Semi‐non‐parametric estimates of substitution for Canadian monetary assets
Bibliographic record
Abstract
We estimate the dynamic Fourier expenditure system to obtain consistent estimates of short‐run and long‐run Morishima elasticities of substitution for Canadian liquid assets. We argue that the variability of the estimated elasticities and evidence of less than perfect substitution between monetary assets interferes with the successful use of simple‐sum aggregates and traditional log‐linear money‐demand functions. Calibrations semi‐non‐paramétriques de la substitution pour des actifs monétaires canadiens. Les auteurs calibrent le système dynamique de dépenses à la Fourier pour obtenir des estimations cohérentes des élasticités de substitution à la Morishima à court et à long termes pour des actifs monétaires canadiens. Ils suggèrent que la variabilité des élasticités estimées et la constatation que la substitution n’est pas parfaite entre les actifs monétaires rendent difficile un usage heureux des agrégats de simple somme et des fonctions log‐linéaires de demande de monnaie.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".