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Record W2566834249 · doi:10.18559/ebr.2016.1.4

Co-movements of NAFTA stock markets: Granger‑causality analysis

2016· article· en· W2566834249 on OpenAlexaboutno aff
Paweł Folfas

Bibliographic record

VenueEconomics and Business Review/˜The œPoznań University of Economics Review · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsGranger causalityIndex (typography)Bivariate analysisEconomicsStock (firearms)Stock market indexCausality (physics)EconometricsStock marketInternational economicsGeographyStatisticsMathematics

Abstract

fetched live from OpenAlex

: The paper scrutinizes the causal relationship between performance of American, Canadian and Mexican stock markets. It is aimed at answering the question as to whether there is a one way or two way causal link between the performance of stock markets (or possibly no causality at all) in the case of NAFTA members during 1992–1993 (pre-NAFTA period) and 1994–2013 (NAFTA in force). The study finds bivariate Granger causality for American and Canadian indexes in the periods: 1980–1988 and 1994–2013. Additionally the American index Granger-caused Mexican index during all the included periods, apart from 1992–1993, but the Canadian index did not Granger-cause the Mexican index at all. Moreover the Mexican index was a Granger-cause of the Canadian index in years 1994–2013 and a Granger-cause of the American index during period 1992–1993.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
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 head0.029
GPT teacher head0.219
Teacher spread0.190 · 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 designSimulation or modeling
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

Citations2
Published2016
Admission routes1
Has abstractyes

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