Monetary Policy Under Global and Spillover Uncertainty Shocks: What Do the Bayesian Time-Varying Coefficient VAR, Local Projections, and Vector Error Correction Model Tell Us in Tunisia?
Bibliographic record
Abstract
This study assesses the informational usefulness of several uncertainty metrics in predicting the monetary policy and actual economic activity of Tunisia. We use a Bayesian time-varying vector autoregressive (VAR) model to identify uncertainty shocks sequentially. We complement the analysis with the use of local projections (LPs), a recently flexible and simple method that accommodates the effect of an exogenous intervention on policy outcomes. The findings suggest that shocks to global and spillover uncertainty are important in elucidating the dynamics of industrial production and consumer prices. The impulse response functions (IRFs) show that the central bank does not follow a linear-rule-based monetary strategy. The irreversibility theory, or the “precautionary” behavior, is tested in a vector error correction model (VECM). The money market rate impacts industrial production and consumer prices differently during high versus low uncertainty, depending on the uncertainty variable and the horizon (short versus long run). The effects can be insignificant or significantly dampened during high uncertainty, indicating that conventional monetary policy may be ineffective or less influential. The “wait and see” strategy adopted by economic agents implies that they do not take timely actions until additional pieces of information arrive. While this could not be the sole explanation of our findings, it conveys the importance of dealing with uncertainty in decision-making and highlights the necessity of a clear and credible communication strategy. Importantly, the central bank should complement interest rates with the use of unconventional monetary policy instruments for better flexibility. Our work provides a comprehensive and clear picture of the Tunisian economy and a focal guide for the central bank’s future practices to achieve macroeconomic objectives.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".