Making It Real: Bringing Research Models into Central Bank Projections
Bibliographic record
Abstract
This paper aims to bridge the gap between models in research and models used to support policy decisions in central banks. Models used in central bank projection environments overlap with research models and benefit from lessons learned in research, but they differ from research models in important ways. For example, to deal with real-world macroeconomic projection issues, central bank models may have a broader scope. To inform policy decision-making, models generally need both a theoretical basis and an ability to “fit” the data. For repeated projection exercises, forecasters need models that can be adapted to deal with data flows, including historical revisions. And, to provide valuable advice, forecasters must incorporate judgement into their projections to address issues outside the scope of the model. If all these challenges are met, then central bank models and projections will also inform the economic narrative that helps the public understand the policy decisions. In this context, this paper is organized around four main themes: 1) model requirements for central bank purposes; 2) overview of the Bank of Canada’s main policy models—ToTEM and LENS; 3) challenges in meeting those modelling requirements; and 4) practical approaches to addressing some challenges under time constraints. The paper concludes with a description of how lessons learned from research and practice set the stage for the Bank’s future modelling agenda, as discussed in Coletti (2023).
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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 | 0.032 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".