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Record W1599312186 · doi:10.34989/sdp-2007-1

The Zero Bound on Nominal Interest Rates: Implications for the Optimal Monetary Policy in Canada

2021· preprint· en· W1599312186 on OpenAlexaffabout
Claude Lavoie, Hope Pioro

Bibliographic record

VenueRePEc: Research Papers in Economics · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
Fundersnot available
KeywordsNominal interest rateZero lower boundInterest rateZero (linguistics)EconomicsMonetary policyVariety (cybernetics)EconometricsMathematical economicsUpper and lower boundsReal interest rateKeynesian economicsMathematicsMacroeconomicsStatistics

Abstract

fetched live from OpenAlex

The authors assess the performance of the Canadian economy under a variety of interest rate rules when the zero bound on nominal interest rates can bind. Their assessment is based on numerical simulations of a dynamic stochastic general-equilibrium model in a stochastic environment. Consistent with the literature, the authors find that the probability and consequences of the zero bound depend strongly on the targeted rate of inflation and that price-level targeting generally leads to better outcomes. Their results show that a non-linear rule is preferable to a linear rule under both inflation and price-level targeting, because of the zero-bound issue. This suggests that central banks should be pre-emptive and adopt an aggressive monetary policy when expected inflation falls below its desired level. The authors' results also show that the monetary authority must be much more forward looking under price-level targeting than under inflation targeting.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.122
GPT teacher head0.328
Teacher spread0.206 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueRePEc: Research Papers in Economics→Same topicMonetary Policy and Economic Impact→French-language works237,207→