2007. The Canadian Business Cycle: A Comparison of Models. Bank of Canada Working Paper
Bibliographic record
Abstract
This paper examines the ability of univariate and multivariate linear and nonlinear models to replicate features of the real Canadian GDP data. We evaluate the models using various business-cycle metrics. We find that from the 9 data generating processes that we design, none can completely accommodate every business-cycle metrics under consideration. Richness and complexity does not warrant a close match with actual Canadian data. Our findings for Canada are consistent with Piger and Morley’s (2005) study of the United States data and confirms their contradictions with the results reported by Engel, Haugh, and Pagan (2005): nonlinear models do provide an im-provement in matching business-cycle features. Our findings provide support for the notion of forecast combinations: a diversified model portfolios help reduce uncertainty. JEL classification: C32, E37 Bank classification: Business fluctuations and cycles, econometric and statistical methods ∗Thanks to the participants of a Bank of Canada seminar. The views expressed herein and any remaining
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".