On Agricultural Performance amidst Macroeconomic Instability in Nigeria; Autoregressive Distributed Lagged Modelling (2010Q1-2017Q4)
Bibliographic record
Abstract
The interaction among macroeconomic indicators causes shock among themselves and by extension shocks on other macroeconomic variables including agricultural performance. This study investigated agricultural performance amidst macroeconomic instability in Nigeria. Data on the study variables spanning from first quarter of 2010 to the fourth quarter of 2017 was sourced from the Statistical Bulletin of the Central Bank of Nigeria. Diagnostic checks revealed that the variables were integrated of order I(0) and I(1) hence the used of the Autoregressive Distributed Lagged model The cointegration bounds test indicated a long run cointegration consequently the ECM which results showed a correct sign, significant effect and 40.1% speed of adjustment. Empirical, results also indicated that; 91.3% variation in agricultural sector performance was explained by the adopted explanatory variables of the parsimonious model (R2 =0.913). Particularly, changes in the fourth lag of agricultural sector performance, current period exchange rate, the first, second and third lag of exchange rate were significant determinant of agricultural performance within the period under review.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".