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Record W6911407363 · doi:10.5281/zenodo.10149740

Identification of fluctuations origins in the Business Cycle in Morocco: Reduced DSGE modelling

2023· article· fr· W6911407363 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness cycleDynamic stochastic general equilibriumProductivityInflation (cosmology)Quarter (Canadian coin)Supply shockIdentification (biology)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.088
GPT teacher head0.279
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicCOVID-19 Pandemic Impacts→French-language works237,207→