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Record W2911558992

Interest Rates, Inflation and Partial Fisher Effects under Nonlinearity: Evidence from Canada

2018· article· en· W2911558992 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEconomics bulletin · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsFisher hypothesisInflation (cosmology)EconometricsEconomicsNominal interest rateFisher equationMaturity (psychological)Real interest rateSeries (stratigraphy)Interest rateVariable (mathematics)Inflation rateInternational Fisher effectMathematicsMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.070
GPT teacher head0.227
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it