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Record W1587488684

Output volatility in the OECD: Are the member states becoming less vulnerable to exogenous shocks?

2013· article· en· W1587488684 on OpenAlexaboutno aff
Jorge M. Andraz, Nélia Norte

Bibliographic record

VenueEconomic Issues Journal Articles · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsVolatility (finance)RecessionVulnerability (computing)Great recessionMonetary economicsContext (archaeology)Leverage (statistics)Great ModerationMacroeconomicsEconometricsGeographyLabour economics
DOInot available

Abstract

fetched live from OpenAlex

This paper analyses the vulnerability of OECD member states to external shocks by estimating the degree of asymmetric effects from positive and negative shocks. We use asymmetric conditional heteroscedasticity models with endogenously determined regime changes in a context of progressive moderation in both moments. The results suggest that recessions are associated with higher volatility and significant leverage effects. The estimated impacts of negative and positive shocks amount to 0.961 and 0.028 respectively. The disaggregated analysis over different periods reveals an increasing pattern of these asymmetries, as well as huge differences among the countries. The country-specific analysis suggest an increasing vulnerability to negative exogenous shocks in Australia, Denmark, Finland, Japan, Mexico, the Netherlands, Turkey and the United Kingdom, although with different levels, and decreasing vulnerability in Canada, Greece, Italy and New Zealand. Finally, some economies seem to have developed higher levels of immunity to external shocks by reaching balanced effects from positive and negative shocks. Among these are the largest European economies, together with the northern economies, the United States and the wealthiest economies of Luxembourg and Switzerland.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.289
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.007

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 head0.123
GPT teacher head0.268
Teacher spread0.145 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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