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Record W4402608566 · doi:10.1016/j.iref.2026.105490

Exchange Rate Stability and Monetary Policy in Canada: A Markov-Switching DSGE Approach

2024· preprint· en· W4402608566 on OpenAlexaboutno aff
Joonyoung Hur, Kyunghun Kim

Bibliographic record

VenueInternational Review of Economics & Finance · 2024
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsDynamic stochastic general equilibriumMonetary policyMarkov chainEconomicsExchange rateStability (learning theory)Financial stabilityMonetary economicsEconometricsKeynesian economicsComputer scienceMathematicsFinancial systemStatistics

Abstract

fetched live from OpenAlex

This study estimates the monetary policy rule in Canada using a Markov-switching dynamic stochastic general equilibrium (DSGE) model. The interest rate policy rule is estimated based on two blocks in which the underlying regimes are different: the response of the interest rate to the changes in the inflation rate and output gap (i.e., traditional Taylor rule block in a closed economy) and the response to the exchange rate change. Each block is independently estimated by dividing it into two regimes with strong and weak interest rate responses. According to the estimation results, the period estimated to be a regime that strongly (weakly) responds to the inflation rate and output gap is simultaneously a period of a regime that weakly (strongly) responds to the exchange rate change. As interest rates have different purposes in each block, a trade-off between the two goals is in line with the following trilemma: exchange rate stability and monetary autonomy for internal balance are not simultaneously achieved in an open capital market. Considering that the above trilemma is binding, our counterfactual experiments show that a monetary policy that does not responds strongly to exchange rate changes performs better in terms of welfare in general but this is not always the case for certain types of shocks.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.063
GPT teacher head0.251
Teacher spread0.188 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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