Identification of fluctuations origins in the Business Cycle in Morocco: Reduced DSGE modelling
Bibliographic record
Abstract
Résumé Cet article explore les origines des fluctuations cycliques au Maroc en utilisant un modèle d'équilibre général stochastique dynamique (DSGE) réduit privilégiant l'examen des chocs résultant des variations de la demande, de l'offre, ainsi que de la politique monétaire. Pour étayer notre étude, nous mobilisons des données s'étalant du premier trimestre 2007 au quatrième trimestre 2022. Les résultats obtenus soulignent le rôle prépondérant des perturbations du côté de l'offre dans les variations de la production et de l'inflation au Maroc L'économie réagit de manière plus marquée aux facteurs liés l'offre, notamment la productivité, les interruptions de la chaîne d'approvisionnement et la dynamique de l'offre agricole. Les implications de ces constatations sont d'importance pour les décideurs politiques, mettant en évidence la nécessité d'ajuster et d'adapter leur politique en vue de stabiliser l'économie et de promouvoir la croissance économique. Mots clés : Cycle économique, DSGE réduit, Origines de fluctuations, Chocs d'offre. Abstract This article explores the origins of cyclical macroeconomic fluctuations in Morocco. A reduced Dynamic Stochastic General Equilibrium (DSGE) model is used to identify these fluctuations, with a specific focus on demand, supply and monetary policy shocks. The study leverages data spanning from the first quarter of 2007 to the fourth quarter of 2022.The results indicates that supply pertubations predominantly drive production and inflation fluctuations within Morocco. Our economy tends to react more sensitively to supply-side factors, such as productivity fluctuations, supply chain interruptions and agricultural supply dynamics. The implications of these findings are significant for policy-makers, revealing the necessity to adjust and adapt their policies in order to stabilise the economy and promote economic growth. Keywords : Business cycle, Reduced DSGE, Fluctuations origins, Supply shocks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".