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Record W3046253984 · doi:10.1002/ijfe.1870

The role of financial stress in the economic activity: Fresh evidence from a Granger‐causality in quantiles analysis for the UK and Germany

2020· article· en· W3046253984 on OpenAlexaff
Andisheh Saliminezhad, Pejman Bahramian

Bibliographic record

VenueInternational Journal of Finance & Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsQueen's University
Fundersnot available
KeywordsQuantileCausality (physics)Granger causalityEconomicsEconometricsDistribution (mathematics)Conditional probability distributionQuantile regressionFinancial crisisMacroeconomicsMathematics

Abstract

fetched live from OpenAlex

Abstract This study examines the dynamic causal relationship between financial stress index and economic activity in the UK and Germany for the period from 2003 to 2018. Unlike the previous studies which ignore the consideration of all quantiles of distribution, we employ a Granger causality in quantiles that captures causal links in each quantile of distribution. Hence we are able to disseminate between the causality in the median and the tails of the conditional distribution. Our findings indicate that there is a significant, negative causal relationship running from financial stress to economic activity in both countries. However, the changes in the financial stress level in Germany start influencing the industrial production earlier (when economic activity is at lower levels) than in the UK. Our results highlight the emphasis on the consideration of the entire conditional distribution to avoid the risk of misleading inferences on the causality analysis.

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)0.004
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.072
GPT teacher head0.274
Teacher spread0.202 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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