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Record W2464419396 · doi:10.1111/iere.12372

EXTREME EVENTS AND OPTIMAL MONETARY POLICY

2018· article· en· W2464419396 on OpenAlexafffund
Jinill Kim, Francisco J. Ruge‐Murcia

Bibliographic record

VenueInternational Economic Review · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaKorea UniversityBank of Canada
KeywordsEconomicsMonetary policyInflation (cosmology)New Keynesian economicsMonetary economicsEconometricsInflation targetingExtreme value theoryKeynesian economicsMacroeconomicsMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract This article studies the implication of extreme shocks for monetary policy. The analysis is based on a small‐scale New Keynesian model with sticky prices and wages where shocks are drawn from asymmetric generalized extreme value distributions. A nonlinear perturbation solution of the model is estimated by the simulated method of moments. Under the Ramsey policy, the central bank responds nonlinearly and asymmetrically to shocks. The trade‐off between targeting a gross inflation rate above 1 as insurance against extreme shocks and targeting an average gross inflation at unity to avoid adjustment costs is unambiguously decided in favor of strict price stability.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0060.008

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 head0.099
GPT teacher head0.290
Teacher spread0.191 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations13
Published2018
Admission routes2
Has abstractyes

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