MétaCan
Menu
Back to cohort
Record W3183797068 · doi:10.3390/jrfm14070336

Empirical Evidence Regarding the Impact of Economic Growth and Inflation on Economic Sentiment and Household Consumption

2021· article· en· W3183797068 on OpenAlexvenueno aff
Larissa M. Batrancea

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsConsumption (sociology)Inflation (cosmology)Sample (material)Panel dataEstimationEconomic impact analysisEmpirical evidenceEconomic expansionMacroeconomicsGeneralized method of momentsMonetary economicsEconometricsMicroeconomics

Abstract

fetched live from OpenAlex

The dynamics of the interconnected global market and consumption behavior has recently changed considerably. Using a sample of 28 nations within the European Union, the study examined the degree to which economic growth and inflation impacted economic sentiment and household consumption during the time frame of December 2019 up to October 2020. The results estimated via panel generalized method of moments and panel least squares (with cross-section weights, time fixed effects) showed that economic sentiment and household consumption were significantly shaped by the proxies of economic growth and inflation. Moreover, in the case of economic sentiment, the negative impact of inflation was much stronger than the positive impact of economic growth. The reverse applied in the case of household consumption. The study draws policy implications regarding the strategies that public authorities, companies, and individual consumers could apply for stimulating national economies amid challenging times.

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)0.007
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.271
Teacher spread0.226 · 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

Citations48
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of risk and financial managementSame topicMarket Dynamics and VolatilityFrench-language works237,207