Forecasting the probability of US recessions: a Probit and dynamic factor modelling approach
Bibliographic record
Abstract
Abstract Quantifying the probability of U.S. recessions has become increasingly important since August 2007. In a data-rich environment, this paper is the first to apply a Probit model to common factors extracted from a large set of explanatory variables to model and forecast recession probability. The results show the advantages of the proposed approach over many existing models. Simulated real-time analysis captures all recessions since 1980. The proposed model also detects a significant jump in the next six-month recession probability based on data up to November 2007, one year before the formal declaration of the recent recession by the NBER. Quantifier la probabilité des récessions américaines est devenu de plus en plus important depuis août 2007. Dans un environnement où l’information foisonne, ce texte est le premier à appliquer la technique probit à des facteurs communs extraits d’un vaste ensemble de variables explicatives pour modéliser et prédire la probabilité de récession. Les résultats montrent les avantages de l’approche utilisée sur plusieurs des modèles en vogue. Une analyse de simulation en temps réel saisit toutes les récessions depuis 1980. Le modèle proposé détecte aussi un saut significatif dans la probabilité de récession dans les prochains six mois à partir des données disponibles jusqu’à novembre 2007 – un an avant que le NBER n’annonce formellement le commencement de la récente récession.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".