Does Exchange Rate Volatility Affect Economic Growth in Nigeria?
Bibliographic record
Abstract
This study used monthly data from 2003 to 2017 to analyze the effects of USD/NG₦ exchange-rate volatility on Nigeria’s economic growth. The results from generalized autoregressive conditional heteroscedasticity (GARCH) and vector error correction model (VECM) analyses indicated that USD/NG₦ volatility had a significant effect on the country’s gross domestic product (GDP) growth. The results of the Granger causality/block exogeneity Wald tests and impulse-response functions also indicated that USD/NG₦ volatility had a significant negative effect on the country’s GDP growth. Moreover, USD/NG₦ exchange-rate volatility was found to exhibit short-term unidirectional causality for economic growth. However, a bidirectional relationship was confirmed between narrow money supply and economic growth. Yet, it was also found that the interbank exchange rate, which is a semiofficial Forex window, had little effect on Nigeria’s economic growth—a strong indication that a large portion of the productive sector lacks access to this Forex platform.
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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 | 0.003 |
| 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 | 0.001 |
| 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".