Canadian Financial Stress and\nMacroeconomic Condition
Bibliographic record
Abstract
L'auteur crée un nouvel indicateur composite de tensions systémiques du marché financier pour le Can ada. Comparativement aux mesures existantes, cet indicateur rend mieux compte de la correction du marché immobilier en 1990 et reflète avec plus d'exactitude l'absence de possibilités de diversification lors d'événements systémiques. L'indicateur proposé peut servir à des fins de contrôle. Durant la pandémie de maladie à coronavirus de 2019, par exemple, il a atteint un sommet que seule surpasse la crise financière mondiale de 2008. L'indicateur peut également servir à intégrer la dynamique macrofinancière non liné aire dans les modèles macroéconomiques empiriques de l'économie canadienne. Selon les constatations de l'auteur, les conditions macroéconomiques se détériorent sensiblement lorsque l'indicateur canadien de tensions financières excède le 90e percentile. Abstract: I construct a new composite measure of systemic financial market stress for Canada. Compared with ex isting measures, it better captures the 1990 housing market correction and more accurately reflects the absence of diversification opportunities during systemic events. The index can be used for monitoring. For instance, during the coronavirus disease 2019 pandemic, it reached a peak second only to the 2008 global financial crisis. The index can also be used to introduce non-linear macro-financial dynamics in empirical macroeconomic models of the Canadian economy. Macroeconomic conditions are shown to deteriorate significantly when the Canadian financial stress index is above its 90th percentile.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".