Economic Projections and Econometric Modelling: Recent Developments at the Bank of Canada
Bibliographic record
Abstract
This Technical Report describes some new techniques for making economic projections that have been suggested for use by the staff of the Bank of Canada. The procedures enable the staff to combine information from a newly developed econometric model of the Canadian economy with judgmental input from various sectoral specialists, an approach which recognizes the fact that an econometric model cannot fully reflect the variety of changing influences affecting the Canadian economy at any given time. The Bank of Canada's new model, RDXF, and its associated computer software have been jointly designed to facilitate the timely provision of a range of alternative projections conditional on explicit assumptions about policy and other exogenous variables. The main aspects of the model and the software are summarized in the earlier parts of this report. The structure and dynamics of RDXF will be analyzed in more detail in Bank of Canada Technical Reports 25 and 26 soon to be forthcoming. This report concludes with a description of the administrative procedures followed in the course of making economic projections and highlights the contributions made by the various sectoral specialists and the Bank's projection-coordination group.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".