Bibliographic record
Abstract
Abstract. In this paper we utilize Canadian and U.S. data to investigate the relationship between financial market variables (Canadian and U.S.) and Canadian output growth, using a non‐parametric technique. The financial variables examined are those that are often associated with future output growth, namely, stock prices, interest rates, interest rate spreads and monetary aggregates. Our results show that as the number of autocovariances that are assigned a non‐zero weight increases, the feedback from selected Canadian or U.S. financial variables to future Canadian output growth increases. In particular, we find that stock prices as well as yield spreads and monetary aggregates are useful predictors of output growth. This is in line with earlier parametric studies in the literature that find these variables to be good predictors of economic activity. Variables financières et niveau d’activité réelle au Canada. Ce mémoire utilise des données canadiennes et américaines pour analyser la relation entre les variables des marchés financiers (au Canada et aux E.U.) et la croissance du produit canadien, à l’aide d’une technique non‐paramétrique. Les variables financières analysées sont celles qui sont généralement associées à la croissance du produit national dans l’avenir, i.e., le prix des actions, les taux d’intérêt, les écarts entre taux d’intérêts, et les agrégats monétaires. Les résultats montrent que, à proportion que le nombre des auto covariances auquel on assigne une valence zéro s’accroît, les effets d’échos variables financières sur la croissance future du produit canadien s’accroissent. En particulier, il appert que les prix des actions, les écarts entre taux d’intérêts, et les agrégats monétaires sont des prédicteurs utiles de la croissance du produit. Voilà qui confirme les résultats d’études paramétriques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".