Short-run and Long-run Gasoline Demand Elasticities: A Case Study of Australia
Bibliographic record
Abstract
This study focuses on examining gasoline demand elasticities in Australia. The short-run and long-run elasticities are estimated based on the panel data from seven capital cities in Australia between 2010 (quarter 3) and 2017 (quarter 4). The paper exploits single-equation panel data results, instrumental variable (IV) estimates, and distributed lag method to demonstrate the short-run and long-run effects of various factors on gasoline demand. We use the world crude oil price as an instrumental variable. The research results indicate short-run and long-run price elasticities of -0.11 and around -0.16 to -0.18, respectively. Although the conclusion is not able to be drawn about the long-run income elasticity of gasoline demand, the short-run finding of 1.35 shows that gasoline demand is income elastic. These findings indicate that the Australian government should increase the amount of gasoline stockpile and inform relevant tax policies on gasoline apart from emission taxes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".