Modèles à changements de régimes et mémoire longue : problèmes économétriques, inférence statistique et applications en économie et finance
Bibliographic record
Abstract
Recemment de nombreux travaux theoriques et empiriques ont mis l'accent sur certains problemes souvent rencontres lors de l'uilisation des modeles a changement de regimes : i) Il apparait souvent que differents modeles a changements de regimes sont significatifs lorsque l'hypothese nulle de linearite est rejetee. Ii) De meme, l'analyse des fonctions d'autocorrelations des modeles a changements de regimes montre la presence de comportements de longue memoire. Ces deux problemes ont de fortes implications sur le choix du bon modele, et par consequent sur l'analyse de l'impact des chocs et la bonne qualite des previsions. L'objectif de ce travail de these est d'apporter des solutions a ces problemes. Precisement, nous proposons deux nouvelles methodes dans le but de differencier les modeles a changements de regimes de ceux a memoire longue. La premiere methode est basee sur la comparaison du parametre de memoire longue d avant et apres filtrage des points de ruptures. La deuxieme methode est basee sur l'evolution temporelle du parametre d. La validite, sur le plan empirique, de ces deux methodes est faite sur la base, d'une part des series de rendements de sept grandes entreprises du CAC40 et de l'indice lui-meme et d'autre part sur la base des series du taux d'inflation des Etats-Unis, du Canada, de la France et de l'Italie. Enfin, nous avons etudie empiriquement l'efficience des marches des changes sur la base de l'estimation de modeles a changement de regime et de processus a memoire longue.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".