Bibliographic record
Abstract
In this paper we gauge consumption and portfolio shares, rather than the traditional pricing implications. We study both aggregated (financial, tangible, and human) and disaggregated (deposits, stocks, insurance, and pensions) assets. The empirical shares are computed from recent aggregate Canadian data. The theoretical shares are constructed from a flexible specification of both investors’ preferences and investment opportunities. Our results reveal that the theoretical shares statistically match observed consumption and aggregated assets, but not disaggregated assets. Also, our findings for corporate stocks are consistent with the empirical asset returns literature. Finally, our findings for other assets highlight several new striking features. JEL classification: G11 Consommation et parts de porte feuille au Canada. Cette recherche analyse la consommation et les parts de portefeuille, plutôt que les prix des actifs. Nous étudions les actifs agrégés (i.e., financiers, tangibles et humains) ainsi que désagrégés (i.e., dépôts, actions et caisses de retraite et assurance). Les parts empiriques sont calculées pour des données agrégées canadiennes récentes. Les parts théoriques sont construites à partir d’un modèle flexible en ce qui a trait aux préférences et aux possibilités d’investissement. Nos résultats démontrent que les parts théoriques reproduisent la consommation et les parts agrégées, mais pas les parts désagrégées. Aussi, nos résultats sont cohérents avec ceux de la littérature empirique sur les rendements. Nos résultats concernant les autres actifs identifient d’autres aspects caractéristiques.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".