Energy market dynamics and the role of fiscal policy in oil‐exporting countries: a TVAR approach
Bibliographic record
Abstract
Abstract While realising the macroeconomic significance of oil price fluctuations, this research examines the role of fiscal policy under changing dynamics of energy market for selected oil‐exporting countries. We specify a non‐linear threshold structural vector autoregression model which constitutes policy variables such as general government expenditures and primary fiscal balance and macroeconomic indicators such as real GDP growth and the inflation rate. To capture the energy market dynamics, this research selects Brent crude oil price as threshold variable and segregates the sample period 1991‐2019 as ‘high’ and ‘low’ oil price regimes. While using non‐linear generalised impulse response functions, we find that under higher oil price regime, an increase in government expenditures and reduction in fiscal deficit have larger multiplier effect to enhance output growth in most of the sampled countries. In addition, this research identifies larger inflationary effects of an increase in government expenditures and fiscal deficit under higher oil price regime for all countries except Canada. However, under a higher oil price regime, a fiscal deficit induced output growth, and under a lower oil price regime, a reduction in government expenditure brings inflation in Saudi Arabia. Furthermore, this research provides an alternative measure of threshold crude oil price for the sampled countries to their accounting‐based concept of fiscal break‐even price.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".