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Record W4246903553 · doi:10.1017/s1365100517000621

MONETARY NEUTRALITY

2017· article· en· W4246903553 on OpenAlexaff
Apostolos Serletis, Zisimos Koustas

Bibliographic record

VenueMacroeconomic Dynamics · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBrock UniversityUniversity of Calgary
Fundersnot available
KeywordsEconomicsDivisia monetary aggregates indexMonetarismCointegrationNeutralityMonetary policyMonetary economicsInflation (cosmology)Keynesian economicsDivisia indexQuantity theory of moneyStability (learning theory)EconometricsQuantitative easingCentral bank

Abstract

fetched live from OpenAlex

We test the long-run neutrality of money proposition for the United States paying attention to the integration and cointegration properties of the variables. We use quarterly data (over the period from 1967:1 to 2014:1) and the new Center for Financial Stability Divisia monetary aggregates. We make a comparison among the narrower monetary aggregates, M1, M2M, MZM, M2, and ALL, and the broad monetary aggregates, M4+, M4-, and M3, and show that there is no statistically significant evidence against long-run monetary neutrality, consistent with both monetarist and Keynesian macroeconomic theory.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.054
GPT teacher head0.244
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2017
Admission routes1
Has abstractyes

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