of double digit spending increases by the industry, suggesting growth in demand for
oilfield services is far from over. The planned increase for 1998 follows an increase in
1997 of 18.7%, the strongest in more than 16 years, higher even than the firm's mid-year
1997 update. It is noted that actual spending has exceeded estimates in each of the past
three years, and except for last year's survey, the year-ahead outlook for spending growth
in 1998 is the strongest in ten years. Further, this spending trend is based on realistic
oil/gas prices. Average oil price assumption for this survey is slightly lower at $19.23 per
barrel (WTI) from assumptions one year ago of $19.67 for 1997. And companies with the
largest spending budgets base estimates on an even lower crude price of $18.35. The
average natural gas price assumption for the U.S. did climb modestly to $2.19 per MMBtu
(Henry Hub) from $2.03 assumptions one year ago. (Hugh. A, et.al, 1998)
According to Kilian L et.al (2004), they say economists have long been intrigued
by empirical evidence that suggests that oil price shocks may be closely related to
macroeconomic performance. That interest dates back to the 1970s. The 1970s were a
period of growing dependence on imported oil, unprecedented discruptions in the global
oil market and poor macroeconomic performance in the United States. Thus, it was
natural to suspect a causal relationship from oil prices to U.S macroeconomic aggregates.
Since then, a large body of work has accumulated that purposes to establish this link on
theoretical grounds and to provide empirical evidence in its support. They do not attempt a
29
comprehensive survey of this literature, but rather provide an idiosyncratic synthesis of
what we view as the key issues in this debate and the insights gained over the last 30
years. The timing seems right for such an account. Although the experience of the 1970s
continues to play an important role in discussions of the link between oil and the
macroeconomy, there have been number of new “oil price shocks” since the 1970s,
notable the 1986 collapse of oil prices and the 2000 boom in oil prices as well as the oil
prices associated with the 1990-1991 Gulf War and the 2003 Iraq War. Given them a
richer case history, they arguably in a better position than two decades ago to distinguish
the idiosyncratic features of each oil crisis from the system effects. Increases in oil prices
have been held responsible for recessions, periods of excessive inflation, reduced
productivity and lower economic growth.
One of study from Ahmad Al-Kandari et.al (2007), employs newly developed
techniques of rank tests of nonlinear cointegration analysis proposed by Breitung J, et.al
(1997) and Breitung J (2001). The Breitung's method is selected in their study due its
potential superiority at detecting cointegration when the error-correction mechanism is
nonlinear. The purpose of this research is to examine the linkages between oil prices and
stock market in Gulf Cooperation Council (GCC) countries. Prior work argues that oil
prices and the GCC stock markets are not related. That conclusion could be due to the
fact that only linear linkages have been examined. The empirical analyses of the paper
supports that oil price impact the stock price indices in GCC countries in a nonlinear
fashion. Thus, the statistical analysis in their paper obviously supports a nonlinear
modeling of the relationship between oil and the economy. In an important study, they
detect no relationship between oil prices and the GCC stock market returns, which is
against the importance of the oil prices on the economy of these countries. This study
argues that the conclusion is due to the fact that they focus solely on linear dependences.
30
In this paper, they consider an application of rank tests for a nonlinear cointegration
relationship between oil price and the stock markets in GCC countries. Their empirical
analysis supports that oil price impacts the stock price indices in GCC countries in a
nonlinear fashion. Thus, the statistical analysis in this paper obviously supports a
nonlinear modeling of the relationship between oil and the economy. The implication of
their findings is that policy makers at GCC countries should keep an eye on the effects of
changes in oil price levels on their own economies and stock markets. For individual and
institutional investors, the nonlinear relationship between oil and stock markets implies
predictability in the GCC stock markets.
According to one journal paper from YI WEN, et.al (2007), their paper offers a
plausible explanation for the close link between oil prices and aggregate macroeconomic
performance in the 1970s. Although that link has been well documented in the empirical
literature, standard economic models are not able to replicate this link when actual oil
prices are used to simulate the models. In particular, standard models cannot explain the
depth of the recession in 1974–75 and the strong revival in 1976–78 based on the oil price
movements in that period. Their paper argues that a missing multiplier-accelerator
mechanism from standard models may hold the key. A large body of empirical literature
has suggested that oil price shocks have an important effect on economic activity. Their
literature has convincingly argued that oil prices were both significant determinants of U.S.
economic activity and exogenous to it in the post-war period. However, despite 30 years
of research since the first major post-war oil crisis in 1973–74, how exactly can oil shocks
because a severe economic recession still remains an open question. Imported oil as an
input for the entire U.S. economy accounted for roughly 1%–2% of the total production
cost in the early 1970s. Based on this cost share, and assuming constant returns to scale,
even a 100% increase in the price of oil can only translate into an approximately 1%–2%
31
decrease in output, notwithstanding the likely counter effects from factor substitutions. Yet
the actual decline in output following the 1973 oil crisis, which caused a roughly 80%
increase in the price of imported oil, was about 7%–8% from its peak.
According to Hamilton J.D (1996), many of the quarterly oil price increases
observed since 1985 are corrections to even bigger oil price decreases the previous
quarter. When one looks at the net increase in oil prices over the year, recent data are
consistent with the historical correlation between oil shocks and recessions. Hooker M
(1996) has convincingly demonstrated that neither the linear relation between oil prices
and output proposed by Hamilton J.D (1983) nor the asymmetric relation based on oil
price increases alone advocated by Mork A (1989) is consistent with observed economic
performance over the last decade. Hooker's evidence is overwhelming and his conclusion
is unassailable. Oil price changes are clearly an unreliable instrument for macroeconomic
analysis of data subsequent to 1986. To summarize, the evidence since 1983 has
strengthened, not weakened, my earlier convictions. My 1985 article concluded with the
statement: 'The political history of the Middle East makes it almost inevitable that
sometime within the next decade economists will be granted some more data with which
to assess the economic effects of oil supply disruptions.' This is exactly what happened in
1990 when Iraq invaded Kuwait, and surely this oil shock was a key factor in the recession
that followed. But for those who have yet to be convinced, he hereby renew the forecast -
sometime again within the next ten years, turmoil in the Middle East will produce another
major disruption to world petroleum supplies. The crisis will produce a recession in the
United States.
According to Gali J, et.al (2007), they characterize the macroeconomic
performance of a set of industrialized economies in the aftermath of the oil price shocks of
32
the 1970s and of the last decade, focusing on the differences across episodes. They
examine four different hypotheses for the mild effects on inflation and economic activity of
the recent increase in the price of oil: (a) good luck (i.e. lack of concurrent adverse
shocks), (b) smaller share of oil in production, (c) more flexible labor markets, and (d)
improvements in monetary policy. We conclude that all four have played an important role.
Finally, there have reach five main conclusion. First, the effects of oil price shocks must
have coincided in time with large shocks of a different nature. They have given some
evidence that increases in other commodity prices were important in the 1970s. They
have not identified the other shocks for the 2000s. Second, the effects of oil price shocks
have changed over time, with steadily smaller effects on prices and wages, as well as on
output and employment. Third, that a first plausible cause for these changes is a decrease
in real wage rigidities. Such rigidities are needed to generate the type of large stagnation
in response to adverse supply shocks such as those that took place in the 1970s. They
have shown that the response of the consumption wage to the marginal rate of
substitution, and thus to employment, appears to have increased over time. Fourth, that a
second plausible cause for these changes is the increased credibility of monetary policy.
They have offered a simple formalization of lack of credibility and its effect on the volatility
frontier. They also have shown that the response of expected inflation to oil shocks has
substantially decreased over time. Fifth, that a third plausible cause for these changes is
simply the decrease in the share of oil in consumption and in production. The decline is
large enough to have quantitatively significant implications.
According to Hamilton J.D (2005), Economic theory suggests that it would be the
real oil price rather than the nominal price that should matter for economic decisions. It
does not make much difference in summarizing the size of any given shock whether one
uses the nominal price ot or the real price of oil, since in most of these shocks the move in
33
nominal prices is an order of magnitude larger than the change in overall prices during
that quarter. However, particularly in the early part of the sample, the nominal oil price
would stay frozen for years and then adjust suddenly. To the extent that there is a
difference between using nominal and real prices as the explanatory variable in such
regressions, the real price results from the confluence of two forces— events such as the
Suez Crisis, which accounts for almost all of the movement in the nominal price between
1955 and 1965, and the quarter-to-quarter change in inflation, which is completely
endogenous with respect to the economy and whose consequences for future output are
likely to be quite different from those of an oil shock. Insofar as the statistical exogeneity
of the right-hand variables is important for interpreting the regression, many researchers
have for this reason used the nominal oil price change rather than the real oil price
change as the explanatory variable.
Another potential macroeconomic effect of oil price shocks is on the inflation rate.
The long-run inflation rate is governed by monetary policy, so ultimately this is a question
about how the central bank responds to the oil shock. Hooker, et.al (1996) found
evidence that oil shocks made a substantial contribution to U.S. core inflation before 1981
but have made little contribution since, consistent with the conclusion of Gertler M (2000)
that U.S. monetary policy has become significantly more devoted to curtailing inflation.
34
2.7 RELATION BETWEEN PERFORMANCE AND THE FACTOR THAT INFLUENCE
According to Forbes K.J (2002), his paper documents several stylized facts of
how recent depreciations affected different measures of firm performance. It uses a
sample of over 13,500 firms from 42 countries to examine the impact of 12 “major
depreciations” between 1997 and 2000. It evaluates firm performance based on the
immediate impact of depreciations on sales and net income, as well as the expected
longer-term impact as measured by changes in market capitalization and asset value. The
first part of the analysis focused on how depreciations affect firms on average. It finds that
in the year after depreciations, firms have significantly higher growth in market
capitalization, suggesting that depreciations increase the present value of firms’ expected
future profits. On the other hand, firms have significantly lower growth in net income
(measured in local currency), suggesting that even if firms benefit from depreciations in
the long run, the immediate impact on performance may be negative. Firms also tend to
display worse performance after depreciations when performance is measured in US
dollars, but this could largely reflect changes in relative currency values and not significant
changes in real performance.
35
CHAPTER 3DATA METHODOLOGY AND DESIGN
36
CHAPTER 3: RESEARCH METHODOLOGY AND DESIGN
3.1 RESEARCH DESIGN
This chapter explains the Research Methodology and design that were being adapted by
the researcher. The topic of this study is ‘’ Sales Performance of PETRONAS“. This topic
has chosen because I wants to knows and identify how the sales performance affected by
increasing in price and production growth of oil and gas in Malaysia. This research is an
exploratory study.
3.2 DATA COLLECTION METHOD
The discussion is about the research design, and the data collection of the secondary
data. Secondary Data is the integral part of this study. It served as an access to the
company internal, as Sekaran (2003) explained that secondary data are indispensable for
most organizational research. All monthly data from 2003 till 2007 gathered for
investigation will be collected from the PETRONAS and JABATAN PERANGKAAN.
PETRONAS sales data also can be collected from software DATA STREAM v4 at library
of Universiti Teknologi Mara Dungun.
3.3 RESEARCH METHODOLOGY
In order to determine the nature of the relationship between dependent variable and
independent variable, hypothesis testing Descriptive Analysis, Unit Root Test and Multiple
Linear Regression Analysis are applied in interpret data. In this study “Sales Performance
of PETRONAS”, I will use the E-views to regress all the variables to find the relationship
between these variables and PETRONAS sales.
37
3.4 DATA ANALYSIS
3.4.1 Descriptive Statistic
This analysis used the simple methods to estimate various parameters of the sales of
Petronas. It is important to be estimated because of the characteristics of the all variable
can be known. Descriptive Statistics are used to describe the basic features of the data
gathered from an experimental study in various ways. They provide simple summaries
about the sample and the measures. Together with simple graphics analysis, they form
the basis of virtually every quantitative analysis of data. It is necessary to be familiar with
primary methods of describing data in order to understand phenomena and make
intelligent decisions. Various techniques that are commonly used are classified as:
1. Graphical description in which we use graphs to summarize data.
2. Tabular description in which we use tables to summarize data.
3. Summary statistics in which we calculate certain values to summarize data.
In general, statistical data can be briefed as a list of subjects or units and the data
associated with each of them. Although most research uses many data types for each
unit, we will limit ourselves to just one data item each for this simple introduction.
3.4.2 Unit Root Test
Unit root tests are important in examining the stationarity of a time series. It is important to
determine the characteristic of the individual series. Therefore, to test the presence of
stochastic non-stationary (unit root) in the data series, two tests was undertaken that is
Augmented Dickey-Fuller (ADF) and Philips-Perron (PP). The first step in modeling time
series is to test the stationarity of the data by applying unit root test. Because the data are
trended, the purpose of the unit root test is to determine whether the series is consistent
38
with an I(1) process with a stochastic trend, or if it is consistent with an I(0) process, that is
it is stationary, with a deterministic trend. If two series are integrated of order one, they
may have a linear combination that is stationary without requiring differencing and if they
do, they are considered to be cointegrated.
3.4.3 Multiple Linear Regression Analysis
In practice, the concept of estimation with Multiple Regression is the same as with Simple
linear, but necessary computation can be much more complicated. Regression Analysis
will indicate how variable are related to another by providing an equation that allows the
use of unknown values of variables to estimate the unknown value of the dependent
variable.
3.4.3.1 Linear Regression
Y = dependent Variable (PETRONAS sales)α = the constant valueβ Price = independent variable (Price Oil and Gas in Malaysia)β Production = independent variable (Production Oil and Gas in Malaysia)
Figure 3.1 Multiple Linear Functions
Dependent Variables
The dependent variable is PETRONAS sales performances.
Independent Variables
The independent variables that will influence the dependent variable are price and
production of oil and gas in Malaysia
39
Y= α + β Price + β Production
3.4.3.2 T-Statistic
T-statistic result will shows that are all independent variables have a significant or
insignificant relationship towards PETRONAS sales. If t-value is greater than standard
distribution, the independent variable is said to be statistically significant. To be more
precise, we must to refer to the student’s t Distribution table to get the t-critical.
Degree of Freedom = (number of observation – number of independent variables – 1)
For example there are 20 observations with two independents variable and one constant.
Refer to the t-Distribution table, at 95% (0.05) confidence, the table value is 2.110. If the t-
statistic or T-ratio is greater than 2.11, the variable is significant. This is using to
determine the significant relationship between PETRONAS sales and independent
variables which are Malaysia’s oil and gas price and Malaysia’s oil and gas production.
3.4.3.3 Coefficient of Determination (R²)
Coefficient of Determination or R² measures how much the variation of the dependent
variable is explains by independent variables. The value of R² must range from 0 to 1. The
measurement of R² is shown below:
R² = 0: Use for providing absolute no explanation to the variation independent
variables. Here, the dependent variable has no relationship with the independent
variables.
R² = 0.1 to 0.5: The relationship between dependent variable and dependent variables
is weak
R² = 0.6 to 0.99: It means more than 60% of the dependent variable is explained by
the independent variables.
R² = 1: Equation is perfect where the dependent variable is perfectly explained by
independent variables.
40
CHAPTER 4DATA ANALYSIS AND INTERPRETATION
41
CHAPTER 4: DATA ANALYSIS AND INTERPRETATION
This chapter discusses the result and findings of this research that can be explained and
interpret to study this research “A Study on Relationship between Petronas Sales
Performances with Price and Production of Oil and Gas in Malaysia’’. The all data are
collected from the Data Stream from the year 2003 up to 2007 by monthly. The findings of
this study use the method that state in chapter 3. Probably the data must be 60 data if all
data selected from month of January till December for year 2003 till 2007. But because of
the data for 2007 in data stream are only till march 2007 and not till December, so all
variables data become only 51 data.
4.1 DESCRIPTIVE ANALYSIS
PETRONAS SALES
OILPRODUCTION
OILPRICE
GASPRODUCTION
GASPRICE
Mean 14256084 678.7451 50.51039 279090.5 167037.5 Median 12451080 664.0000 51.97000 279002.0 167625.0 Maximum 19496370 790.0000 78.16000 326325.0 213522.0 Minimum 8970494. 530.0000 27.08000 203230.0 123684.0 Std. Dev. 3877151. 69.98224 16.05210 25560.88 17551.03 Observations 51 51 51 51 51
Table 4.1Descriptive statistic of the variables
2003-2007: 51 observations
Table 4.1 shows the descriptive statistic of the variables. For Petronas sales, value of
mean is 14,256,084 while value of median is 12,451,080. The total observation for all
variables is 51 observations. For Petronas sale’s standard deviation value is 3,877,151,
maximum value is 19,496,370 and for minimum value is 8,970,494. For oil production
value of mean is 678.7451 while value of median is 664. For oil production’s standard
42
deviation value is 69.98224, maximum value is 790 and for minimum value is 530. For oil
price value of mean is 50.51039 while value of median is 51.97. For oil price’s standard
deviation value is 16.0521, maximum value is 78.16 and for minimum value is 27.08. For
gas production value of mean is 279,090.5 while value of median is 279,002. For gas
production’s standard deviation value is 25,560.88, maximum value is 326,325 and for
minimum value is 203,230. And lastly for gas price value of mean is 167,037 while value
of median is 167,625. For gas price’s standard deviation value is 17,551.03, maximum
value is 213522 and for minimum value is 123,684.
4.2 UNIT ROOT TEST ANALYSIS
Level 1st DifferenceVariable ADF PP KPSS ADF PPSALES 0.7313 0.6362 - 5.5427 7.4762
- * *GAS PRICE 1.6488 3.9475 0.6557 4.4456 14.7580
* ** * *GAS PRODUCTION 6.0795 6.1883 - 8.6188 21.7813
* * - * *OIL PRICE 1.0943 0.9332 - 6.8344 8.7205
- * *OIL PRODUCTION 1.1444 1.1444 - 6.5616 6.5689
- * ** denotes 99%of significant level** denotes 95% of significant level***denotes 90% of significant level
Table 4.2Unit Root Test Table for ADF, PP and KPSS test
2003-2007: 51 observations
The result of unit root test is presented in Table 4.3. When the ADF and PP are above
compared to critical value, the null hypothesis can be rejected. It provides the statistical
results for both tests in levels and first differences for all the series. The ADF and PP test
agree in classifying all the variables namely sales, gas price, gas production, oil price and
43
oil production as variables in lag 10. As stated above, the variable without symbol means
of non-stationary. We can see that all the variables are non-stationary except the gas
price and gas production in ‘level’. However all variables are stationary in ‘first
differencing’.
In ‘level’, both gas price and gas production can reject the null hypothesis in level,
meaning to say both are stationary. Gas price is stationary 3.9475 only in PP at 99% of
confident level. Means that there is conflict in ADF and PP gas price only stationary in PP.
So we must do KPSS test and get 0.6557 at 95% of confident level. Gas Production is
stationary at 6.0795 in ADF at 99% confident level and 6.1883 in PP at 99% confident
level. However, we still cannot accept the result because all variable still not stationary in
‘level’. So we proceed to ‘first difference’.
In ‘first difference’, all variables can reject the null hypothesis mean stationary in first
difference. Sales are stationary at 5.5427 in ADF at 99% confident level and 7.4762 in PP
at 99% confident level. Gas price are stationary at 4.4456 in ADF at 99% confident level
and 14.7580 in PP also at 99% confident level. Gas production is stationary at 8.6188 in
ADF at 99% confident level and 21.7813 in PP also at 99% confident level. Oil price is
stationary at 6.8344 in ADF at 99% confident level and 8.7205 in PP at 99%confident
level. Lastly, Oil production is stationary at 6.5616 in ADF at 99% confident level and
6.5689 in PP at 99% confident level.
When all variables stationary enough at first difference, so we can proceed to multiple
linear regression analysis to explain the relationship between all variables.
44
4.3 MULTIPLE LINEAR REGRESSION ANALYSIS
Regression Analysis is basically a statistical technique that be used to explain the
relationship between all the variables. A multiple regression mode allows us to evaluate
the influence of more that one predictor variable on a response variable. The multiple
regression analysis could be formulated as:
Dependent Variable: PETRONAS SALES
Method: Least Squares
Sample: 2003M01 2007M03
Included observations: 51
Coefficient Std. Error t-Statistic Prob.
C 16.78010 2.787780 6.019161 0.0000
OIL PRODUCTION -0.651327 0.194819 -3.343234 0.0017
OIL PRICE 0.621256 0.062715 9.905956 0.0000
GAS PRODUCTION 0.266919 0.142579 1.872077 0.0676
GAS PRICE -0.153948 0.155755 -0.988395 0.3281
Table 4.3Multiple Regression Analysis of the variables
2003-2007: 51 Observations
45
4.3.1 Linear Function
Figure 4.1 Multiple Linear Functions for PETRONAS sales
Figure 4.1 shows the relationship between PETRONAS sales with independent variables
which are oil price, gas price, oil production, and gas production.
Explanation of Linear Function
1. If all variables are constant, PETRONAS sales will change by 16.7801. Meaning to
say, if all independent variable are constant in this study, the dependent variable
will change by 16.7801.
2. If the rising in Malaysia’s oil production, it will indicated that PETRONAS sales will
decline by 0.6513. The result due to the strong negative relationship between the
Malaysia’s oil production and PETRONAS sales.
3. If the Malaysia’s gas production will increase, PETRONAS sales will be increase
by 0.2669. There is a weak positive relationship between Malaysia’s gas
production and PETRONAS sales.
4. If the Malaysia’s oil prices will rises, it will raise PETRONAS sales by 0.6212. It is
also show the strong positive relationship between Malaysia’s oil price and
PETRONAS sales.
5. While, if the increasing in Malaysia’s gas price, it will be decreasing the
PETRONAS sales by 0.1539. It shows negative and weak relationship between
PETRONAS sales and Malaysia’s gas price.
46
PETRONAS Sales = 16.7801 + 0.6212 Oil Price – 0.1539 Gas Price – 0.6513 Oil Production + 0.2669 Gas Production
4.3.2 T-statistic
Another regression get from the table is T-statistic. T-statistics is use to determine if there
is a significant relationship between the PETRONAS sales and each independent variable
which are Malaysia’s oil and gas price, and Malaysia’s oil and gas production. If t-value is
greater than standard distribution, the independent variable is said to be statistically
significant. To be more precise, we must to refer to the student’s t Distribution table to get
the t-critical.
Figure 4.2 Degree of Freedom
Refer on t-Distribution table at 95% (0.05) confidence, Degree of Freedom=46 are not
stated. So I take the Degree of Freedom=40 because that is the most near with 46. The
value T-critical value that I get is 2.021. So, t-critical 2.021 are using to determine the
significant relationship between PETRONAS sales and independent variables which are
Malaysia’s oil and gas price and Malaysia’s oil and gas production.
Independent Variables T-statistic T-critical
Oil Production -3.343234 > 2.021 – significant
Gas Production 1.872077 < 2.021 – insignificant
Oil Price 9.905956 > 2.021 – significant
Gas Price -0.988395 < 2.021 – insignificant
T-critical = 2.021Table 4.4
T-Statistic between all independent variables2003-2007: 51 Observations
47
Degree of Freedom = (number of observation – number of independent variables – 1)
= (51 – 4 – 1)
From the t-statistic result on table 4.4, it shows that are two independent variables, which
are gas production and gas price are insignificant relationship towards the PETRONAS
sales because their T-statistic are below T-critical which is 2.021. However the other two
variables which are oil production and oil price shows the significant relationship toward
PETRONAS sales because the table shows that t-statistic of the both oil price and
production are above than T-critical which is 2.021. So the results for hypothesis based
on T-statistic are:
Hypothesis 1
H1: PETRONAS sales performance is influenced by the production of oil - ACCEPT
H0: PETRONAS sales performance is not influenced by the production of oil – REJECT
Hypothesis 2
H1: PETRONAS sales performance is influenced by the price of oil - ACCEPT
H0: PETRONAS sales performance is not influenced by the price of oil – REJECT
Hypothesis 3
H1: PETRONAS sales performance is influenced by the production of gas - REJECT
H0: PETRONAS sales performance is not influenced by the production of gas – ACCEPT
Hypothesis 4
H1: PETRONAS sales performance is influenced by the price of gas - REJECT
H0: PETRONAS sales performance is not influenced by the price of gas – ACCEPT
4.3.3 Coefficient of Determination (R2)
48
R-squared Adjusted R-squared0.902543 0.894069
Table 4.5Coefficient of Determination Table
2003-2007: 51 Observations
Coefficient of Determination or R² measures how much the variation of the dependent
variable is explains by independent variables. The value of R² must range from 0 to 1. The
coefficient of determination is the percent of variation in the dependent variable that is
explained by the regression equation. In this study the coefficient of determination is
0.9025 (based on table 4.5) which means 90.25% of the dependent variable can be
explained by all independent variables which are Malaysia’s oil and gas production, and
Malaysia’s oil and gas price. And another 9.75% of variation can not be explained by all
independent variables and only can be explained by other variable. I also found that
adjusted R squared is 0.894069.
49
CHAPTER 5CONCLUSION & RECOMMENDATION
CHAPTER 5: CONCLUSION AND RECOMMENDATION
50
5.1 CONCLUSION
This paper is providing an investigation to find whether increasing in price and
production growth of oil and gas in Malaysia related to PETRONAS sales performance or
not. This study only take year 2003 till 2007 because that year are the most critical year of
increasing price and production growth of oil and gas in Malaysia. So, purpose of this
study is to explore whether increasing price and production growth of oil and gas in
Malaysia influenced PETRONAS sales performance or not.
Based on the result that analyzed in chapter 4 before, we can conclude that oil
production in Malaysia has strongly negative relationship with PETRONAS sales
performances. It means that when oil production growth in Malaysia in decreasing level,
PETRONAS sales will strongly increase and when oil production growth in Malaysia in
increasing level, PETRONAS sales will decrease. Oil price has strongly positive
relationship with PETRONAS sales performance. It means that when oil price in Malaysia
increase, PETRONAS sales also strongly increase and when oil price in Malaysia
decrease, PETRONAS sales also decrease.
The rationale of decreasing rate of oil production in Malaysia can make rise in
PETRONAS sales is, oil production in Malaysia is oil production in Malaysia only part of
PETRONAS product in Malaysia. PETRONAS has other oil supply in other country. Oil
production in Malaysia that contributes from PETRONAS is only 47% and the other 53%
are contributed from Shell, Exxon-Mobil and Conoco Philips. PETRONAS’s oil is the high
grade oil then other’s oil. So PETRONAS rather sell their oil product to other country to
51
gain more profit. For that when oil production decrease in Malaysia, PETRONAS also can
maintain their sales in increasing level.
Gas production has positive but weak relationship with PETRONAS sales
performance. It means that when gas production growth in Malaysia are in decreasing
level, PETRONAS sales also will decrease and when gas production growth in Malaysia
are in increasing level, PETRONAS sales also will increase but not too much than oil
production. Gas price has negative and weak relationship with PETRONAS sales
performance. It means that when gas price in Malaysia increase, PETRONAS sales will
decrease and when gas price in Malaysia decrease, PETRONAS sales will increase but
also not too much than oil production.
Based on T- Statistic result, we can concluded that Malaysia’s oil price and
production can influenced the performance of PETRONAS sales while Malaysia’s gas
price and production can not influenced the performance of PETRONAS sales. Oil price in
Malaysia has strong significant relationship with PETRONAS sales based on T-statistic
which is 9.9059. It means that positive impact between oil price in Malaysia and
PETRONAS are strong. Increasing in oil price will strongly increase PETRONAS sales. Oil
production in Malaysia also has a statistically significant relationship with PETRONAS
sales based on T-statistic which is 3.3432. It means that negative impact between oil
production in Malaysia and PETRONAS sales are strong. Decreasing oil production in
Malaysia, strongly increase PETRONAS sales.
While gas production and price in Malaysia has statically insignificant relationship
with PETRONAS sales. It means that gas production and price can not influenced
PETRONAS sales. It means that Malaysia’s gas price and production are not important in
52
influenced PETRONAS sales but Malaysia’s oil price and production can influence
performance of PETRONAS sales and the most important factor to influence PETRONAS
sales.
Based on R squared result which is 0.9025, it means that 90.25% from of
PETRONAS sales can be explained by all independent variables which are Malaysia’s oil
and gas production and price. Another 9.75% of variation can not be explained by price
and production of oil and gas in Malaysia and only can be explained by other variables.
As a conclusion, PETRONAS sales performance are strongly influenced by oil
price but weakly influenced by gas production. Other independent variables which are oil
production and gas price in Malaysia have negative impact to PETRONAS sales.
These results suggest that PETRONAS can maintain increasing in their sales
performance by maintaining the increasing of oil price and gas production in Malaysia.
PETRONAS is the main producers of oil and gas in Malaysia and for that they can easily
maintain increasing in their sales by controlling the oil price and gas production in
Malaysia.
53
5.2 RECOMMENDATION
PETRONAS as the main producer of oil and gas in Malaysia can easily increase
maintain their sales by keep track on oil price in Malaysia and keep it maintain in
increasing rate because the result show that oil price in Malaysia strongly influenced
PETRONAS sales.
Government also can keep oil price in Malaysia in increasing rate because
PETRONAS is the company which is the major contribute to dividend, royalty and tax to
government. So government also must keep the oil price in increasing rate as
PETRONAS is the major contributor to government dividend, royalty and tax. But
government and PETRONAS has to face the difficulty to keep the oil price in Malaysia in
increasing rate because government must face their citizen’s voice about that increasing
rate of oil price in Malaysia because it will burden to Malaysian citizen if oil price in
Malaysia keep in increasing rate.
PETRONAS also must not keep increasing in oil production in Malaysia in
increasing level because it will give strongly negative impact to PETRONAS sales as the
result shows. The rationale of this situation is when production of oil becomes more and
more, the oil price will decrease and it will also decrease the PETRONAS sales.
PETRONAS is the main company which held responsible to manage and maintain
the supply of oil and gas in Malaysia. To reach this motive, Petronas profit must be
invested to activity to find the new place of oil and gas in Malaysia or other’s country.
Petronas profits also must be invested to R&D. If there are no new production for oil and
gas, Malaysia will be the main importers of oil and gas in year 2009. This is the main
54
barriers of PETRONAS to keep production of oil and gas in Malaysia in decreasing rate to
make the PETRONAS sales in increasing rate. PETRONAS can find the new production
of oil and gas but PETRONAS must not keep it drastic because it will also can give their
sales in drastically decrease.
The studies in this field are scarce. Therefore this contemporary study should be
proliferated in future research by expanding the time frame of data collection in order to
achieve better results. If the time frame given is longer we can analyze all the sectors in
Malaysia.
The time constraints of the studies that undergo within four months shall be
improve in order to be more concentrate in finishing this research and also get more idea
in doing the research. Long time of period such extra two month to finish the work is
needed especially for new researcher such as student in first degree. This is because we
are new in this field and lack of experience in doing the work. By providing length of time
enables us to find new skill and knowledge through reading previous work.
To the new researcher that interested in extension this study, they can select other
sector in Malaysia which contributed to Malaysian Economic such as rubber, palm oil,
timber and others.
55
REFERENCES
56
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58
APPENDICES
59
Appendix 1: VARIABLES DATA
VARIABLES SALESOIL
PRODUCTIONGAS
PRODUCTIONOIL PRICE GAS PRICE
CURRENCY/UNIT
RM METRIC/TONNES
CUBIC/FEETUS
DOLLAR/BARRELRM/CUBIC/
FEET2003M01 8970494 726 302755 32.45 1679902003M02 8970494 726 209864 34.52 136633
2003M03 8970494 705 251183 27.87 161910
2003M04 9830365 726 285494 28.28 151421
2003M05 9830365 732 286306 27.14 144605
2003M06 9830365 742 267516 27.08 145661
2003M07 9830365 742 269732 28.67 148550
2003M08 9830365 742 274853 30.17 143223
2003M09 9830365 742 258641 28.51 140396
2003M10 9830365 748 276527 31.87 153313
2003M11 9830365 753 283656 30.98 161512
2003M12 9830365 769 310988 32.03 174372
2004M01 9830365 730 275619 33.94 165094
2004M02 9830365 730 278242 35.47 152769
2004M03 9830365 740 291443 35.11 159377
2004M041245108
0750 268001 35.57 151425
2004M051245108
0760 258207 40.27 155682
2004M061245108
0760 262732 38.48 152265
2004M071245108
0760 265429 41.35 144215
2004M081245108
0760 272331 49.13 158277
2004M091245108
0760 254567 47.87 123684
2004M101245108
0790 259063 53.35 167977
2004M111245108
0764 279002 46.3 172650
2004M121245108
0759 267331 40.61 199209
2005M011245108
0664 251738 49.24 190566
2005M021245108
0637 250699 51.97 167625
2005M031245108
0632 282910 58.32 213522
2005M041656792
0595 220855 55.86 179697
2005M051656792
0554 302573 48.94 169620
2005M061656792
0602 296173 57.37 165507
2005M071656792
0626 274682 58.63 160127
2005M081656792
0656 287615 68.31 169787
2005M091656792
0664 294790 67.4 163515
2005M101656792
0657 303345 59.95 177487
2005M111656792
0635 314869 57.61 176049
2005M121656792
0651 310492 62.36 183523
2006M011656792
0639 326325 71.41 179099
2006M02 1656792 641 203230 64.98 160476
60
0
2006M031656792
0611 260396 65.96 198121
2006M041949637
0595 298704 76.76 179697
2006M051949637
0530 280782 72.18 165599
2006M061949637
0593 300692 71.96 172578
2006M071949637
0602 291204 78.16 172363
2006M081949637
0599 305358 77.54 174378
2006M091949637
0614 292792 67.38 163902
2006M101949637
0626 296114 62.31 180507
2006M111949637
0652 307932 60.77 182091
2006M121949637
0651 273950 66.29 197225
2007M011949637
0594 315196 58.26 188043
2007M021949637
0590 275197 62.87 170470
2007M031949637
0590 305523 66.22 185129
Appendix 2: VARIABLES CHART
61
8,000,000
10,000,000
12,000,000
14,000,000
16,000,000
18,000,000
20,000,000
2003 2004 2005 2006
SALES
520
560
600
640
680
720
760
800
2003 2004 2005 2006
OPRODUCTION
20
30
40
50
60
70
80
2003 2004 2005 2006
OPRICE
200,000
220,000
240,000
260,000
280,000
300,000
320,000
340,000
2003 2004 2005 2006
GPRODUCTION
120,000
140,000
160,000
180,000
200,000
220,000
2003 2004 2005 2006
GPRICE
Appendix 3: DESCRIPTIVE TABLE
62
SALES OPRODUCTION OPRICE GPRODUCTION GPRICE
Mean 14256084 678.7451 50.51039 279090.5 167037.5
Median 12451080 664.0000 51.97000 279002.0 167625.0
Maximum 19496370 790.0000 78.16000 326325.0 213522.0
Minimum 8970494. 530.0000 27.08000 203230.0 123684.0
Std. Dev. 3877151. 69.98224 16.05210 25560.88 17551.03
Skewness 0.111346 -0.162914 -0.000652 -0.833472 0.140850
Kurtosis 1.486371 1.674109 1.652151 4.024354 3.162238
Jarque-Bera 4.973913 3.961317 3.860483 8.134502 0.224561
Probability 0.083163 0.137978 0.145113 0.017124 0.893794
Sum 7.27E+08 34616.00 2576.030 14233618 8518913.
Sum Sq. Dev. 7.52E+14 244875.7 12883.50 3.27E+10 1.54E+10
Observations 51 51 51 51 51
Appendix 4: DESCRIPTIVE HISTOGRAM
63
0
2
4
6
8
10
12
14
10000000 12500000 15000000 17500000
Series: SALESSample 2003M01 2007M03Observations 51
Mean 14256084Median 12451080Maximum 19496370Minimum 8970494.Std. Dev. 3877151.Skewness 0.111346Kurtosis 1.486371
Jarque-Bera 4.973913Probability 0.083163
0
2
4
6
8
10
12
14
525 550 575 600 625 650 675 700 725 750 775 800
Series: OPRODUCTIONSample 2003M01 2007M03Observations 51
Mean 678.7451Median 664.0000Maximum 790.0000Minimum 530.0000Std. Dev. 69.98224Skewness -0.162914Kurtosis 1.674109
Jarque-Bera 3.961317Probability 0.137978
0
1
2
3
4
5
6
30 40 50 60 70 80
Series: OPRICESample 2003M01 2007M03Observations 51
Mean 50.51039Median 51.97000Maximum 78.16000Minimum 27.08000Std. Dev. 16.05210Skewness -0.000652Kurtosis 1.652151
Jarque-Bera 3.860483Probability 0.145113
64
0
2
4
6
8
10
200000 220000 240000 260000 280000 300000 320000
Series: GPRODUCTIONSample 2003M01 2007M03Observations 51
Mean 279090.5Median 279002.0Maximum 326325.0Minimum 203230.0Std. Dev. 25560.88Skewness -0.833472Kurtosis 4.024354
Jarque-Bera 8.134502Probability 0.017124
0
1
2
3
4
5
6
7
8
9
120000 140000 160000 180000 200000
Series: GPRICESample 2003M01 2007M03Observations 51
Mean 167037.5Median 167625.0Maximum 213522.0Minimum 123684.0Std. Dev. 17551.03Skewness 0.140850Kurtosis 3.162238
Jarque-Bera 0.224561Probability 0.893794
65
Appendix 5: UNIT ROOT TEST TABLE
SALES
ADF-LEVEL
Null Hypothesis: SALES has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on AIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -0.731335 0.8292
Test critical values: 1% level -3.568308
5% level -2.921175
10% level -2.598551
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(SALES)
Method: Least Squares
Date: 04/16/08 Time: 00:58
Sample (adjusted): 2003M02 2007M03
Included observations: 50 after adjustments
Coefficient Std. Error t-Statistic Prob.
SALES(-1) -0.021698 0.029670 -0.731335 0.4681
C 517578.6 434770.4 1.190464 0.2397
R-squared 0.011020 Mean dependent var 210517.5
Adjusted R-squared -0.009584 S.D. dependent var 794314.4
S.E. of regression 798111.6 Akaike info criterion 30.05706
Sum squared resid 3.06E+13 Schwarz criterion 30.13354
Log likelihood -749.4266 Hannan-Quinn criter. 30.08619
F-statistic 0.534851 Durbin-Watson stat 2.120717
Prob(F-statistic) 0.468130
66
ADF-1ST DIFFERENCE
Null Hypothesis: D(SALES) has a unit root
Exogenous: Constant
Lag Length: 10 (Automatic based on AIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -5.542676 0.0000
Test critical values: 1% level -3.610453
5% level -2.938987
10% level -2.607932
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(SALES,2)
Method: Least Squares
Date: 04/16/08 Time: 00:59
Sample (adjusted): 2004M01 2007M03
Included observations: 39 after adjustments
Coefficient Std. Error t-Statistic Prob.
D(SALES(-1)) -6.479112 1.168950 -5.542676 0.0000
D(SALES(-1),2) 4.989642 1.072188 4.653701 0.0001
D(SALES(-2),2) 4.500172 0.975316 4.614068 0.0001
D(SALES(-3),2) 4.010702 0.878296 4.566459 0.0001
D(SALES(-4),2) 3.521232 0.781075 4.508190 0.0001
D(SALES(-5),2) 3.031762 0.683565 4.435220 0.0001
D(SALES(-6),2) 2.542292 0.585624 4.341165 0.0002
D(SALES(-7),2) 2.052822 0.486991 4.215316 0.0002
D(SALES(-8),2) 1.563352 0.387138 4.038234 0.0004
D(SALES(-9),2) 1.042235 0.280955 3.709615 0.0009
D(SALES(-10),2) 0.521117 0.170227 3.061310 0.0049
C 1640293. 322394.1 5.087850 0.0000
R-squared 0.744735 Mean dependent var 0.000000
Adjusted R-squared 0.640738 S.D. dependent var 1305703.
S.E. of regression 782618.1 Akaike info criterion 30.22634
Sum squared resid 1.65E+13 Schwarz criterion 30.73820
Log likelihood -577.4136 Hannan-Quinn criter. 30.40999
F-statistic 7.161130 Durbin-Watson stat 1.292687
Prob(F-statistic) 0.000015
67
PP-LEVEL
Null Hypothesis: SALES has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West using Bartlett kernel)
Adj. t-Stat Prob.*
Phillips-Perron test statistic -0.636219 0.8528
Test critical values: 1% level -3.568308
5% level -2.921175
10% level -2.598551
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 6.12E+11
HAC corrected variance (Bartlett kernel) 4.61E+11
Phillips-Perron Test Equation
Dependent Variable: D(SALES)
Method: Least Squares
Date: 04/16/08 Time: 01:01
Sample (adjusted): 2003M02 2007M03
Included observations: 50 after adjustments
Coefficient Std. Error t-Statistic Prob.
SALES(-1) -0.021698 0.029670 -0.731335 0.4681
C 517578.6 434770.4 1.190464 0.2397
R-squared 0.011020 Mean dependent var 210517.5
Adjusted R-squared -0.009584 S.D. dependent var 794314.4
S.E. of regression 798111.6 Akaike info criterion 30.05706
Sum squared resid 3.06E+13 Schwarz criterion 30.13354
Log likelihood -749.4266 Hannan-Quinn criter. 30.08619
F-statistic 0.534851 Durbin-Watson stat 2.120717
Prob(F-statistic) 0.468130
68
PP-1ST DIFFERENCE
Null Hypothesis: D(SALES) has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West using Bartlett kernel)
Adj. t-Stat Prob.*
Phillips-Perron test statistic -7.476241 0.0000
Test critical values: 1% level -3.571310
5% level -2.922449
10% level -2.599224
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 6.27E+11
HAC corrected variance (Bartlett kernel) 4.95E+11
Phillips-Perron Test Equation
Dependent Variable: D(SALES,2)
Method: Least Squares
Date: 04/16/08 Time: 01:02
Sample (adjusted): 2003M03 2007M03
Included observations: 49 after adjustments
Coefficient Std. Error t-Statistic Prob.
D(SALES(-1)) -1.073245 0.145473 -7.377610 0.0000
C 230547.7 119620.9 1.927320 0.0600
R-squared 0.536622 Mean dependent var 0.000000
Adjusted R-squared 0.526763 S.D. dependent var 1174942.
S.E. of regression 808268.4 Akaike info criterion 30.08314
Sum squared resid 3.07E+13 Schwarz criterion 30.16035
Log likelihood -735.0368 Hannan-Quinn criter. 30.11243
F-statistic 54.42913 Durbin-Watson stat 2.011578
Prob(F-statistic) 0.000000
69
GAS PRICE
ADF-LEVEL
Null Hypothesis: GPRICE has a unit root
Exogenous: Constant
Lag Length: 6 (Automatic based on AIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -1.648814 0.4497
Test critical values: 1% level -3.588509
5% level -2.929734
10% level -2.603064
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GPRICE)
Method: Least Squares
Date: 04/16/08 Time: 01:00
Sample (adjusted): 2003M08 2007M03
Included observations: 44 after adjustments
Coefficient Std. Error t-Statistic Prob.
GPRICE(-1) -0.307050 0.186225 -1.648814 0.1079
D(GPRICE(-1)) -0.222609 0.192897 -1.154030 0.2561
D(GPRICE(-2)) -0.051380 0.198027 -0.259461 0.7968
D(GPRICE(-3)) 0.068754 0.194522 0.353452 0.7258
D(GPRICE(-4)) -0.031760 0.191774 -0.165612 0.8694
D(GPRICE(-5)) 0.096839 0.181538 0.533437 0.5970
D(GPRICE(-6)) -0.308157 0.155806 -1.977831 0.0556
C 52644.08 31105.88 1.692416 0.0992
R-squared 0.456345 Mean dependent var 831.3409
Adjusted R-squared 0.350634 S.D. dependent var 17329.12
S.E. of regression 13964.37 Akaike info criterion 22.08937
Sum squared resid 7.02E+09 Schwarz criterion 22.41377
Log likelihood -477.9662 Hannan-Quinn criter. 22.20967
F-statistic 4.316919 Durbin-Watson stat 1.943668
Prob(F-statistic) 0.001477
70
ADF-1ST DIFFERENCE
Null Hypothesis: D(GPRICE) has a unit root
Exogenous: Constant
Lag Length: 10 (Automatic based on AIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -4.445657 0.0010
Test critical values: 1% level -3.610453
5% level -2.938987
10% level -2.607932
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GPRICE,2)
Method: Least Squares
Date: 04/16/08 Time: 01:03
Sample (adjusted): 2004M01 2007M03
Included observations: 39 after adjustments
Coefficient Std. Error t-Statistic Prob.
D(GPRICE(-1)) -6.325627 1.422878 -4.445657 0.0001
D(GPRICE(-1),2) 4.673787 1.332311 3.508030 0.0016
D(GPRICE(-2),2) 4.095179 1.216079 3.367528 0.0023
D(GPRICE(-3),2) 3.678207 1.072794 3.428623 0.0020
D(GPRICE(-4),2) 3.171720 0.944419 3.358381 0.0023
D(GPRICE(-5),2) 2.826706 0.809723 3.490955 0.0017
D(GPRICE(-6),2) 2.163573 0.724939 2.984492 0.0060
D(GPRICE(-7),2) 1.697190 0.597702 2.839527 0.0085
D(GPRICE(-8),2) 1.191214 0.473844 2.513934 0.0182
D(GPRICE(-9),2) 0.821056 0.319392 2.570683 0.0160
D(GPRICE(-10),2) 0.388519 0.170130 2.283657 0.0305
C 3812.903 2366.867 1.610949 0.1188
R-squared 0.870599 Mean dependent var 46.12821
Adjusted R-squared 0.817880 S.D. dependent var 31180.63
S.E. of regression 13306.51 Akaike info criterion 22.07755
Sum squared resid 4.78E+09 Schwarz criterion 22.58942
Log likelihood -418.5123 Hannan-Quinn criter. 22.26121
F-statistic 16.51393 Durbin-Watson stat 1.860123
Prob(F-statistic) 0.000000
71
PP-LEVEL
Null Hypothesis: GPRICE has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West using Bartlett kernel)
Adj. t-Stat Prob.*
Phillips-Perron test statistic -3.947578 0.0035
Test critical values: 1% level -3.568308
5% level -2.921175
10% level -2.598551
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 2.26E+08
HAC corrected variance (Bartlett kernel) 2.51E+08
Phillips-Perron Test Equation
Dependent Variable: D(GPRICE)
Method: Least Squares
Date: 04/16/08 Time: 01:03
Sample (adjusted): 2003M02 2007M03
Included observations: 50 after adjustments
Coefficient Std. Error t-Statistic Prob.
GPRICE(-1) -0.477585 0.124931 -3.822777 0.0004
C 79944.56 20935.66 3.818583 0.0004
R-squared 0.233394 Mean dependent var 342.7800
Adjusted R-squared 0.217423 S.D. dependent var 17335.52
S.E. of regression 15335.59 Akaike info criterion 22.15292
Sum squared resid 1.13E+10 Schwarz criterion 22.22940
Log likelihood -551.8230 Hannan-Quinn criter. 22.18204
F-statistic 14.61362 Durbin-Watson stat 2.198728
Prob(F-statistic) 0.000380
72
PP-1ST DIFFERENCE
Null Hypothesis: D(GPRICE) has a unit root
Exogenous: Constant
Bandwidth: 6 (Newey-West using Bartlett kernel)
Adj. t-Stat Prob.*
Phillips-Perron test statistic -14.75806 0.0000
Test critical values: 1% level -3.571310
5% level -2.922449
10% level -2.599224
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 2.14E+08
HAC corrected variance (Bartlett kernel) 1.05E+08
Phillips-Perron Test Equation
Dependent Variable: D(GPRICE,2)
Method: Least Squares
Date: 04/16/08 Time: 14:29
Sample (adjusted): 2003M03 2007M03
Included observations: 49 after adjustments
Coefficient Std. Error t-Statistic Prob.
D(GPRICE(-1)) -1.468997 0.124098 -11.83741 0.0000
C 1013.451 2135.978 0.474467 0.6374
R-squared 0.748830 Mean dependent var 939.1020
Adjusted R-squared 0.743486 S.D. dependent var 29521.43
S.E. of regression 14951.78 Akaike info criterion 22.10301
Sum squared resid 1.05E+10 Schwarz criterion 22.18023
Log likelihood -539.5237 Hannan-Quinn criter. 22.13230
F-statistic 140.1244 Durbin-Watson stat 2.130884
Prob(F-statistic) 0.000000
73
KPSS-LEVEL
Null Hypothesis: GPRICE is stationary
Exogenous: Constant
Bandwidth: 5 (Newey-West using Bartlett kernel)
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic 0.655798
Asymptotic critical values*: 1% level 0.739000
5% level 0.463000
10% level 0.347000
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
Residual variance (no correction) 3.02E+08
HAC corrected variance (Bartlett kernel) 9.34E+08
KPSS Test Equation
Dependent Variable: GPRICE
Method: Least Squares
Date: 04/16/08 Time: 14:31
Sample: 2003M01 2007M03
Included observations: 51
Coefficient Std. Error t-Statistic Prob.
C 167037.5 2457.635 67.96676 0.0000
R-squared 0.000000 Mean dependent var 167037.5
Adjusted R-squared 0.000000 S.D. dependent var 17551.03
S.E. of regression 17551.03 Akaike info criterion 22.40303
Sum squared resid 1.54E+10 Schwarz criterion 22.44090
Log likelihood -570.2772 Hannan-Quinn criter. 22.41750
Durbin-Watson stat 0.956463
74
GAS PRODUCTION
ADF-LEVEL
Null Hypothesis: GPRODUCTION has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on AIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -6.079528 0.0000
Test critical values: 1% level -3.568308
5% level -2.921175
10% level -2.598551
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GPRODUCTION)
Method: Least Squares
Date: 04/16/08 Time: 01:04
Sample (adjusted): 2003M02 2007M03
Included observations: 50 after adjustments
Coefficient Std. Error t-Statistic Prob.
GPRODUCTION(-1) -0.872273 0.143477 -6.079528 0.0000
C 243037.3 40131.49 6.056025 0.0000
R-squared 0.435033 Mean dependent var 55.36000
Adjusted R-squared 0.423262 S.D. dependent var 33772.61
S.E. of regression 25648.01 Akaike info criterion 23.18150
Sum squared resid 3.16E+10 Schwarz criterion 23.25798
Log likelihood -577.5374 Hannan-Quinn criter. 23.21062
F-statistic 36.96066 Durbin-Watson stat 1.722524
Prob(F-statistic) 0.000000
75
ADF-1ST DIFFERENCE
Null Hypothesis: D(GPRODUCTION) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic based on AIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -8.618878 0.0000
Test critical values: 1% level -3.574446
5% level -2.923780
10% level -2.599925
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GPRODUCTION,2)
Method: Least Squares
Date: 04/16/08 Time: 14:30
Sample (adjusted): 2003M04 2007M03
Included observations: 48 after adjustments
Coefficient Std. Error t-Statistic Prob.
D(GPRODUCTION(-1)) -1.972637 0.228874 -8.618878 0.0000
D(GPRODUCTION(-1),2) 0.350648 0.128812 2.722178 0.0092
C 2069.558 3803.531 0.544115 0.5890
R-squared 0.765754 Mean dependent var -229.0208
Adjusted R-squared 0.755343 S.D. dependent var 53197.47
S.E. of regression 26312.94 Akaike info criterion 23.25397
Sum squared resid 3.12E+10 Schwarz criterion 23.37092
Log likelihood -555.0953 Hannan-Quinn criter. 23.29817
F-statistic 73.55302 Durbin-Watson stat 2.113782
Prob(F-statistic) 0.000000
76
PP-LEVEL
Null Hypothesis: GPRODUCTION has a unit root
Exogenous: Constant
Bandwidth: 3 (Newey-West using Bartlett kernel)
Adj. t-Stat Prob.*
Phillips-Perron test statistic -6.188342 0.0000
Test critical values: 1% level -3.568308
5% level -2.921175
10% level -2.598551
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 6.32E+08
HAC corrected variance (Bartlett kernel) 7.55E+08
Phillips-Perron Test Equation
Dependent Variable: D(GPRODUCTION)
Method: Least Squares
Date: 04/16/08 Time: 01:04
Sample (adjusted): 2003M02 2007M03
Included observations: 50 after adjustments
Coefficient Std. Error t-Statistic Prob.
GPRODUCTION(-1) -0.872273 0.143477 -6.079528 0.0000
C 243037.3 40131.49 6.056025 0.0000
R-squared 0.435033 Mean dependent var 55.36000
Adjusted R-squared 0.423262 S.D. dependent var 33772.61
S.E. of regression 25648.01 Akaike info criterion 23.18150
Sum squared resid 3.16E+10 Schwarz criterion 23.25798
Log likelihood -577.5374 Hannan-Quinn criter. 23.21062
F-statistic 36.96066 Durbin-Watson stat 1.722524
Prob(F-statistic) 0.000000
77
PP-1ST DIFFERENCE
Null Hypothesis: D(GPRODUCTION) has a unit root
Exogenous: Constant
Bandwidth: 11 (Newey-West using Bartlett kernel)
Adj. t-Stat Prob.*
Phillips-Perron test statistic -21.78132 0.0001
Test critical values: 1% level -3.571310
5% level -2.922449
10% level -2.599224
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 7.41E+08
HAC corrected variance (Bartlett kernel) 1.54E+08
Phillips-Perron Test Equation
Dependent Variable: D(GPRODUCTION,2)
Method: Least Squares
Date: 04/16/08 Time: 14:30
Sample (adjusted): 2003M03 2007M03
Included observations: 49 after adjustments
Coefficient Std. Error t-Statistic Prob.
D(GPRODUCTION(-1)) -1.442974 0.118535 -12.17340 0.0000
C 1703.092 3970.167 0.428972 0.6699
R-squared 0.759211 Mean dependent var 2514.633
Adjusted R-squared 0.754088 S.D. dependent var 56034.51
S.E. of regression 27787.25 Akaike info criterion 23.34250
Sum squared resid 3.63E+10 Schwarz criterion 23.41972
Log likelihood -569.8913 Hannan-Quinn criter. 23.37180
F-statistic 148.1916 Durbin-Watson stat 2.360075
Prob(F-statistic) 0.000000
78
OIL PRICE
ADF-LEVEL
Null Hypothesis: OPRICE has a unit root
Exogenous: Constant
Lag Length: 2 (Automatic based on AIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -1.094389 0.7107
Test critical values: 1% level -3.574446
5% level -2.923780
10% level -2.599925
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(OPRICE)
Method: Least Squares
Date: 04/16/08 Time: 01:05
Sample (adjusted): 2003M04 2007M03
Included observations: 48 after adjustments
Coefficient Std. Error t-Statistic Prob.
OPRICE(-1) -0.049402 0.045141 -1.094389 0.2797
D(OPRICE(-1)) -0.133318 0.141608 -0.941456 0.3516
D(OPRICE(-2)) -0.273417 0.141387 -1.933815 0.0596
C 3.538900 2.385107 1.483749 0.1450
R-squared 0.123199 Mean dependent var 0.798958
Adjusted R-squared 0.063418 S.D. dependent var 5.035986
S.E. of regression 4.873686 Akaike info criterion 6.085233
Sum squared resid 1045.124 Schwarz criterion 6.241167
Log likelihood -142.0456 Hannan-Quinn criter. 6.144161
F-statistic 2.060817 Durbin-Watson stat 2.062405
Prob(F-statistic) 0.119175
79
ADF-1ST DIFFERENCE
Null Hypothesis: D(OPRICE) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic based on AIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -6.834468 0.0000
Test critical values: 1% level -3.574446
5% level -2.923780
10% level -2.599925
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(OPRICE,2)
Method: Least Squares
Date: 04/16/08 Time: 01:05
Sample (adjusted): 2003M04 2007M03
Included observations: 48 after adjustments
Coefficient Std. Error t-Statistic Prob.
D(OPRICE(-1)) -1.447964 0.211862 -6.834468 0.0000
D(OPRICE(-1),2) 0.289856 0.140895 2.057243 0.0455
C 1.048199 0.715072 1.465865 0.1496
R-squared 0.605994 Mean dependent var 0.208333
Adjusted R-squared 0.588483 S.D. dependent var 7.614043
S.E. of regression 4.884379 Akaike info criterion 6.070423
Sum squared resid 1073.572 Schwarz criterion 6.187373
Log likelihood -142.6901 Hannan-Quinn criter. 6.114618
F-statistic 34.60574 Durbin-Watson stat 2.059118
Prob(F-statistic) 0.000000
80
PP-LEVEL
Null Hypothesis: OPRICE has a unit root
Exogenous: Constant
Bandwidth: 8 (Newey-West using Bartlett kernel)
Adj. t-Stat Prob.*
Phillips-Perron test statistic -0.933209 0.7694
Test critical values: 1% level -3.568308
5% level -2.921175
10% level -2.598551
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 24.26684
HAC corrected variance (Bartlett kernel) 13.80776
Phillips-Perron Test Equation
Dependent Variable: D(OPRICE)
Method: Least Squares
Date: 04/16/08 Time: 01:06
Sample (adjusted): 2003M02 2007M03
Included observations: 50 after adjustments
Coefficient Std. Error t-Statistic Prob.
OPRICE(-1) -0.052617 0.044734 -1.176212 0.2453
C 3.316564 2.355367 1.408088 0.1655
R-squared 0.028015 Mean dependent var 0.675400
Adjusted R-squared 0.007765 S.D. dependent var 5.047354
S.E. of regression 5.027719 Akaike info criterion 6.106988
Sum squared resid 1213.342 Schwarz criterion 6.183469
Log likelihood -150.6747 Hannan-Quinn criter. 6.136112
F-statistic 1.383475 Durbin-Watson stat 2.191680
Prob(F-statistic) 0.245311
81
PP-1ST DIFFERENCE
Null Hypothesis: D(OPRICE) has a unit root
Exogenous: Constant
Bandwidth: 11 (Newey-West using Bartlett kernel)
Adj. t-Stat Prob.*
Phillips-Perron test statistic -8.720519 0.0000
Test critical values: 1% level -3.571310
5% level -2.922449
10% level -2.599224
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 25.02604
HAC corrected variance (Bartlett kernel) 11.34473
Phillips-Perron Test Equation
Dependent Variable: D(OPRICE,2)
Method: Least Squares
Date: 04/16/08 Time: 01:06
Sample (adjusted): 2003M03 2007M03
Included observations: 49 after adjustments
Coefficient Std. Error t-Statistic Prob.
D(OPRICE(-1)) -1.127117 0.144996 -7.773418 0.0000
C 0.725855 0.735236 0.987241 0.3286
R-squared 0.562490 Mean dependent var 0.026122
Adjusted R-squared 0.553181 S.D. dependent var 7.641512
S.E. of regression 5.107932 Akaike info criterion 6.139426
Sum squared resid 1226.276 Schwarz criterion 6.216644
Log likelihood -148.4159 Hannan-Quinn criter. 6.168723
F-statistic 60.42602 Durbin-Watson stat 2.006471
Prob(F-statistic) 0.000000
82
OIL PRODUCTION
ADF-LEVEL
Null Hypothesis: OPRODUCTION has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on AIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -1.144442 0.6910
Test critical values: 1% level -3.568308
5% level -2.921175
10% level -2.598551
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(OPRODUCTION)
Method: Least Squares
Date: 04/16/08 Time: 01:08
Sample (adjusted): 2003M02 2007M03
Included observations: 50 after adjustments
Coefficient Std. Error t-Statistic Prob.
OPRODUCTION(-1) -0.063318 0.055326 -1.144442 0.2581
C 40.36884 37.84259 1.066757 0.2914
R-squared 0.026562 Mean dependent var -2.720000
Adjusted R-squared 0.006282 S.D. dependent var 27.01023
S.E. of regression 26.92526 Akaike info criterion 9.463185
Sum squared resid 34798.55 Schwarz criterion 9.539666
Log likelihood -234.5796 Hannan-Quinn criter. 9.492310
F-statistic 1.309747 Durbin-Watson stat 1.843957
Prob(F-statistic) 0.258115
83
ADF-1ST DIFFERENCE
Null Hypothesis: D(OPRODUCTION) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on AIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -6.561656 0.0000
Test critical values: 1% level -3.571310
5% level -2.922449
10% level -2.599224
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(OPRODUCTION,2)
Method: Least Squares
Date: 04/16/08 Time: 01:08
Sample (adjusted): 2003M03 2007M03
Included observations: 49 after adjustments
Coefficient Std. Error t-Statistic Prob.
D(OPRODUCTION(-1)) -0.956197 0.145725 -6.561656 0.0000
C -2.653935 3.956379 -0.670799 0.5056
R-squared 0.478099 Mean dependent var 0.000000
Adjusted R-squared 0.466994 S.D. dependent var 37.73537
S.E. of regression 27.54955 Akaike info criterion 9.509810
Sum squared resid 35671.96 Schwarz criterion 9.587027
Log likelihood -230.9903 Hannan-Quinn criter. 9.539106
F-statistic 43.05534 Durbin-Watson stat 1.991438
Prob(F-statistic) 0.000000
84
PP-LEVEL
Null Hypothesis: OPRODUCTION has a unit root
Exogenous: Constant
Bandwidth: 0 (Newey-West using Bartlett kernel)
Adj. t-Stat Prob.*
Phillips-Perron test statistic -1.144442 0.6910
Test critical values: 1% level -3.568308
5% level -2.921175
10% level -2.598551
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 695.9711
HAC corrected variance (Bartlett kernel) 695.9711
Phillips-Perron Test Equation
Dependent Variable: D(OPRODUCTION)
Method: Least Squares
Date: 04/16/08 Time: 01:07
Sample (adjusted): 2003M02 2007M03
Included observations: 50 after adjustments
Coefficient Std. Error t-Statistic Prob.
OPRODUCTION(-1) -0.063318 0.055326 -1.144442 0.2581
C 40.36884 37.84259 1.066757 0.2914
R-squared 0.026562 Mean dependent var -2.720000
Adjusted R-squared 0.006282 S.D. dependent var 27.01023
S.E. of regression 26.92526 Akaike info criterion 9.463185
Sum squared resid 34798.55 Schwarz criterion 9.539666
Log likelihood -234.5796 Hannan-Quinn criter. 9.492310
F-statistic 1.309747 Durbin-Watson stat 1.843957
Prob(F-statistic) 0.258115
85
PP-1ST DIFFERENCE
Null Hypothesis: D(OPRODUCTION) has a unit root
Exogenous: Constant
Bandwidth: 2 (Newey-West using Bartlett kernel)
Adj. t-Stat Prob.*
Phillips-Perron test statistic -6.568930 0.0000
Test critical values: 1% level -3.571310
5% level -2.922449
10% level -2.599224
*MacKinnon (1996) one-sided p-values.
Residual variance (no correction) 727.9991
HAC corrected variance (Bartlett kernel) 750.3419
Phillips-Perron Test Equation
Dependent Variable: D(OPRODUCTION,2)
Method: Least Squares
Date: 04/16/08 Time: 01:08
Sample (adjusted): 2003M03 2007M03
Included observations: 49 after adjustments
Coefficient Std. Error t-Statistic Prob.
D(OPRODUCTION(-1)) -0.956197 0.145725 -6.561656 0.0000
C -2.653935 3.956379 -0.670799 0.5056
R-squared 0.478099 Mean dependent var 0.000000
Adjusted R-squared 0.466994 S.D. dependent var 37.73537
S.E. of regression 27.54955 Akaike info criterion 9.509810
Sum squared resid 35671.96 Schwarz criterion 9.587027
Log likelihood -230.9903 Hannan-Quinn criter. 9.539106
F-statistic 43.05534 Durbin-Watson stat 1.991438
Prob(F-statistic) 0.000000
86
Appendix 6: MULTIPLE REGRESSION TABLE
Dependent Variable: LNSALES
Method: Least Squares
Date: 04/03/08 Time: 13:53
Sample: 2003M01 2007M03
Included observations: 51
Coefficient Std. Error t-Statistic Prob.
C 16.78010 2.787780 6.019161 0.0000
OPRODUCTION -0.651327 0.194819 -3.343234 0.0017
OPRICE 0.621256 0.062715 9.905956 0.0000
GPRODUCTION 0.266919 0.142579 1.872077 0.0676
GPRICE -0.153948 0.155755 -0.988395 0.3281
R-squared 0.902543 Mean dependent var 16.43515
Adjusted R-squared 0.894069 S.D. dependent var 0.279170
S.E. of regression 0.090862 Akaike info criterion -1.866062
Sum squared resid 0.379769 Schwarz criterion -1.676667
Log likelihood 52.58457 Hannan-Quinn criter. -1.793688
F-statistic 106.5011 Durbin-Watson stat 0.954378
Prob(F-statistic) 0.000000
Estimation Command:=========================LS SALES C OPRODUCTION OPRICE GPRODUCTION GPRICE
Estimation Equation:=========================SALES = C(1) + C(2)*OPRODUCTION + C(3)*OPRICE + C(4)*GPRODUCTION + C(5)*GPRICE
Substituted Coefficients:=========================SALES = 16.7801006208 - 0.651327154912*OPRODUCTION + 0.621256316991*OPRICE + 0.266918819272*GPRODUCTION - 0.153947955962*GPRICE
87
Appendix 7: T-DISTRIBUTION TABLE
df 10% 5% 1%1 6.314 12.706 63.6572 2.920 4.303 9.9253 2.353 3.182 5.8414 2.132 2.776 4.6045 2.015 2.571 4.0326 1.943 2.447 3.7077 1.895 2.365 3.4998 1.860 2.306 3.3559 1.833 2.262 3.250
10 1.812 2.228 3.16911 1.796 2.201 3.10612 1.782 2.179 3.05513 1.771 2.160 3.01214 1.761 2.145 2.97715 1.753 2.131 2.94716 1.746 2.120 2.92117 1.740 2.110 2.89818 1.734 2.101 2.87819 1.729 2.093 2.86120 1.725 2.086 2.84521 1.721 2.080 2.83122 1.717 2.074 2.81923 1.714 2.069 2.80724 1.711 2.064 2.79725 1.708 2.060 2.78726 1.706 2.056 2.77927 1.703 2.052 2.77128 1.701 2.048 2.76329 1.699 2.045 2.75630 1.697 2.042 2.75040 1.684 2.021 2.70460 1.671 2.000 2.660
120 1.658 1.980 2.617∞ 1.645 1.960 2.576
88