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Interactions Between Water Level, Crude Oil, and Hydroelectric Power Generation in Ghana; A Structured Vector Auto Regression Approach

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  • Smart Asomaning Sarpong
  • Akwasi Agyei

Abstract

Countries that suffer disturbances in their power generation are less likely to meet many of the sustainable development goals and general economic growth. This study used a three-variable SVAR model to examine the interactions of water level, crude oil and power generated from the Akosombo hydroelectric generation Dam in Ghana. Data used for this span from January 2010 to December 2019. From the results, none of the three important variables studied was found to be completely independent; dam level and crude oil are adjusted to absorb power generation shocks. All three variables drift away from their normal levels to contain shock before returning to their desired levels at varying time points. It has also been established that Dam water level shocks leads to a negative response in both power generation and crude oil in the short run. Overall, shocks to crude oil explains much of the variability in power generation than shocks to dam water level. These findings convey that there is exist very useful interactions among the three-variables studied in this paper. Policy makers should institute effective measures for early detection and intervention of the short-term power disturbance that characterizes the hydroelectric power generation to ensure a sustainable power and growth of the Ghanaian economy.

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  • Smart Asomaning Sarpong & Akwasi Agyei, 2022. "Interactions Between Water Level, Crude Oil, and Hydroelectric Power Generation in Ghana; A Structured Vector Auto Regression Approach," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(3), pages 1-22, May.
    • Handle: RePEc:ibn:ijspjl:v:11:y:2022:i:3:p:22
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    1. Majid Dehghani & Hossein Riahi-Madvar & Farhad Hooshyaripor & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Kwok-wing Chau, 2019. "Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 12(2), pages 1-20, January.
    2. Karel Mertens & José Luis Montiel Olea, 2018. "Marginal Tax Rates and Income: New Time Series Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(4), pages 1803-1884.
    3. Mumtaz, Haroon & Theodoridis, Konstantinos, 2020. "Dynamic effects of monetary policy shocks on macroeconomic volatility," Journal of Monetary Economics, Elsevier, vol. 114(C), pages 262-282.
    4. Lorde, Troy & Jackman, Mahalia & Thomas, Chrystol, 2009. "The macroeconomic effects of oil price fluctuations on a small open oil-producing country: The case of Trinidad and Tobago," Energy Policy, Elsevier, vol. 37(7), pages 2708-2716, July.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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