Macroeconomic and Financial Shocks in African Franc Zone: Exploring the nexus with Vector Autoregression
Bibliographic record
Abstract
This paper analyzes the impulse response functions due to macroeconomic and financial shocks in the African franc zone. To this end, we rely on the estimation of a vector autoregression (VAR) model for a sample of 14 African countries of the franc zone over the 1994-2014 times. Our results show that the evolution of the combined impulse response functions that a shock of the interest rate has a positive impact on snapshot itself, but negative on the other variables. A shock of the consumer price index has a positive impact on the instantaneous interest rate and the change in GDP per capita. But has a negative impact on the global balance as well as itself. A shock of the global balance has a negative higher instantaneous impact on itself but positive on the other variables. Although the variations observed following this shock on the other variables are quite low. A supply shock in the level of GDP per capita has a negative instantaneous impact on the global balance and itself, but positive on the other variables. Moreover, while this shock causes a slight increase in interest rates over the time, the stationary trend evolutions of the price index and decreasing of the global balance is observed. In terms of recommendations, it appears that the interest rate and the global balance are the two central variables that have captured the attention of the economic policymakers in these countries to improve country’s performance on the pathway of progress.
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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 | 0.001 |
| 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 | 0.001 | 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".