Comparative Analysis of the Stability of Money Demand between Côte d’Ivoire And Ghana: An Application of ARDL Model
Bibliographic record
Abstract
This paper empirically examined the broad money demand function and its stability in two West African countries namely Cote d’Ivoire and Ghana covering the period of 1980 to 2015 using the Autoregressive Distributed Lag (ARDL) Bounds testing procedure. The empirical results confirm the stability of the money demand function and support the choice of M2 as a viable instrument for policy implementation in both countries cited above. The study also demonstrates that a long-run relationship exists between money aggregate (M2) and its determinants during the study period. In fact, the real income tends to be the most significant factor explaining the demand for broad money in both countries. In addition, the overall short run estimation of our model is statistically significant for Cote d’Ivoire and insignificant for Ghana at the conventional level. This means that money demand is stable for Cote d’Ivoire in short run and unstable for Ghana in the same period. It is recommended that monetary policy authorities should continue to implement policies that will reinforce macroeconomic stability and facilitate economic growth.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".