Petronas Sales Performance 2

CHAPTER 1 INTRODUCTION

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CHAPTER 1: INTRODUCTION

1.1 BACKGROUND OF COMPANY 1

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 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.

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Petronas Malaysia: Corporate News 2005 about PETRONAS

2

2

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.

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PETRONAS Group Sustainability Reports 2007

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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.

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OIL & 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.

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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. 4

The 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.

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LAUNCH OF THE MALAYSIAN OIL & GAS SERVICES COUNCIL (MOGSC) - UCAPAN TIMBALAN PERDANA MENTERI

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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.

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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.

PRICE OF OIL AND GAS IN MALAYSIA SALES PERFORMANCES OF PETRONAS

PRODUCTION OF OIL AND GAS IN MALAYSIA

INDEPENDENT VARIABLES

DEPENDENT VARIABLES

Figure 1.1 Schematic 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

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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. 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.

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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.

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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.

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 14

How well or badly you do something; how well or badly something works; the country economic performance.

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. 15

8. Volume The amount or quantity of something, especially when great.

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 16

Statistics a measure of the relation between the mean value of one variable and corresponding values of other variables.

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.

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CHAPTER 2 LITERATURE REVIEW

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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, 19

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. (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 21

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 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.

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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

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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, factfinder directory listing the addresses, and contact details of 1115 companies worldwide. (Oil and Gas Industry, 2006)

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 24

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 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.

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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

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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 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 on27

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 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 28

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.

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 29

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 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 30

(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. 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 31

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% 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 32

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 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 33

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 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.

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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.

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 35

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.

CHAPTER 3 DATA 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 37

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.

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.

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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 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.

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3.4.3.1 Linear Regression

Y= α + β Price + β Production Y α β Price β Production

= = = =

dependent Variable (PETRONAS sales) the constant value independent variable (Price Oil and Gas in Malaysia) 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

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 tstatistic 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.

40

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.

CHAPTER 4 41

DATA ANALYSIS INTERPRETATION

AND

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

42

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

Mean Median Maximum Minimum Std. Dev. Observations

PETRONAS OIL SALES PRODUCTION 14256084 678.7451 12451080 664.0000 19496370 790.0000 8970494. 530.0000 3877151. 69.98224 51 51

OIL PRICE 50.51039 51.97000 78.16000 27.08000 16.05210 51

GAS PRODUCTION 279090.5 279002.0 326325.0 203230.0 25560.88 51

GAS PRICE 167037.5 167625.0 213522.0 123684.0 17551.03 51

Table 4.1 Descriptive 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 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.

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4.2 UNIT ROOT TEST ANALYSIS

Level PP 0.6362

Variable SALES

ADF 0.7313

GAS PRICE

1.6488

GAS PRODUCTION OIL PRICE

6.0795 * 1.0943

3.9475 * 6.1883 * 0.9332

OIL PRODUCTION

1.1444

1.1444

KPSS 0.6557 ** -

1st Difference ADF PP 5.5427 7.4762 * * 4.4456 14.7580 * * 8.6188 21.7813 * * 6.8344 8.7205 * * 6.5616 6.5689 * *

* denotes 99%of significant level ** denotes 95% of significant level ***denotes 90% of significant level Table 4.2 Unit 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 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.

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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.

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 45

Method: Least Squares Sample: 2003M01 2007M03 Included observations: 51 Coefficient

Std. Error

t-Statistic

Prob.

16.78010 -0.651327 0.621256 0.266919 -0.153948

2.787780 0.194819 0.062715 0.142579 0.155755

6.019161 -3.343234 9.905956 1.872077 -0.988395

0.0000 0.0017 0.0000 0.0676 0.3281

C OIL PRODUCTION OIL PRICE GAS PRODUCTION GAS PRICE

Table 4.3 Multiple Regression Analysis of the variables 2003-2007: 51 Observations

4.3.1 Linear Function

PETRONAS Sales

= 16.7801 + 0.6212 Oil Price – 0.1539 Gas Price – 0.6513 Oil Production + 0.2669 Gas Production

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.

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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.

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. Degree of Freedom 1)

= (number of observation – number of independent variables – = (51 – 4 – 1) Figure 4.2 Degree of Freedom

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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 Oil Production Gas Production Oil Price Gas Price T-critical = 2.021

T-statistic -3.343234 1.872077 9.905956 -0.988395

T-critical > 2.021 – significant < 2.021 – insignificant > 2.021 – significant < 2.021 – insignificant

Table 4.4 T-Statistic between all independent variables 2003-2007: 51 Observations 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

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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)

R-squared 0.902543

Adjusted R-squared 0.894069

Table 4.5 Coefficient 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

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independent variables and only can be explained by other variable. I also found that adjusted R squared is 0.894069.

CHAPTER 5 CONCLUSION & RECOMMENDATION 50

CHAPTER 5: CONCLUSION AND RECOMMENDATION

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 51

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 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.

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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 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.

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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.

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 54

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 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.

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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.

REFERENCES 56

REFERENCES

Ahmad Al-Kandari, et.al (2007), ‘Oil prices and stock markets in GCC countries: new evidence from nonlinear cointegration analysis’, Managerial Finance, Volume 33 Number 7 2007 pp. 449-460 Breitung J (2001), ‘Rank tests for nonlinear cointegration’, Journal of Business and Economic Statistics, Vol. 19 pp.331-40. Breitung, J, et.al (1997), ‘Rank tests for unit roots’, Journal of Econometrics, Vol. 81 pp.727. Edwards J.D (1997), ‘Crude oil and alternate energy production forecasts for the twentyfirst century; the end of the hydrocarbon era’, University of Colorado, Department of Geological Sciences, Energy and Minerals Applied Research Center (EMARC), Boulder, CO, United States, v. 81; no. 8; p. 1292-1305

57

Forbes.K.J (2002), “HOW DO LARGE DEPRECIATIONS AFFECT PERFORMANCE?” National Bureau of Economic Research, pp. 24–26.

FIRM

Gali J, et.al (2007), ‘The Macroeconomic Effects of Oil Price Shocks: Why are the 2000s so different from the 1970s?’, National Bureau Economic Research, pp.64-66 Gertler M, et.al (2000), “Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory,” Quarterly Journal of Economics, 115. pp. 147-180. Hamilton J.D (1983), ‘Oil and the macroeconomy since World War II’, Journal of Political Economy, Vol 91, pp. 228-248. Hamilton J.D (1985), ‘Historical causes of postwar oil shocks and recessions’, Energy Journal, Vol 6, pp. 97-116. Hamilton J.D (1996), ‘This is what happened to the oil price-macroeconomy relationship’, Journal of Monetary Economics, Vol 38, pp. 215 220 Hamilton J.D (2005), “Oil and the Macroeconomy”, Department of Economics, Vol 0508, pp. 2 Hooker, et.al. (1996), “What Happened to the Oil Price-Macroeconomy Relationship?,” Journal of Monetary Economics, Vol 38, pp. 195-213. Hugh A, et.al (1998), ”Third year of double digit growth”, World Oil; v. 219 (Feb. 1998) pp. 57-8+. John S, et.al (1996), “The impact of mineral rights and oil and gas activities on agricultural land values.” The Appraisal Journal, v. 64, p. 67-75. Kenneth D. Bailey (1982). Methods of Social Research, 2nd edition. The Free Press, A Division of Macmillan Publishing Co., Inc. Kilian L, et.al (2004), ‘Oil and the Macroeconomy since the 1970s’, The Journal of Economic Perspectives, Vol. 18, No. 4, pp. 115-134 Mork A (1989), ‘Oil and the macroeconomy when prices go up and down: An extension of Hamilton's results’, Journal of Political Economy, Vol 91, pp. 740-744 Oil and Gas Industry (2006), “Global Outlook 2006”, Global Industry Analysts, Inc., July 2006, Pages: 175 Pain. N, et.al (1997), “EXPORT PERFORMANCE AND THE ROLE OF FOREIGN DIRECT INVESTMENT”, National Institute of Economic And Social Research & Maastricht Economic Research Institute on Innovation and Technology, pp. 26. White D.S, et.al (1998), “Measuring export performance in service industries”, International Marketing Review, MCB University Press, 0265-1335 Vol. 15 No. 3, 1998, pp. 188-204

58

White D, et.al (1994), “The Revolution in Upstream Oil and Gas”, The McKinsey Quarterly, No. 3, 1994 Razmahwata bin Mohamad Razalli (2005) “THE MALAYSIAN OIL AND GAS INDUSTRY : An Overview”, THE MONTHLY BULLETIN OF THE INSTITUTION OF ENGINEERS, MALAYSIA, KDN PP 1050/02/2005 ISSN 0126-9909 BIL.2005 N0.01 JANUARY 2005, pg 8 Sachs, J.D, et.al (1997), “Understanding China’s Economic Performance”, NATIONAL BUREAU OF ECONOMIC RESEARCH, pg 44. Samad bin Solbai, Chairman, Oil, Gas & Mining Technical Division (2005), “MALAYSIAN ENGINEERS AND THE OIL & GAS INDUSTRY”, THE MONTHLY BULLETIN OF THE INSTITUTION OF ENGINEERS, MALAYSIA, KDN PP 1050/02/2005 ISSN 0126-9909 BIL.2005 N0.01 JANUARY 2005, pg 5 Sekaran U. (2003), “Research Methods for Business”, New York: John Willey& Sons 4th edition. Youngquist W, et.al (1999), “Encircling the Peak of World Oil Production”, Natural Resources Research, ISSN: 1520-7439, Volume 8, Number 3, p. 219-232 YI WEN, et.al (2007),’Understanding the Large Negative Impact of Oil Shocks’, Journal of Money, Credit and Banking 39, Vol 4 pp. 925–944

APPENDICES 59

Appendix 1: VARIABLES DATA VARIABLES

SALES

OIL PRODUCTION

GAS PRODUCTION

CURRENCY/UNIT

RM

METRIC/TONNES

CUBIC/FEET

2003M01 2003M02 2003M03 2003M04 2003M05 2003M06 2003M07 2003M08 2003M09 2003M10 2003M11 2003M12 2004M01 2004M02 2004M03 2004M04

8970494 8970494 8970494 9830365 9830365 9830365 9830365 9830365 9830365 9830365 9830365 9830365 9830365 9830365 9830365 1245108

726 726 705 726 732 742 742 742 742 748 753 769 730 730 740 750

302755 209864 251183 285494 286306 267516 269732 274853 258641 276527 283656 310988 275619 278242 291443 268001

60

OIL PRICE US DOLLAR/BARREL 32.45 34.52 27.87 28.28 27.14 27.08 28.67 30.17 28.51 31.87 30.98 32.03 33.94 35.47 35.11 35.57

GAS PRICE RM/CUBIC/FEET 167990 136633 161910 151421 144605 145661 148550 143223 140396 153313 161512 174372 165094 152769 159377 151425

2004M05 2004M06 2004M07 2004M08 2004M09 2004M10 2004M11 2004M12 2005M01 2005M02 2005M03 2005M04 2005M05 2005M06 2005M07 2005M08 2005M09 2005M10 2005M11 2005M12 2006M01 2006M02 2006M03 2006M04 2006M05 2006M06 2006M07 2006M08 2006M09 2006M10 2006M11 2006M12 2007M01 2007M02

0 1245108 0 1245108 0 1245108 0 1245108 0 1245108 0 1245108 0 1245108 0 1245108 0 1245108 0 1245108 0 1245108 0 1656792 0 1656792 0 1656792 0 1656792 0 1656792 0 1656792 0 1656792 0 1656792 0 1656792 0 1656792 0 1656792 0 1656792 0 1949637 0 1949637 0 1949637 0 1949637 0 1949637 0 1949637 0 1949637 0 1949637 0 1949637 0 1949637 0 1949637

760

258207

40.27

155682

760

262732

38.48

152265

760

265429

41.35

144215

760

272331

49.13

158277

760

254567

47.87

123684

790

259063

53.35

167977

764

279002

46.3

172650

759

267331

40.61

199209

664

251738

49.24

190566

637

250699

51.97

167625

632

282910

58.32

213522

595

220855

55.86

179697

554

302573

48.94

169620

602

296173

57.37

165507

626

274682

58.63

160127

656

287615

68.31

169787

664

294790

67.4

163515

657

303345

59.95

177487

635

314869

57.61

176049

651

310492

62.36

183523

639

326325

71.41

179099

641

203230

64.98

160476

611

260396

65.96

198121

595

298704

76.76

179697

530

280782

72.18

165599

593

300692

71.96

172578

602

291204

78.16

172363

599

305358

77.54

174378

614

292792

67.38

163902

626

296114

62.31

180507

652

307932

60.77

182091

651

273950

66.29

197225

594

315196

58.26

188043

590

275197

62.87

170470

61

2007M03

0 1949637 0

590

305523

Appendix 2: VARIABLES CHART

62

66.22

185129

SALES

OPRODUCTION

20,000,000

800

18,000,000

760 720

16,000,000

680 14,000,000 640 12,000,000

600

10,000,000

560

8,000,000

520 2003

2004

2005

2006

2003

OPRICE

2004

2005

2006

GPRODUCTION

80

340,000

70

320,000 300,000

60

280,000 50 260,000 40

240,000

30

220,000

20

200,000 2003

2004

2005

2006

2003

GPRICE 220,000 200,000 180,000 160,000 140,000 120,000 2003

2004

2005

2006

Appendix 3: DESCRIPTIVE TABLE

63

2004

2005

2006

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

SALES 14256084 12451080 19496370 8970494. 3877151. 0.111346 1.486371

OPRODUCTION 678.7451 664.0000 790.0000 530.0000 69.98224 -0.162914 1.674109

OPRICE 50.51039 51.97000 78.16000 27.08000 16.05210 -0.000652 1.652151

GPRODUCTION 279090.5 279002.0 326325.0 203230.0 25560.88 -0.833472 4.024354

GPRICE 167037.5 167625.0 213522.0 123684.0 17551.03 0.140850 3.162238

Jarque-Bera Probability

4.973913 0.083163

3.961317 0.137978

3.860483 0.145113

8.134502 0.017124

0.224561 0.893794

Sum Sum Sq. Dev.

7.27E+08 7.52E+14

34616.00 244875.7

2576.030 12883.50

14233618 3.27E+10

8518913. 1.54E+10

Observations

51

51

51

51

51

Appendix 4: DESCRIPTIVE HISTOGRAM

64

14

Series: SALES Sample 2003M01 2007M03 Observations 51

12 10 8 6 4 2

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

14256084 12451080 19496370 8970494. 3877151. 0.111346 1.486371

Jarque-Bera Probability

4.973913 0.083163

0 10000000

12500000

15000000

17500000

14

Series: OPRODUCTION Sample 2003M01 2007M03 Observations 51

12 10 8 6 4 2

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

678.7451 664.0000 790.0000 530.0000 69.98224 -0.162914 1.674109

Jarque-Bera Probability

3.961317 0.137978

0 525 550 575 600 625 650 675 700 725 750 775 800

6

Series: OPRICE Sample 2003M01 2007M03 Observations 51

5 4 3 2 1 0 30

40

50

60

70

65

80

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

50.51039 51.97000 78.16000 27.08000 16.05210 -0.000652 1.652151

Jarque-Bera Probability

3.860483 0.145113

10

Series: GPRODUCTION Sample 2003M01 2007M03 Observations 51

8

6

4

2

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

279090.5 279002.0 326325.0 203230.0 25560.88 -0.833472 4.024354

Jarque-Bera Probability

8.134502 0.017124

0 200000 220000 240000 260000 280000 300000 320000

9

Series: GPRICE Sample 2003M01 2007M03 Observations 51

8 7 6 5 4 3

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

167037.5 167625.0 213522.0 123684.0 17551.03 0.140850 3.162238

Jarque-Bera Probability

0.224561 0.893794

2 1 0 120000

140000

160000

180000

200000

66

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:

-3.568308

1% level 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

-0.009584

S.D. dependent var

794314.4

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

Durbin-Watson stat

2.120717

Adjusted R-squared S.E. of regression

F-statistic

0.534851

Prob(F-statistic)

0.468130

67

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

Durbin-Watson stat

1.292687

F-statistic

7.161130

Prob(F-statistic)

0.000015

68

PP-LEVEL Null Hypothesis: SALES has a unit root Exogenous: Constant Bandwidth: 4 (Newey-West using Bartlett kernel)

Phillips-Perron test statistic Test critical values:

1% level

Adj. t-Stat

Prob.*

-0.636219

0.8528

-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

-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

Durbin-Watson stat

2.120717

Adjusted R-squared

F-statistic

0.534851

Prob(F-statistic)

0.468130

69

PP-1ST DIFFERENCE Null Hypothesis: D(SALES) has a unit root Exogenous: Constant Bandwidth: 4 (Newey-West using Bartlett kernel)

Phillips-Perron test statistic Test critical values:

Adj. t-Stat

Prob.*

-7.476241

0.0000

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

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

Durbin-Watson stat

2.011578

F-statistic

54.42913

Prob(F-statistic)

0.000000

70

0.000000

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

Durbin-Watson stat

1.943668

F-statistic

4.316919

Prob(F-statistic)

0.001477

71

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

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

Durbin-Watson stat

1.860123

F-statistic

16.51393

Prob(F-statistic)

0.000000

72

46.12821

PP-LEVEL Null Hypothesis: GPRICE has a unit root Exogenous: Constant Bandwidth: 4 (Newey-West using Bartlett kernel)

Phillips-Perron test statistic Test critical values:

1% level

Adj. t-Stat

Prob.*

-3.947578

0.0035

-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

Durbin-Watson stat

2.198728

F-statistic

14.61362

Prob(F-statistic)

0.000380

73

PP-1ST DIFFERENCE Null Hypothesis: D(GPRICE) has a unit root Exogenous: Constant Bandwidth: 6 (Newey-West using Bartlett kernel)

Phillips-Perron test statistic Test critical values:

Adj. t-Stat

Prob.*

-14.75806

0.0000

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

Durbin-Watson stat

2.130884

F-statistic

140.1244

Prob(F-statistic)

0.000000

74

KPSS-LEVEL Null Hypothesis: GPRICE is stationary Exogenous: Constant Bandwidth: 5 (Newey-West using Bartlett kernel) LM-Stat. Kwiatkowski-Phillips-Schmidt-Shin test statistic Asymptotic critical values*:

0.655798 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.

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

C

Durbin-Watson stat

0.956463

75

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:

-3.568308

1% level 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

Durbin-Watson stat

1.722524

F-statistic

36.96066

Prob(F-statistic)

0.000000

76

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

Durbin-Watson stat

2.113782

F-statistic

73.55302

Prob(F-statistic)

0.000000

77

PP-LEVEL Null Hypothesis: GPRODUCTION has a unit root Exogenous: Constant Bandwidth: 3 (Newey-West using Bartlett kernel)

Phillips-Perron test statistic Test critical values:

1% level

Adj. t-Stat

Prob.*

-6.188342

0.0000

-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

Durbin-Watson stat

1.722524

F-statistic

36.96066

Prob(F-statistic)

0.000000

78

PP-1ST DIFFERENCE Null Hypothesis: D(GPRODUCTION) has a unit root Exogenous: Constant Bandwidth: 11 (Newey-West using Bartlett kernel)

Phillips-Perron test statistic Test critical values:

Adj. t-Stat

Prob.*

-21.78132

0.0001

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

Durbin-Watson stat

2.360075

F-statistic

148.1916

Prob(F-statistic)

0.000000

79

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

1045.124

Schwarz criterion

6.241167

Hannan-Quinn criter.

6.144161

Durbin-Watson stat

2.062405

Sum squared resid Log likelihood

-142.0456

F-statistic

2.060817

Prob(F-statistic)

0.119175

80

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

1073.572

Schwarz criterion

6.187373

Hannan-Quinn criter.

6.114618

Durbin-Watson stat

2.059118

Sum squared resid Log likelihood

-142.6901

F-statistic

34.60574

Prob(F-statistic)

0.000000

81

PP-LEVEL Null Hypothesis: OPRICE has a unit root Exogenous: Constant Bandwidth: 8 (Newey-West using Bartlett kernel)

Phillips-Perron test statistic Test critical values:

1% level

Adj. t-Stat

Prob.*

-0.933209

0.7694

-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

F-statistic

1.383475

Prob(F-statistic)

0.245311

Hannan-Quinn criter.

6.136112

Durbin-Watson stat

2.191680

82

PP-1ST DIFFERENCE Null Hypothesis: D(OPRICE) has a unit root Exogenous: Constant Bandwidth: 11 (Newey-West using Bartlett kernel)

Phillips-Perron test statistic Test critical values:

Adj. t-Stat

Prob.*

-8.720519

0.0000

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

Hannan-Quinn criter.

6.168723

Durbin-Watson stat

2.006471

Log likelihood

-148.4159

F-statistic

60.42602

Prob(F-statistic)

0.000000

83

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:

-3.568308

1% level 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

Hannan-Quinn criter.

9.492310

Durbin-Watson stat

1.843957

Log likelihood

-234.5796

F-statistic

1.309747

Prob(F-statistic)

0.258115

84

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

Hannan-Quinn criter.

9.539106

Durbin-Watson stat

1.991438

Log likelihood

-230.9903

F-statistic

43.05534

Prob(F-statistic)

0.000000

85

PP-LEVEL Null Hypothesis: OPRODUCTION has a unit root Exogenous: Constant Bandwidth: 0 (Newey-West using Bartlett kernel)

Phillips-Perron test statistic Test critical values:

1% level

Adj. t-Stat

Prob.*

-1.144442

0.6910

-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

Hannan-Quinn criter.

9.492310

Durbin-Watson stat

1.843957

Log likelihood

-234.5796

F-statistic

1.309747

Prob(F-statistic)

0.258115

86

PP-1ST DIFFERENCE Null Hypothesis: D(OPRODUCTION) has a unit root Exogenous: Constant Bandwidth: 2 (Newey-West using Bartlett kernel)

Phillips-Perron test statistic Test critical values:

Adj. t-Stat

Prob.*

-6.568930

0.0000

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

Hannan-Quinn criter.

9.539106

Durbin-Watson stat

1.991438

Log likelihood

-230.9903

F-statistic

43.05534

Prob(F-statistic)

0.000000

87

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 OPRODUCTION OPRICE GPRODUCTION GPRICE

16.78010 -0.651327 0.621256 0.266919 -0.153948

2.787780 0.194819 0.062715 0.142579 0.155755

6.019161 -3.343234 9.905956 1.872077 -0.988395

0.0000 0.0017 0.0000 0.0676 0.3281

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

0.902543 0.894069 0.090862 0.379769 52.58457 106.5011 0.000000

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat

16.43515 0.279170 -1.866062 -1.676667 -1.793688 0.954378

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

88

Appendix 7: T-DISTRIBUTION TABLE

df 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 40 60 120 ∞

10% 6.314 2.920 2.353 2.132 2.015 1.943 1.895 1.860 1.833 1.812 1.796 1.782 1.771 1.761 1.753 1.746 1.740 1.734 1.729 1.725 1.721 1.717 1.714 1.711 1.708 1.706 1.703 1.701 1.699 1.697 1.684 1.671 1.658 1.645

5% 12.706 4.303 3.182 2.776 2.571 2.447 2.365 2.306 2.262 2.228 2.201 2.179 2.160 2.145 2.131 2.120 2.110 2.101 2.093 2.086 2.080 2.074 2.069 2.064 2.060 2.056 2.052 2.048 2.045 2.042 2.021 2.000 1.980 1.960

89

1% 63.657 9.925 5.841 4.604 4.032 3.707 3.499 3.355 3.250 3.169 3.106 3.055 3.012 2.977 2.947 2.921 2.898 2.878 2.861 2.845 2.831 2.819 2.807 2.797 2.787 2.779 2.771 2.763 2.756 2.750 2.704 2.660 2.617 2.576