Regression analysis of oil price return
Contents
1.0 Introduction and Motivation 2
2.0 Methodology 5
2.1. Descriptive Statistics 5
2.2 Matrix of pairwise correlation. 6
3.0 Model Specification 6
3.1 Linear Regression Model. 6
3.2 The Regression Specification Error Test 8
3.3 Non-linear models 9
3.4 Autocorrelation. 10
3.5 Heteroskedasticity Test 10
4.0 Hypothesis Testing 11
5.0 Binary (Dummy) Variables 11
6.0 Conclusion 13
Reference List 13
1.0 Introduction and Motivation
Crude oil is one of the world’s most important natural resources. Over the past six decades or so, crude oil – because of the products derived from it, has become highly indispensable in our everyday lives. Despite being a non-renewable resource, it is still used extensively in power generation. It can be argued that industrialization owes its development to crude oil.
Even though efforts are ongoing in the search for alternative fuels, as of today there is no effective substitute for oil. This is why crude oil, and its price, is so significant for economies all over the globe. Several economies (especially in the Middle East) are largely dependent on oil revenues, while the rest are slaves to its irreplaceability.
For the above-mentioned reasons, it is no surprise that oil price changes feature so prominently in business news segments. Economists, analysts and investors all over the world keenly follow movements in the crude oil market.
The topic was also of particular interest to two of our group members who were raised in oil-producing economies. Additionally, some other factors piqued our interest towards oil prices:
1. Oil prices have major implications for economies. For example, while a decline in oil prices affects the Kingdom of Saudi Arabia to a reasonable extent, it can spell doom for smaller producers like Venezuela. India’s balance of payment crisis of 1991 is also usually considered to have resulted from the oil supply shock of 1990.
2. For many major corporations, for example airline companies, oil
1.0 Introduction and Motivation 2
2.0 Methodology 5
2.1. Descriptive Statistics 5
2.2 Matrix of pairwise correlation. 6
3.0 Model Specification 6
3.1 Linear Regression Model. 6
3.2 The Regression Specification Error Test 8
3.3 Non-linear models 9
3.4 Autocorrelation. 10
3.5 Heteroskedasticity Test 10
4.0 Hypothesis Testing 11
5.0 Binary (Dummy) Variables 11
6.0 Conclusion 13
Reference List 13
1.0 Introduction and Motivation
Crude oil is one of the world’s most important natural resources. Over the past six decades or so, crude oil – because of the products derived from it, has become highly indispensable in our everyday lives. Despite being a non-renewable resource, it is still used extensively in power generation. It can be argued that industrialization owes its development to crude oil.
Even though efforts are ongoing in the search for alternative fuels, as of today there is no effective substitute for oil. This is why crude oil, and its price, is so significant for economies all over the globe. Several economies (especially in the Middle East) are largely dependent on oil revenues, while the rest are slaves to its irreplaceability.
For the above-mentioned reasons, it is no surprise that oil price changes feature so prominently in business news segments. Economists, analysts and investors all over the world keenly follow movements in the crude oil market.
The topic was also of particular interest to two of our group members who were raised in oil-producing economies. Additionally, some other factors piqued our interest towards oil prices:
1. Oil prices have major implications for economies. For example, while a decline in oil prices affects the Kingdom of Saudi Arabia to a reasonable extent, it can spell doom for smaller producers like Venezuela. India’s balance of payment crisis of 1991 is also usually considered to have resulted from the oil supply shock of 1990.
2. For many major corporations, for example airline companies, oil
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