Have structural changes eliminated the out-of-sample ability of financial variables to forecast real activity after the mid-1980s? Evidence from the Canadian economy
Bibliographic record
Abstract
This article evaluates how consistently reliable the information content of individual financial variables is for Canada's future output growth. We estimate the timing of structural changes in linear growth models and check robustness to specification changes, multiple breaks, and business cycle asymmetry. Our simulated out-of-sample forecast evaluation strategy, using the Mean Square Error F-type (MSE-F) and the new encompassing (ENC-NEW) tests, shows that the leading information content of most financial variables for Canada's future Gross Domestic Product (GDP) growth has deteriorated substantially after 1984:04, but the 1–3-year term spread exhibits a consistently reliable predictive ability at the 1 and 2 quarter horizons and has significant forecasting ability at the 8 quarter horizon. Also, the real M1 money growth has regained its ability to forecast output growth since 1991:01.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".