Effects of Bilateral Real Exchange Rate on Sino-Nigeria Trade: An ARDL Cointegration Approach
Bibliographic record
Abstract
This research is motivated to scrutinise the effects of real bilateral exchange rate fluctuation on China-Nigeria bilateral trade, taking into consideration volatility and third country’s bilateral exchange rate effect to determine their consequences. Due to its robustness in time series analyses, an ARDL approach to co-integration was used to determine the long-and short-runs effects. Both export and import were considered separately. Outcome revealed that Nigeria’s import from China responds negatively to real bilateral exchange rate increase just as it does to its volatility. Her export to China reacts positively on both front, most especially in the short-run. Japan was integrated as a third country in this research due to her competing presence in Nigerian market. Third country’s real bilateral exchange rate play prominent but negative role in China-Nigeria trade, and is mostly effective in the long-run. With the absolute value of the co-efficient of real bilateral exchange rate greater than one, depreciating the Naira against the Renminbi will tend to ameliorate the negative balance of trade Nigeria has with China. Finally, democratic regime was found to be very essential in enhancing international business.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".