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Record W2891121336 · doi:10.1111/twec.12916

Unconventional economic policies and sentiment: An international assessment

2020· article· en· W2891121336 on OpenAlexaff
Marie‐Hélène Gagnon, Céline Gimet

Bibliographic record

VenueWorld Economy · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEconomicsSpillover effectTransparency (behavior)Consumer confidence indexZero lower boundMonetary policyFinancial crisisEconomic recoveryOrder (exchange)MacroeconomicsMonetary economicsInternational economicsFinance

Abstract

fetched live from OpenAlex

Abstract This paper estimates, using Bayesian and global VARs, the spillover effects of unconventional fiscal and monetary policies implemented in the United States and in the Eurozone during the last decade. Consumer confidence and investor sentiment indicators are introduced in the models in order to highlight the signalling channel in the responses to economic policy innovations in times of crisis. Our results reveal that consumer and investor perceptions of innovative economic measures are relevant to study the pass‐through of economic policies to the real sector in times of crisis and zero lower bound interest rates. In particular, the signalling channel plays an important role in successful unconventional economic policies. Moreover, if unconventional economic policies have an impact abroad, the effects are similar to those measured in the domestic country/region. Consequently, coordination and transparency are a prerequisite for ensuring short‐term growth after a global financial crisis.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.260
Teacher spread0.232 · 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 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

Citations7
Published2020
Admission routes1
Has abstractyes

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