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Record W2128484333 · doi:10.1093/oep/gpr049

Measuring coherence of output gaps with an application to the euro area

2011· article· en· W2128484333 on OpenAlexaff
Mark Mink, Jan Jacobs, Jakob de Haan

Bibliographic record

VenueOxford Economic Papers · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsCenter for Interuniversity Research and Analysis on Organizations
FundersU.S. Department of Commerce
KeywordsCoherence (philosophical gambling strategy)EconomicsComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

If output gaps in a currency union are not sufficiently coherent, the common monetary policy will not be optimal for all countries or regions in the union. It is common practice to measure coherence of output gaps by a correlation coefficient. We propose new measures of output gap coherence, which take differences in the signs and-or amplitudes of the output gaps more adequately into account than the correlation coefficient. We apply these measures to the euro area, using the US as a benchmark. We also examine how sensitive our findings are to different ways of calculating the output gaps. Copyright 2012 Oxford University Press 2011 All rights reserved, Oxford University Press.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.751

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.111
GPT teacher head0.209
Teacher spread0.098 · 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

Citations116
Published2011
Admission routes1
Has abstractyes

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