Bibliographic record
Abstract
This paper applies the Qual VAR method developed recently by Dueker (2005) to search for the ideal macroeconomic indicator(s) that fully fit the in-sample movements of business cycle fluctuations and accurately predict the out-of-sample recession probability in Canada. Compared to previous works, I apply a more comprehensive dynamic forecasting model on the most updated Canadian macroeconomic dataset. The results are threefold: First, consistent with the findings by Estrella and Mishkin (1998), the term spread 1 between 10-year and 3-month marketable bonds on its own provides a reliable in-sample goodness of fit. Introducing additional leading financial variables, such as the bank rate and major stock market indices, does not entirely distort the forecasting performance of the term spread. Second, coupling the term spread with the US-Canada noon-spotted exchange rate gives us the best short-term out-of-sample forecasting power. Third, the combination of the term spread, exchange rate, and the growth rate of real GDP provides convincing recession prediction in longer horizons.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.784 | 0.673 |
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".