Panel Cointegration and Pooled Mean Group Estimations of Energy-Output Dynamics in South Asia
Bibliographic record
Abstract
This study employs the panel cointegration and pooled mean group (PMG) techniques to examine the long run relationships between energy consumption and GDP for 5 South Asian countries from 1981 to 2009. Unit root and panel cointegration tests find a long run relationship between energy consumption and GDP after allowing for country-specific effect. Furthermore, we use the PMG technique to identify the magnitude of this relationship. Our results are consistent with the theory that suggests a role of energy use in GDP. On average, a 1% increase in energy consumption leads to a 0.61% increase in the long run GDP in South Asia from 1981 to 2009. Hence, it is apparent that energy is an important component to maintain the economic activities in these countries. These results have important implications for policy makers of South Asian countries which have experienced magnificent growth performance along with a sharp rise in consumption demand for energy in last few decades.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".