Evaluating Real GDP Growth Forecasts in the Bank of Canada Monetary Policy Report
Bibliographic record
Abstract
This paper examines the quality of projections of real GDP growth taken from the Bank of Canada Monetary Policy Report (MPR) since they were first published in 1997. Over the last decade, it has become common practice among the central banking community to discuss forecast performance publicly. The assessment we undertake is on annual forecasts as well as the average prediction over the policy horizon. We find that the MPR is more accurate than a naïve forecast model and marginally superior to a consensus of professional forecasters. The accuracy of the MPR annual predictions, measured by the root-mean-square prediction error (RMSPE), improves from 1.6 to 0.6 percentage points as more data become available. On a two-year average basis, the RMSPEs are about 1.0 percentage point for forecasts made in April and October. Our results also suggest that the bias present in MPR forecasts is often not statistically significant for both annual and two-year projections. Nonetheless, we found a tendency to overpredict growth at the beginning of the forecast cycle. Finally, at the beginning of the forecast cycle, the MPR correctly predicts the sign of the change in annual real GDP growth roughly 50 per cent of the time, improving to about 75 per cent at the end of the cycle. The sign of change is correctly predicted roughly 60 per cent of the time for the two-year average prediction.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".