Bibliographic record
Abstract
The forecast of world economic growth plays a key role in the conduct of Canadian monetary policy. In this context, the authors study the usefulness of the monthly Purchasing Managers’ Indexes (PMIs) in predicting short-term real GDP growth in the euro area, Japan, the United Kingdom, and China, as well as in the world economy. The main advantage of the PMIs lies in the timeliness of their releases compared to that of quarterly national accounts data and other related monthly indicators. The authors’ goal is to assess whether PMIs can help predict real GDP growth at the margin of other traditional monthly indicators (on top of the advantage related to their timeliness). To that end, the authors build simple indicator models and verify whether the addition of PMIs improves the in- and out-of-sample predictions. For all economies, PMIs turn out to be significant explanatory variables and to substantially improve the accuracy of predictions.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".