Measurement of the Output Gap: A Discussion of Recent Research at the Bank of Canada
Bibliographic record
Abstract
In this paper, we discuss some methodologies for estimating potential output and the output gap that have recently been studied at the Bank of Canada. The assumptions and econometric techniques used by the different methodologies are discussed in turn, and applications to Canadian data are presented. The use of the Hodrick-Prescott (HP) filter to measure the output gap has been justified on the basis that this filter extracts business-cycle frequencies from the data and that it can estimate an unobserved cyclical component. We note that the HP filter is unlikely to do well in achieving these objectives for series whose spectra have the typical Granger shape, such as real output, and that it will often fail to measure cyclical components adequately. The problems of the HP filter are accentuated at the end of samples, which is the place most relevant for policymakers. Finally, we note that univariate filters will only be able to give us information about the current output gap if the gap...
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.035 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".