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Marcellus Shale and structural breaks in oil and gas markets: The case of Pennsylvania

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  • Potts, Todd B.
  • Yerger, David B.

Abstract

This paper first documents the rapid increase in Pennsylvania's share of U.S. natural gas production from ongoing development of the Marcellus Shale formation. It does stand to reason that the Marcellus Shale boom has made Pennsylvania natural gas production far more influential on aggregate oil and gas markets than before, but does such a differential effect appear in the data? Can we say, unequivocally, that the Marcellus Shale boom caused Pennsylvania's natural gas output to move the needle on national natural gas prices more so than before the boom occurred? These questions are investigated utilizing a threshold VAR model based on Tsay's (1998) test for unknown break points to investigate how, if at all, Pennsylvania's rapidly growing share of national natural gas production has altered the linkages between Pennsylvania's natural gas production and overall oil and gas prices. Findings indicate a structural break in the impact of Pennsylvania's natural gas production on natural gas prices occurring in early 2009, a date that matches well with the onset of the state's rapid production growth. Pre-break, there is minimal evidence that changes in Pennsylvania's production had a significant effect on the aggregate U.S. natural gas market; but when post-break data is included, an increase in Pennsylvania's production leads to a lower average national price of natural gas, which is transitory and lasts for only a few months. These results provide statistical support to the notion that Pennsylvania has become a substantial component of the U.S. natural gas market at least in the short-run.

Suggested Citation

  • Potts, Todd B. & Yerger, David B., 2016. "Marcellus Shale and structural breaks in oil and gas markets: The case of Pennsylvania," Energy Economics, Elsevier, vol. 57(C), pages 50-58.
    • Handle: RePEc:eee:eneeco:v:57:y:2016:i:c:p:50-58
    DOI: 10.1016/j.eneco.2016.04.017
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    Cited by:

    1. Zhu, Fangfei & Zhu, Yabei & Jin, Xuejun & Luo, Xingguo, 2018. "Do spillover effects between crude oil and natural gas markets disappear? Evidence from option markets," Finance Research Letters, Elsevier, vol. 24(C), pages 25-33.
    2. Kannika Duangnate & James W. Mjelde, 2020. "Prequential forecasting in the presence of structure breaks in natural gas spot markets," Empirical Economics, Springer, vol. 59(5), pages 2363-2384, November.
    3. Feng, Gen-Fu & Wang, Quan-Jing & Chu, Yin & Wen, Jun & Chang, Chun-Ping, 2021. "Does the shale gas boom change the natural gas price-production relationship? Evidence from the U.S. market," Energy Economics, Elsevier, vol. 93(C).
    4. Geng, Jiang-Bo & Ji, Qiang & Fan, Ying, 2016. "The impact of the North American shale gas revolution on regional natural gas markets: Evidence from the regime-switching model," Energy Policy, Elsevier, vol. 96(C), pages 167-178.
    5. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    6. Hupka, Yuri & Popova, Ivilina & Simkins, Betty & Lee, Thomas, 2023. "A review of the literature on LNG: Hubs development, market integration, and price discovery," Journal of Commodity Markets, Elsevier, vol. 31(C).
    7. Potts, Todd B. & Yerger, David B., 2024. "The macroeconomic impact of energy price shocks: Threshold effects and the fracking boom," Resources Policy, Elsevier, vol. 90(C).
    8. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • L95 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Gas Utilities; Pipelines; Water Utilities

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