Variable Rational Partisan Business Cycles: theory and some evidence
Bibliographic record
Abstract
The Variable Rational Partisan Business Cycle model is developed, where agents face uncertainty regarding the timing and outcome of the next election. The model predicts that partisan influences on the economy persist throughout the government’s rule and are further influenced, in the opposite direction, by which party ruled in the previous period. Party popularity also has a causal effect on the business cycle. Finally, the effects from changes in election timing expectations are dependent on which party ruled in the previous period. Empirical results for output and unemployment in Canada, Germany, and United Kingdom yield mixed support for the model. JEL Classification: D72, E32 Le modèle des cycles d’affaires variables fondé sur le comportement du partisan rationnel : théorie et résultats. Le modèle de cycles d’affaires variables fondé sur le comportement du partisan rationnel est développé pour le cas où les agents font face à de l’incertitude quant au moment du déclenchement des élections et quant aux résultats des prochaines élections. Le modèle prédit que les influences partisanes sur l’économie persistent tout au long du mandat du gouvernement, et qu’elles sont influencées, dans la direction opposée, par le type de parti au pouvoir au cours du mandat antérieur. La popularité du parti a un effet de causalité sur le cycle d’affaires. Enfin les effets des changements dans les anticipations du moment du déclenchement des élections dépendent de quel parti était au pouvoir au cours de la période antérieure. Les résultats empiriques de la calibration du modèle pour la production et le chômage au Canada,en Allemagne et au Royaume Uni donnent des résultats mitigés.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".