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The performance and robustness of simple monetary policy rules in models of the Canadian economy

2004· article· en· W2162906532 on OpenAlexaffvenueabout
Denise Côté, John Kuszczak, Jean‐Paul Lam, Ying Liu, Pierre St‐Amant

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
Fundersnot available
KeywordsTaylor ruleOutput gapMonetary policySimple (philosophy)Robustness (evolution)Inflation (cosmology)EconomicsSmoothingInterest rateEconometricsFunction (biology)Macroeconomic modelExchange rateSmall open economyComputer scienceMacroeconomicsCentral bank

Abstract

fetched live from OpenAlex

Abstract. In this paper, we evaluate seven simple monetary policy rules in a wide range of models of the Canadian economy. Our results indicate that none of the seven simple policy rules we examined is robust to model uncertainty, in that no single rule performs well in all models. In fact, our results show that the performance of some of the simple rules, particularly rules with interest rate smoothing and rules with a high coefficient on the inflation gap, can substantially deviate from that of the optimal rule and can even be unstable in some models. Furthermore, we find that “open‐economy” rules do not perform well in many models. We find that adding an exchange rate term to a simple policy rule often increases the value of the policy‐maker's loss function. Although it is not robust, we find that a simple nominal Taylor‐type rule that has a coefficient of 2 on the inflation gap and 0.5 on the output gap outperforms the other simple rules in a certain class of models. However, even in those models, the loss‐function value of this simple rule can be substantially higher than that of the optimal or base‐case rule. JEL classification: E52, E58

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 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 score0.426
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.119
GPT teacher head0.176
Teacher spread0.057 · 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

Citations32
Published2004
Admission routes3
Has abstractyes

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