The overall theme of the three chapters is the relationship between the prices of natural gas and crude oil, and the factors that cause short run departures from the long run equilibrium price relationship. In the first chapter, we find evidence that the link between natural gas and crude oil prices is indirect, acting through competition at the margin between natural gas and residual fuel oil. We also find that technology is critical to the long run relationship between fuel prices, and short run departures from long run equilibrium are influenced by product inventories, weather, other seasonal factors and supply shocks such as hurricanes.
Once establishing that this long run relationship exists, I extend my research to determine what drives this price relationship in the second and third chapters. Specifically I focus on the driving demand factors that keep these prices moving together. In the power sector, I focus on substitution between natural gas and crude oil on two levels. First, I focus on dispatch decisions and where natural gas generation falls on the stack relative to oil-fired generation. Starting with a translog functional form the paper estimates switching within NERC regions and sub-regions. However, there are some limitations with this functional form and the inability to capture this switching is made clear through an examination of plant-level switching behavior. Therefore, a new functional form is introduced that allows for an S-shaped dispatch as a function of the crude-to-natural gas relationship. This new functional form produces results that are cohesive with the plant-level switching analysis.
The final chapter focuses on demand in the industrial sector. Using Texas natural gas consumption tax data, I take a sectoral look at the demand response to deviations in the long run relationship between crude oil and natural gas. I find that industries with natural gas as a feedstock are far less responsive than industries with high natural gas consumption for fuel, but not as a feedstock. I further find that certain industries, such as oil and gas production, are more responsive to local gas prices while others, such as brick manufacturing, are more responsive to hub prices. This analysis uses both OLS and IV techniques as well as correcting for dynamic panel data problems using Arrellano Bond and bias correction in the least squares dummy variable methodology.
| Identifer | oai:union.ndltd.org:RICE/oai:scholarship.rice.edu:1911/62069 |
| Date | January 2010 |
| Contributors | Hartley, Peter |
| Source Sets | Rice University |
| Language | English |
| Detected Language | English |
| Type | Thesis, Text |
| Format | application/pdf |
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