Bibliographic record
Abstract
Le but de cet article est de montrer que l’introduction de rigidités nominales dans les modèles d’équilibre général intertemporel stochastique permet d’améliorer considérablement les capacités de ces modèles à reproduire les évolutions dynamiques des économies réelles. Pour cela on construit un modèle dynamique qu’on étudie successivement sous les hypothèses suivantes : (a) équilibre walrasien, (b) contrats de salaires à une période, (c) contrats de salaires multipériodiques, (d) contrats multipériodiques de salaires et de prix. À chaque étape une solution analytique du modèle est donnée. On trouve que l’introduction de contrats nominaux, même à durée limitée, améliore de nombreuses corrélations, tandis que l’introduction de contrats multipériodiques permet d’obtenir la réponse persistante de l’output et de l’inflation à certains chocs qui échappait aux modèles traditionnels.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.264 | 0.117 |
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".