Macroeconomic Variables, the Oil, and the Agricultural Sectors in Nigeria
Bibliographic record
Abstract
The present study examined the impact of the macroeconomic variables and the oil sector on the performance of the agricultural sector between 1981and 2017 in Nigeria. The study adopted a three-stage estimation approach. The initial step in this estimation was the conduct of descriptive statistics and stationarity tests of the variables. Some of the series were stationary at level and some others at the first difference which informed the deployment of the Auto regressive distributed lag (ARDL) technique for model estimation. The third stage was the post-estimation of the model in order ascertain its robustness for predictability and policy formulation. These were the Cumulative Sum Control Chart (CUSUM) stability, Vector Error Correction (VEC) Residual Heteroscedasticity, Breusch-Godfrey Serial Correlation LM, Vector Error Correction Residual Normality, and Vector Error Correction (VEC) Residual Heteroscedasticity tests. The results indicated that contrary to the Dutch disease postulation the oil sector positively impacted the output of the agricultural sector. The influence of exchange rate was also positive. Interest and unemployment rates on the other hand, had negative effects. The rate of inflation and the national output had no impact. The study recommended that the Nigerian government should channel resources towards the agricultural sector to ensure increase in foreign earnings and sufficient domestic production.
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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 | 0.001 | 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".