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Record W1970482282 · doi:10.1002/ijfe.147

Irreducibility and structural cointegrating relations: an application to the G‐7 long‐term interest rates

2001· article· en· W1970482282 on OpenAlexaboutno aff
Marco Barassi, Guglielmo Maria Caporale, Stephen G. Hall

Bibliographic record

VenueInternational Journal of Finance & Economics · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsIrreducibilityRanking (information retrieval)EconomicsEconometricsTerm (time)CointegrationVariance (accounting)GermanSet (abstract data type)Relation (database)Identification (biology)Point (geometry)Mathematical economicsInterest rateMathematicsMacroeconomicsComputer sciencePure mathematics

Abstract

fetched live from OpenAlex

Abstract In this paper we examine the causal linkages between the G‐7 long‐term interest rates by using a new technique, which enables the researcher to analyse relations between a set of I (1) series without imposing any identification conditions based on economic theory. Specifically, we apply the so‐called Extended Davidson's Methodology (EDM), which is based on the innovative concept of an irreducible cointegrating (IC) vector, defined as a subset of a cointegrating relation that does not have any cointegrated subsets. Ranking the irreducible vectors according to the criterion of minimum variance allows us to distinguish between structural and solved relations. The empirical results provide support for the hypothesis that larger, more stable economies can achieve policy objectives more successfully by accommodating rather than driving other countries' policies. It appears that the driving force is Canada, which is linked to the USA, UK and France in three out of the four fundamental relations, and which is a reference point for the US, Italian and German rates, which are not cointegrated, seem to be determined by country‐specific factors. Copyright © 2001 John Wiley & Sons, Ltd.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.069
GPT teacher head0.293
Teacher spread0.224 · 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

Citations27
Published2001
Admission routes1
Has abstractyes

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