Linear and asymmetric impacts of oil price shocks in an oil‐importing and ‐exporting economy: the case of Nigeria
Bibliographic record
Abstract
Abstract Given its economic structure, high‐energy intensity and its simultaneity as an oil‐importing and ‐exporting economy, Nigeria stands out as a special case to study the oil price–macroeconomy relation. By using a structural vector autoregressive model with 10 theoretically derived structural factorizations, this paper studies the linear and asymmetric impacts of oil price shocks on the Nigerian economy, focusing on the supply side effects, wealth transfer effects, inflation effects and real balance effects of oil price shocks between the period 1970Q1 and 2008Q4. Overall, the results show that oil price shocks have asymmetric impacts in one direction (positive) on the Nigerian economy and are not a major determinant of macroeconomic activity in Nigeria. Using Granger causality analysis, the paper also investigates the short‐run impacts of oil price shocks on the Nigerian economy. The results showed that depending on the measurement of oil price shock used, it only Granger‐caused output and inflation in the short run. This revealed that though Nigeria is a major exporter of crude oil, domestic macroeconomic trends do not significantly influence the dynamics of global oil markets, i.e. oil prices are strictly exogenous to the Nigerian economy. These insights have inspired us to recommend that the common practise of national development planning, premised on forecasts of anticipated oil prices should be de‐emphasized in Nigeria.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".