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Record W2917167586 · doi:10.22059/ier.2019.72998

Core Inflation and Economic Growth, Does Nonlinearity Matters? A Nonlinear Granger Causality Analysis

2019· article· en· W2917167586 on OpenAlexaboutno aff
Amritkant Mishra, Amba Agarwal

Bibliographic record

VenueIranian economic review · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsNexus (standard)Inflation (cosmology)Causality (physics)EconomicsGranger causalityCore inflationCore (optical fiber)MacroeconomicsMonetary economicsPerspective (graphical)EconometricsEconomyMonetary policyInflation targetingEngineering

Abstract

fetched live from OpenAlex

This empirical analysis endeavors to trace out the causal nexus between core inflation and economic growth from the perspective of twenty worlds’ leading economy with the help of the nonlinear Granger causality approach by using time series data from 1981 to 2016. Based on nonlinear Granger causality results, it has been found that there is unidirectional casualty running from core inflation to economic growth in Belgium, Denmark, Franc, Greece, India, Norway, and Portugal as well as Sweden. In these countries, core inflation is causing economic growth. As per as Australia, Canada, Germany, Greece, Japan, New Zealand, Portugal, Sweden, Switzerland, and the United Kingdom is concerned the outcome of causality analysis documents that there is unidirectional causality running from economic growth towards the core inflation. Finally, the analysis also manifests that there is no causality running from core inflation towards the economic growth and vice-versa in Ireland, Israel, Netherlands, and Spain.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.009

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 head0.030
GPT teacher head0.245
Teacher spread0.215 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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