Econometric Analysis of Residential Sector Gas Demand Elasticities in Gas Exporting Countries
Bibliographic record
Abstract
This paper examines how residential sector gas demand in gas exporting countries response to changes by taking into consideration the economic variables. For this purpose, the short and long-run price and income elasticities of residential sector gas demand in the GECF countries for 2000 and 2019 are measured. Using Cobb-Douglas functional form, this paper applies the bounds testing approach to co-integrate within the framework of ARDL (Autoregressive Distributed Lag). Findings of this research show that there is a significant long-run relationship in nine GECF countries, including Algeria, Egypt, Iran, Malaysia, Norway, Peru, Russia, Trinidad and Tobago and Venezuela, that use gas as a source of energy in their residential sector. On average, long-rung income elasticity for underlying countries is 2.65, while long-run price elasticity is negative and calculated at 0.79. This shows that in considered gas exporting countries, residential sector gas demand is very sensitive to income policies, while the price policies impact on demand is more limited. Furthermore, short-run income and price elasticities are estimated at 6.99 and -0.02 (near zero) respectively, which implies that natural gas is very inelastic to price, as a result,price policies are unable to make significant changes in demand over the short-term. Meanwhile, as expected short-run price elasticity is lower than long-run elasticities, indicating that gas exporting countries are more responsive to price in the long-term than in the short-term. Finally, it was found that most of the preferred models have empirical constancy over the sample period.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".