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Record W2620617635 · doi:10.11114/aef.v4i4.2342

Macroeconomic Switching Regimes and Monetary Policy in Canada

2017· article· en· W2620617635 on OpenAlexaffabout
Ronald H. Lange

Bibliographic record

VenueApplied Economics and Finance · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsLaurentian University
Fundersnot available
KeywordsEconomicsMonetary policyOutput gapInflation targetingMonetary economicsInflation (cosmology)SurpriseKeynesian economicsMacroeconomics

Abstract

fetched live from OpenAlex

This study examines the behaviour of monetary policy in Canada over the last 40 years using a Markov-switching VAR model of the macroeconomy. The Markov-switching estimates capture three continuous regimes that are interpreted as the ‘surprise’ regime from 1972Q1 to 1982Q2, the ‘recovery’ regime from 1982Q3 to 1991Q3 and the ‘target’ regime from 1991Q4 to 2014Q4. Monetary policy multipliers for the output gap are greater than one for all three regimes, suggesting that the central bank does not accommodate any expected changes in inflation over the long-run due to the domestic relationship between the output gap and future inflation. The long-run multipliers for inflation are equal to one in the surprise and recovery regimes, indicating that monetary policy also responds to offset inflation shocks. Overall, the policy multipliers and impulse response functions indicate a proactive central bank that responds systematically to movements in the output gap in order to control expected future inflation and to inflation surprises in the three regimes. The regime-dependent behaviour of monetary policy indicates a central bank pursuing an implicit form of inflation targeting as a means of achieving a nominal anchor for policy. The implicit inflation tar­gets are consistent with historical episodes of inflation in Canada over the past 40 years.

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 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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.021
GPT teacher head0.192
Teacher spread0.172 · 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 teacher head, not a consensus.

Study designObservational
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

Citations2
Published2017
Admission routes2
Has abstractyes

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