A time–frequency analysis of the Canadian macroeconomy and the yield curve
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We use wavelet analysis to study the relationship between the yield curve and macroeconomic indicators in Canada. We rely on the Nelson–Siegel approach to model the zero-coupon yield curve and use the Kalman filter to estimate its time-varying factors: the level, the slope and the curvature. Apart from establishing a bidirectional yield–macro relation, the paper broadens the existing literature by exploring the link between the monetary policy and the yield curve. We reached several conclusions. First, the monetary policy variable, the bank rate, affects mainly short-run interest rates. Arguably, the main driver for economic activity is the long-run interest rate (instead of the short run), suggesting that monetary policy is mostly ineffective. Second, we concluded that concerning the inflation rate, the Bank of Canada is very proactive. Third, regarding the unemployment rate, we found that both the slope and the curvature are leading indicators for the long-run evolution of unemployment. Finally, our results suggest that the industrial production index leads the yield curve factors and not the other way.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it