Leveraged Bootstrap Test of Volatility: A Novel Approach to the Energy Consumption and Economic Growth Puzzle
Bibliographic record
Abstract
The main purpose of this research is to examine the causal relationship between the Energy industry and nine other industries by use of volatility instead of returns. Existing literatures find a causal relationship by use of stock returns, however, we find that using volatility reveals a causal relationship that might not otherwise be revealed through returns alone. Since the existing literature shows that volatility of stock prices is informative, we apply a Granger causality test by use of a leveraged bootstrap test developed by Hacker and Hatemi (2006) to investigate the causal behavior of the volatility. Our results show that volatility of the Energy industry causes volatility in two other industries- Industrials and Health Care. Also, the Energy industry market is affected by the Materials, Consumer Staples and Utilities industries. This finding is substantially different from the findings of previous research, and provides a novel approach to analyzing and solving the energy consumption and economic growth puzzle.
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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 | 0.012 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".