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Predictability within the energy consumption–economic growth nexus: Some evidence from income and regional groups

Authors: Seema Narayan;
Abstract
In re-examining the nexus between energy consumption and economic growth through the predictability framework, we adopt a panel data predictive regression model to examine the possibility of growth, conservative, feedback, or neutrality hypotheses for 135 countries. A predictive regression model is fitted to panels of countries on the basis of location and level of economic development. Findings suggest strong support for the neutrality hypothesis. A developing economy panel (90 countries) favours the conservative hypothesis, although a panel of 32 lower middle-income countries suggests that energy consumption per capita predicts real GDP per capita. These forecasts could provide future policy directions.
Related Organizations
- RMIT University Australia
- RMIT University Australia
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).64 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%

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citations
Citations provided by BIP!
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
popularity
Popularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
64
Top 1%
Top 10%
Top 10%