Validation of J-Curve Hypothesis in the Nigerian Non Oil Sector
Bibliographic record
Abstract
J-Curve is a term used to describe the impact of currency devaluation on a country’s balance of trade. In carrying out the study, two objectives stated which are; the validation of the j-curve hypothesis in the short (SR) and long runs (LR). Also, the researcher used the OLS in addition to distributed lag model because exchange rate devaluation does not take effect immediately giving room for lag model effect. The study span from 1985-2014. The study adopted its model from Rose and Yellen (1989) and Rose (1990). The unit root test was used to determine the stationarity of the data. From the results, the OLS result showed delayed J-curve hypothesis. Under the distributed lag (DL), the result shows obedience to the J-curve hypothesis. It is concluded that, policy makers should implement the theory only when the aggregate exchange rate differential between export (non oil) and import (all) is continuously greater than one or equal to one in favour of export (non oil export). One of the recommendations of the study is that policy makers should know that in the current competitive globe, no importing economy will relax to see its economy be a dumping ground (import bias), so superior trade policies should be advocated and implemented. The sustenance of development is one of Nigeria’s challenges. The major policy implication of the study is that Nigeria should diversify the economy, deepen its non oil export and improve its infrastructural base.
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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 | 0.022 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".