Exchange Rate and Inflationary Rate: Do They Interact? Evidence from Nigeria
Bibliographic record
Abstract
The research paper examines the level, nature of association as well as the impact of exchange rate fluctuations on inflationary pressure and other selected macroeconomic indices in Nigeria between 1979 and 2010. Ordinary least squares method in the form of multiple regressions was applied to evaluate their association and impact and Granger Causality technique to evaluate their causality. Co integration procedure was also applied to assess whether their relationships will stand the test of time. A Stationary test was conducted using the Augmented Dickey- Fuller (ADF) tests. The result reveals that exchange rate and inflationary rate are positively related, though not to a very significant extent. This signifies that fluctuations in exchange rate can as well result in a proportionate response in the prevailing inflationary rate. The study reveals that there is no causality in any direction between exchange rate and inflationary rate. Unidirectional causality runs from interest rate to inflation. Interest rate and real GDP have no significant impact on exchange rate in Nigeria as revealed by the study. However, they both have negative relationship with exchange rate. Consequently, the paper recommends that monetary and fiscal policy setters should fashion out strategies to efficiently regulate and effectively manipulate the highly volatile macroeconomic indices in Nigeria in order to grow the economy faster and sustain it, even at the long run.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".