The Case of Serial Disappointment
Bibliographic record
Abstract
Similar to those of other forecasters, the Bank of Canada’s forecasts of global GDP growth have shown persistent negative errors over the past five years. This is in contrast to the pre-crisis period, when errors were consistently positive as global GDP surprised to the upside. All major regions have contributed to the forecast errors observed since 2011, although the United States has been the most persistent source of notable errors. In turn, the Bank of Canada’s gauge of foreign demand for Canadian exports—the foreign activity measure—has been continuously revised down. Average forecast errors for Canadian GDP growth are also negative over this period, particularly at the one-year-ahead horizon. The most important contributors to this unexpected weakness are exports and business fixed investment, the effects of which were only partly offset by positive surprises on housing. We find that the one-year-ahead export errors can be linked in part to the unanticipated weakness in US growth. Canadian competitiveness may also have been weaker than assumed. The errors on business investment correlate with measures of firm sentiment and uncertainty, as well as with deviations in oil prices from the view in the Bank’s baseline forecast. The possibility that a period of negative surprises in foreign and domestic output growth could continue over the coming years raises important questions for future study by central banks and policy-makers.
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 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 | 0.014 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".