Interest Rates, Inflation and Partial Fisher Effects under Nonlinearity: Evidence from Canada
Why this work is in the frame
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Bibliographic record
Abstract
Abstract: This study aims to reexamine and reconsider the Fisher effect for Canada from a different methodological perspective. To this aim, the nonlinear ARDL model, recently introduced by Shin et al. (2014), is applied for the first time for this country between 1991M1-2018M1. This model decomposes the changes in inflation rates from one series (variable) to two new series (variables) as increases and decreases derived from the original series of inflation. Hence, it enables us to reexamine the Fisher effect in terms of increases and decreases in inflation rates separately. The empirical findings of the nonlinear model reveal that increases and decreases in inflation rates have different (asymmetric) effects on nominal interest rates. When the maturity gets shorter (longer), decreases (increases) in inflation rates affect the nominal interest rates more. Additionally, this model with its decomposed variables enables us to describe and introduce a new version of partial Fisher effects in the long-run and short-run when reconsidering the partiality of the Fisher effect.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 it