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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".