An Examination of Foreign Exchange Reserve and Inflation Relationship of Four West African Countries: Evidence from ADRL Model
Bibliographic record
Abstract
The objective is to provide the empirical evidence regarding the relationships between foreign exchange reserves and inflation for four West African countries namely Cote d’Ivoire, Senegal, Ghana and Nigeria. A comparison of empirical evidence is obtained from the Autoregressive distributive lag model (ARDL) proposed by Pesaran, Shin and Smith (2001) using annual data running the period of 1972 to 2014. The empirical result shows that the relationship between the change in foreign exchange reserves and inflation rate is positive for the countries cited above in long run but the overall short run estimation of our model is insignificant at the conventional level. This means that rise in foreign exchange reserves leads to increase the rate of inflation. Regarding our investigation results, the study suggests that governments of these countries cited above should pay more attention to foreign exchange system management by enlarging open market operations. Moreover, they can use sterilization or other policy instruments to reduce foreign exchange reserves to stabilize domestic economy. According our overall empirical results, we propose the following suggestions. First, the central bank expands the base money supply channels and offers a variety of sterilization methods. Second, reinforce coordination of monetary and fiscal policy, and adopt comprehensive measures to promote the international payments balance. As West African countries’ economy is growing rapidly, exchange reserves will still growth and the inflation is an urgent issue too. Therefore, it’s still very important for these countries to reduce the negative effect of the excessive foreign exchange reserves.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".