Short-term dynamics and long-term relationship between natural gas consumption and economic growth in Nigeria: an ARDL approach with breaks

Galadima, Mukhtar Danladi; Aminu, Abubakar Wambai; Adam, Ibrahim Muhammad; Adamu, Ibrahim Mohammed; Suleiman, Hassan

doi:10.1007/s11356-022-19457-8

Cited by 4 publications

(1 citation statement)

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“…Similarly, the Chinese economic data for the period of 1989-2020 is investigated in another work employing wavelet coherence method and it is concluded that there is a bidirectional causality relationship between the natural gas consumption and the economic growth [18]. In another study, the Nigerian economy is analyzed for the period of 1981-2009 utilizing ARDL cointegration test where it is observed that there exists a unidirectional causality relationship from the natural gas demand to the economic growth [19]. In another work, the panel cointegration test is employed for the investigation of the economic data of Mexico, Indonesia, South Korea, Turkey and Australia where it is shown that there is bidirectional causality between the natural gas consumption and the economic growth [20].…”

Section: Literature Analysismentioning

confidence: 99%

Forecasting the natural gas demand of Türkiye: A deep learning approach

Tuncsiper¹

2023

World J. Adv. Res. Rev.

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Natural gas is an important input for the production of electricity and other forms of energy. The aim of this study is to investigate the relationship between the natural gas consumption, population and the gross domestic product of Türkiye. For this purpose, deep learning methods are utilized for the modelling of the natural gas consumption for the data covering the period of 1982-2021. First of all, the data required for the modelling are taken from the official sources and then the Granger causality relationship among these variables are studied together with the seasonal and trend decomposition. After assessing the nature of the data that contain high seasonality and nonlinearity, a deep learning network is developed in Python programming language. It is visually demonstrated that the developed deep learning model can be successfully used to describe and forecast the natural gas consumption dependent on the population and the gross domestic product. The performance of the developed deep learning model is also verified using the performance metrics such as the coefficient of determination and the mean absolute percentage error. The developed model is shown to be useful for energy planners and for economists dealing with the energy pricing.

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Section: Literature Analysismentioning

confidence: 99%