Measuring Potential Output at the Bank of Canada: The Extended Multivariate Filter and the Integrated Framework
Bibliographic record
Abstract
Estimating potential output and the output gap - the difference between actual output and its potential - is important for the proper conduct of monetary policy. However, the measurement and interpretation of potential output, and hence the output gap, is fraught with uncertainty, since it is unobservable. It is therefore important that we continually expand and improve upon existing models, and innovate by testing new approaches and incorporating them into the analysis of potential output and the output gap. Within this context, this paper first provides an assessment of the extended multivariate filter (EMVF), which the Bank has used since the late 1990s to come up with a baseline measure of the output gap. It is determined that the EMVF has several limitations that need to be addressed. Consequently, a modified version of the EMVF incorporating revised conditioning information is presented. In addition, a newly developed methodology, the integrated framework (IF), provides a separate analysis of trend labour input and trend labour productivity, and in doing so accounts for more long-term structural changes in the economy. While neither of these approaches is perfect, and both have limitations, they represent improvements over the conventional method. The paper also outlines how the modified EMVF, the IF, and information from the Bank’s Business Outlook Survey and other sources are used to come up with an estimate of the current output gap and the future growth rate of potential output.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".