Bibliographic record
Abstract
This paper gauges consumption and portfolio shares for aggregate assets (i.e. financial, tangible, and human assets) and disaggregated assets (i.e. deposits, stocks, and reserves for life insurance and pensions), rather than traditional underlying pricing implications for stocks. Hence, our analysis offers the advantage of providing both global and specific relevant information about the investor's positions in the most important financial and nonfinancial instruments. The empirical shares are computed from quarterly aggregate Canadian data for the post-1960 period. The theoretical shares are constructed from a flexible specification of both investor's preferences and investment opportunities. Our results reveal that the theoretical shares replicate remarkably well the empirical shares for consumption and aggregate assets, but not for disaggregated assets. Also, our findings obtained for stocks are consistent with the conventional empirical results of the stock return's literature. Finally, our findings for assets other than stocks highlight several new striking features. Cette étude mesure la consommation et les parts de portefeuille pour les actifs agrégés (i.e. financiers, réels et humains) et désgrégés (i.e. dépôts, fonds propres et régimes d'assurance-vie et de retraite), plutôt que les implications traditionnelles pour les prix des actions. Ainsi, notre analyse offre l'avantage d'une information globale et spécifique concernant les positions d'investissement. Nos résultats démontrent que les parts théoriques reproduisent remarquablement bien les parts empiriques pour la consommations et les actifs agrégés, mais pas en ce qui a trait aux actifs désagrégés. De plus, nos résultats obtenus pour les fonds propres sont compatibles avec les énigmes empiriques répertoriées dans la littérature sur les prix des actifs. Finalement, nos résultats pour les actifs autres que les actions démontrent plusieurs nouveaux éléments.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".