Estimating monetary conditions index for selected countries in the ECOWAS region
Bibliographic record
Abstract
Monetary conditions index (MCI) has become an important indicator of monetary policy performance since its introduction by Bank of Canada in the 1990s. This is so, as the index allows central banks to gauge monetary policy stance and effectiveness. This paper builds an MCI for the seven (7) central banks in ECOWAS region, namely The Gambia, Ghana, Guinea, Liberia, Nigeria, Sierra Leone, and the UEMOA zone. The paper estimated a Dynamic Factor Model (DFM), complemented by Principal Component Analysis (PCA) to derive the weights of the variables in the MCI basket. Vector Autoregression (VAR) techniques are used for robustness analysis to assess the effectiveness of the computed MCIs as an indicator of monetary policy stance. In constructing the index, we consider three operating targets, namely interest rate, exchange rate, and reserve money, currently in use by central banks in the region. Key findings are that MCI can be effective to gauge monetary policy stance in West Africa, and thus presents the possibility of an alternative argument in the policy rule of central banks in the region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".