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Record W2269292283 · doi:10.34989/tr-92

The Performance and Robustness of Simple Monetary Policy Rules in Models of the Canadian Economy

2021· article· en· W2269292283 on OpenAlexaffabout
Denise Côté, John Kuszczak, Jean‐Paul Lam, Ying Liu, Pierre St‐Amant

Bibliographic record

VenueTechnical reports · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
Fundersnot available
KeywordsSimple (philosophy)Output gapTaylor ruleMonetary policyExchange rateRobustness (evolution)Inflation (cosmology)EconomicsSmoothingEconometricsInterest rateFunction (biology)Macroeconomic modelMathematical economicsComputer scienceMacroeconomicsCentral bank

Abstract

fetched live from OpenAlex

In this report, we evaluate several simple monetary policy rules in twelve private and public sector models of the Canadian economy. Our results indicate that none of the 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 interest-rate-smoothing rules and rules that have a high coefficient on the inflation gap, can substantially deviate from the optimal rule and can even be unstable in some models. Our results are thus very different from those of Levin, Wieland, and Williams (1999), who argue that simple policy rules are not only robust but also generate essentially the same policy frontier as more complicated rules or rules that respond to a large number of variables. Furthermore, we find that open-economy rules do not perform well in many models. In fact, we find that adding an exchange rate term to a simple policy rule often increases the loss-function value. This result is thus very different from that of Ball (1999), who argues in favour of a rule that includes the exchange rate. 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. But even in those models the loss-function value of this simple rule can substantially deviate from the optimal or base-case rule.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.941

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 head0.041
GPT teacher head0.223
Teacher spread0.182 · 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 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

Citations9
Published2021
Admission routes2
Has abstractyes

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