Bibliographic record
Abstract
Forecasting at business cycle frequencies is traditionally done with statistically estimated econometric models. This paper takes a different approach, using a calibrated dynamic general equilibrium model in line with the real business cycle literature. First attempts by others have not proven very successful, most probably because the structure of the models were too simple. We take a simple real business cycle model, the Kydland-Prescott (1982) model economy sufficiently simplified to accomodate for the availability of state variables in the data, augmented by government expense shocks. The forecasts are then evaluated with the traditional tools of the econometric forecaster. It is found that the model has potential for making good forecasts when compared to estimated models that are equally parcimonious. Aux fréquences conjoncturelles, la prévision économique est traditionnellement effectuée avec des modèles économétriques évalués de façon statistique. Ce papier prend une approche différente en utilisant un modèle d'équilibre général dynamique calibré dans la lignée de la littérature sur les cycles réels. De premières tentatives par d'autres chercheurs ne se sont pas avérées particulièrement fructueuses, fort probablement en raison d'une structure de modèle trop simple. Ce modèle-ci a jusqu'à 12 variables d'état, après y avoir ajouté une dimension internationale, et est calibré pour une économie mondiale composée de quatre nations (États-Unis, Canada, Japon et Europe), où les cycles conjoncturels sont influencés par des chocs de productivité et des dépenses publiques. Les prévisions sont alors évaluées avec les outils traditionnels de l'économiste. Il s'avère que le modèle est prometteur pour faire de la prévision.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".