Interest Rates, Inflation and Partial Fisher Effects under Nonlinearity: Evidence from Canada
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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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".