The threshold effects of inflation rate, interest rate, and exchange rate on economic growth in Nigeria
Bibliographic record
Abstract
The study examines the optimum threshold effects of interest rates, inflation rates, and exchange rates in stimulating economic growth in Nigeria. The study adopts the threshold regression technique to ascertain the optimal benchmark beyond which these macroeconomic variables hurt growth. The results of interest rate-economic growth thresholds suggest targeting an average monetary policy rate of 16.5%, a prime lending rate of 20%, and a maximum lending rate of 30%. The results of inflation-economic growth thresholds suggest targeting a headline inflation rate of 9%, while core inflation of 8.7% and food inflation of 12.7% are all growth-enhancing for Nigeria. Lastly, the results of exchange rate-economic growth thresholds suggest that targeting a quarterly depreciation of not more than 2.4% for the official exchange rate and a quarterly depreciation of not more than 2.5% for the unofficial exchange rate are growth-enhancing for Nigeria. The results offer policymakers valuable insights, emphasising the significance of exchange rate management, interest rate management, and inflation rate management in promoting growth and emphasising the necessity of reforms to diversify exports, strengthen institutions, and improve the efficacy of monetary policy. Therefore, the study suggests that the Nigerian government should target the obtainable thresholds for growth to become sustainable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".