Comparing forecasting ability of parametric and non-parametric methods: an application with Canadian monthly interest rates
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Bibliographic record
Abstract
The primary objective of this article is to compare the forecasting ability of some recent parametric and non-parametric estimation methods by using monthly Canadian interest rate data between 1964:1–1999:1. The two-factor continuous time term structure model of Brennan and Schwartz was estimated where the first factor represents the short rate and the second factor the long rate using the continuous time estimation procedures developed by Bergstrom. The interest rates using the multivariate GARCH model developed by Engle and Kroner, and two non-parametric estimation methods namely, non-parametric kernel smoothing and the artificial neural networks was modelled. For the short-term rates, it has been found that, the Bergstrom's method and the artificial neural networks model have marginally better forecasting performance than that of the linear benchmark. For the long-term rates, none of the methods produced better forecasting precision than that of the benchmark.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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