Assessing the Relationship between Oil Prices, Energy Consumption and Macroeconomic Performance in Malaysia: Co-integration and Vector Error Correction Model (VECM) Approach
Bibliographic record
Abstract
This paper investigates the long run relationship between oil price, energy consumption and macroeconomic performance in Malaysia. The sample period is from (1980-2005) and the time series are subjected to various shortcomings such as autocorrelation, multicollinearity problems and host of other problems; data were first tested for their residuals. The results reveal that there is an evidence for a stable long-run relationship between the oil price, employment, economic growth and growth rate of energy consumption and also substantial short run interactions among them. Also, this paper indicates that the changes of world oil prices also affect the total energy consumption in Malaysia but reverse does not hold in Malaysia context. The linkages and causal effects among the oil price, energy consumption and macroeconomic performance have important policy implications on the benefits of energy conservation and regulation of macroeconomic policy. Given the dominant effects of oil price on energy consumption, better response and right mechanism of energy conservation policies should exist to curb the non renewable energy use and to shift extensively to the inter fuel substitution towards indigenous resources, mainly renewable energy. The most important findings here indicated that the growth of energy used has significant impacts on employment growth and present energy conservation policy especially energy saving policy and energy efficiency initiatives has significant impact on economic growth in Malaysia.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".