MétaCan
Menu
Back to cohort
Record W2334610148 · doi:10.11114/aef.v3i2.1338

The Monetary Transmission Mechanism and Inflation Targeting: A Regime-Switching VAR Approach for Canada

2016· article· en· W2334610148 on OpenAlexaffabout
Ronald H. Lange

Bibliographic record

VenueApplied Economics and Finance · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsLaurentian University
Fundersnot available
KeywordsEconomicsMonetary policyCounterfactual thinkingInflation targetingMonetary transmission mechanismInflation (cosmology)Monetary economicsMarkov chainInterest rateVariance (accounting)EconometricsMechanism (biology)MacroeconomicsCredit channelPhysicsMathematicsStatistics

Abstract

fetched live from OpenAlex

This study employs a Markov-switching VAR with regime-dependent dynamics to assess the trans­mission mechanism of monetary policy in Canada. The regime-switching estimations divide the sam­ple period stochastically into two continuous regimes that corresponds to the periods before and after explicit-inflation targeting in Canada. The empirical results indicate relatively large differences in both the innovation process due to the variance component of the VAR and the propagation mech­anism due to the regime-dependent systematic component. The pre-targeting regime corresponds to much larger innovations in the macroeconomic variables and overall larger systematic responses of the macroeconomic variables to the innovation processes, suggesting a stronger monetary transmis­sion mechanism in the regime. However, variance decompositions and counterfactual analysis sug­gest that monetary policy has become more responsive to fluctuations in output growth and inflation in the target regime. Overall, about 80 per cent of the variations in the monetary policy rate in the current regime can be explained by the systematic reactions of policy to output growth and inflation at the relevant policy horizons.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.170
Teacher spread0.151 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueApplied Economics and FinanceSame topicMonetary Policy and Economic ImpactFrench-language works237,207