Bibliographic record
Abstract
Growth forecasts are the foundation of fiscal planning. Risk management in fiscal planning therefore requires an appreciation of the uncertainty associated with the underlying growth forecasts. This paper estimates such uncertainty by examining medium-term government and private-sector forecasts for Québec as well as private sector forecasts for Canada. It shows the distribution of forecast errors for both real and potential output forecasts by forecast horizon. It also examines a variety of decompositions to better understand key sources of forecast uncertainty. The results indicate that forecast uncertainty increases linearly with forecast horizon. Five-year ahead forecast errors for real output of ±5% are common, while those for potential output are roughly half the size, suggesting that the cumulative impact of cyclical factors play an important role. Of the two forecasts for Québec, the private sector forecast showed larger mean forecast errors while the government forecast had somewhat higher mean-squared forecast errors. The latter also tended to have offsetting mean errors in its forecasts of output gaps and trend productivity growth. Productivity growth together with labour force participation rates were a key contributor to most forecast errors while population growth tended to play a significant secondary role at longer horizons and variations in employments were generally minor.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".