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Record W2338290027 · doi:10.1515/revecp-2015-0018

An Evaluation of Selected Economic Areas according to Similarity of Supply and Demand Shocks

2015· article· en· W2338290027 on OpenAlexaboutno aff
Stanislav Kappel

Bibliographic record

VenueReview of Economic Perspectives · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsDemand shockSupply shockSupply and demandStructural vector autoregressionInternational economicsSimilarity (geometry)Vector autoregressionInternational tradeMonetary policyMonetary economicsMacroeconomics

Abstract

fetched live from OpenAlex

Abstract The Euro Area remains a well-known monetary union in the World. But the possibilities of creation of new monetary unions are discussed. It is spoken about NAFTA (Canada, Mexico and the United States) or MERCOSUR (Argentina, Brazil, Paraguay, Uruguay and Venezuela). The aim of this paper is to assess the similarity of demand and supply shocks in the countries of NAFTA and MERCOSUR, and to compare it with the countries of the Euro Area. For these aims, correlation and structural vector autoregression methods are used. Methods are based on Blanchard and Quah (1989) and Bayoumi and Eichengreen (1993). We confirm the existence of core states and periphery states in the Euro Area with some exceptions. If we compare supply and demand shocks, we find more similarity in the case of supply shocks in the countries of the Euro Area. According to the results, the countries of NAFTA are more appropriate for the creation of monetary union than the countries of MERCOSUR. The countries of NAFTA achieve high correlation coefficients of supply and demand shocks (except Mexico for supply shocks).

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.327
Teacher spread0.267 · 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 teacher head, 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

Citations0
Published2015
Admission routes1
Has abstractyes

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